Content + AI

Colleen Jones Now in its third edition, Colleen Jones's book "The Content Advantage" has become a classic in the content-profession literature. The new edition of the book continues to highlight content intelligence and content effectiveness and adds a new focus on the impact and use of AI in content programs. It also takes a fresh look at the enduring concepts of digital disruption and digital transformation, both of which have been accelerated by the arrival of new AI technology. We talked about: her work at Content Science and how it informs the forthcoming third edition of her book, "The Content Advantage" her take on the concepts of "digital disruption" and digital transformation, both of which have been accelerated by the arrival of AI the title she'd give a movie about pace of organizational adoption of AI: "Slow and Slower" how elevating content concerns to the C-suite has garnered better results for companies lke the pharma giant Pfizer how AI can accelerate the implementation of content visions, strategies, and experiences how AI can improve content intelligence and aid in the assessment of content effectiveness how the structure, framework, and methodology for assessing content effectiveness remains the same in the age of AI her push to get organizations to use digital transformation as the lever to take an end-to-end view of their content how she consciously crafts the language she uses to talk about her consulting services - for example, using the term "end-to-end" instead of "omnichannel" a correlation that she's identified between operational maturity and AI implementation how AI might improve the process of improving content performance her optimism about the prospects for content professionals in the new AI-dominated tech world Colleen's bio A content expert and Star Wars fan, Colleen Jones is the founder of Content Science, an award-winning content firm where she has advised or trained hundreds of the world's leading organizations to become content Jedis. She has worked with many of the Fortune 50, the largest U.S. web properties, the largest nonprofits, and several U.S. government agencies. She also served as the fractional head of content at Mailchimp during its high-growth period before its $12 billion acquisition by Intuit. A member of Mensa, Colleen shares insights about content, AI, and business by writing for Entrepreneur, MediaPost, Forbes, and Content Science Review and by speaking at events around the world. She has earned recognition as a top content change agent by publications such as Technical Communication and a top voice for content strategy and artificial intelligence by LinkedIn. As a top instructor on LinkedIn Learning, Colleen's courses have reached hundreds of thousands of professionals. Connect with Colleen online LinkedIn Resources mentioned in this interview Content Science The Content Advantage, third edition November 2024 Video Here’s the video version of our conversation: https://youtu.be/lumGk_5EH6Q Podcast intro transcript This is the Content and AI podcast, episode number 39. The arrival of generative AI has upended many corners of the content world. As a long-time content consultant and researcher, Colleen Jones is very aware of this phenomenon. But Colleen is equally aware of the enduring value of intelligent, effective content, and the fact that all content efforts must ultimately engage and motivate actual human beings. When applied thoughtfully and strategically at an organizational level, AI can help achieve all of these goals. Interview transcript Larry: Hi, everyone. Welcome to episode number 39 of the Content + AI Podcast. I am really delighted today to welcome to the show Colleen Jones. Colleen is the president of Content Science, and also the author of the forthcoming book, The Content Advantage, in its third edition. It's been out for quite a while and the new edition has a lot of new additions about AI. Welcome, Colleen. Tell the folks a little bit more about what you're up to these days. Colleen: Thank you so much, Larry. It is great to be here, and fantastic to connect with you again. Content Science, we have been doing all kinds of interesting things in and around content. We do a lot of professional services as part of that. We do a lot of research and analysis, and we get the opportunity to do it for clients, but also independently, just delve into things that are of interest to us or that relate to trends that we're seeing. We've been continuing that over the past several years, and I'm really excited with the third edition of the book to bring some of those updated insights, facts, stats, all that kind of good stuff into our current, very interesting situation with AI and content. Larry: Yeah, and I think that very interesting in air quotes is appropriate. And one of the things, I can't remember, I read the second edition of your book maybe five years ago, so I can't remember if digital disruption figured as prominently then, but that's how you open the third edition of the book, is with this notion of digital disruption, which I think is really apt in the age of AI, but I think it's also just in general, it's related to digital transformation and a number of other phenomena that are going on. Can you talk a little bit just about what your concept of digital disruption, and how it applies especially to content practice? Colleen: Yeah, absolutely. You know what? I mentioned it in the second edition without really having any clue of just how much disruption would happen between the second edition and the third edition, so made it much more prominent in this third edition. And really, what that is about is the pace, the acceleration of change driven by technology. And right now, what's really driving that is artificial intelligence. At a macro level, big picture view, when disruption happens, that really drives the need for change. Business models might need to change just the way a current business model is executed, might need to change all kinds of implications. Colleen: That really is what digital transformation is trying to address. And I know a lot of people see that as jargon, but in the business world it is taken kind of seriously, a lot of big budget around it. And with my book, The Content Advantage, I am really trying to tie in content to business decisions. I thought it was important to mention both digital disruption and digital transformation, and really kind of make the case for how important content is to both of those concepts. Larry: It just occurred to me literally, as we were talking that in both the case of digital transformation and the adoption of AI, you get the sense that there's a lot of director and VP and management level people who are getting the charter from on high, "We have to do digital transformation, we got to do AI." And I think that's the level that most of us operate at. Is that a correct assumption on my part? Because you're way more in the management consulting side of this than I am, I think. Colleen: Yeah, I think that there's certainly that reactive stance of, "Hey, there's a lot going on here. We really need to take this seriously. Do we really need to get into implementing AI and so on?" AI, in some ways is a shiny object. It is getting a whole lot of attention. There's that kind of reactive stance, but then we're also seeing a little bit more of a strategic approach. Something that I think is interesting is that individual adoption of AI, what we've seen over the past couple of years, especially generative AI, that can be fast. Someone creates their own account and they can start generating content, refining their prompts and so on. But organization-wide AI adoption, it has been slow and it's getting slower. If I gave it a movie title, I think I'd call it, Slow and Slower. Colleen: And I think that's a good thing because there's a little bit of pause around all of the potential pitfalls that come with AI. I think there's more realization of just how much impact generative AI can have, how much it affects an organization because content supports just about every business function. So it's far reaching in terms of implications, and so it's an opportunity to get more strategic. I think the slowness isn't necessarily bad. It's an opportunity for organizations who are really looking at potentially implementing AI at a larger scale to think about doing that strategically. And it's a big opportunity for content leaders, professionals or allies of content leaders and professionals to be a big part of that conversation. That's what I'd really love to see more of. Larry: Yeah, I've talked to a couple of people on the podcast too, especially in the content design world, in that product content world where they're often like the perennial fight for the seat of the table on these product and teams. And when they demonstrate their AI chops, they often not only get a seat at the table, they have the C-suite calling them for advice about stuff. Are there any examples of that in your practice? I'm going to keep in mind throughout the conversation that you do all this kind of independent curiosity of your own research. Are you finding any places where content people have sort of an edge, like an edge in that AI gives them a competitive edge in terms of that seat at the table or influence in an organization? Colleen: Yeah, absolutely. We've worked with, over the past year, director-level content leaders and above who really are trying to update strategy and operations to factor in AI in the right way, which I think is super exciting and that's the right way to do it, doing everything from a series of AI readiness workshops to really kind of dig into, where are the opportunities, what are the gaps we have to be able to make the most of those opportunities? That type of thing.

Bill Rogers Bill Rogers is an experienced AI entrepreneur whose latest venture, ai12z, gives web content platform owners tools to build digital assistants and chatbots and to run gen-AI-powered searches. We talked about: his work at his latest startup, ai12z, which builds copilots designed to power content experiences his use of the term "copilot" as a generic AI capability, to distinguish it from branded uses of the word the two main capabilities of their copilot: question answering and ReAct (reasoning and action) his take on RAG architectures and how ReAct fits into them how integrating copilots into content and commerce architectures can guide users through complex interaction flows that are connected to third-party services how to ensure that users have confidence in AI systems and that the systems are technically secure the technical architecture that underlies their copilot platform how copilots help write queries to search utilities and other information and knowledge sources to help with tasks like complex product comparisons the variety of UIs their platform provides: search boxes, knowledge panels, etc. how interactions with copilots can inform an organization's content planning the importance of including image AI in this kind of platform, to both better understand the content and create more robust ALT text Bill's bio Bill Rogers is a visionary entrepreneur with a deep technologist background in AI and digital technologies. Recognized for significantly influencing the evolution of online experiences, Bill founded Ektron and served as its CEO. Under his leadership, Ektron emerged as a pioneering SaaS web content management platform, serving thousands of organizations globally. After Bill sold Ektron to Accel KKR, it merged with Episerver and became part of Optimizely. Bill then co-founded and led Orbita as its CEO, driving innovation in advanced conversational AI. Beyond these startups, Bill co-founded several other ventures and has had an expansive career in digital signal processing and robotics engineering. Bill holds a Bachelor of Science in Electrical Engineering from Boston University. Connect with Bill online ai12z bill at ai12z dot com Video Here’s the video version of our conversation: https://youtu.be/hJPnAvWXBlA Podcast intro transcript This is the Content and AI podcast, episode number 38. You wouldn't try to operate an airliner without a copilot, and you shouldn't operate a modern web architecture one function at a time either. That's the case that Bill Rogers makes for his latest AI startup, ai12z. His company builds AI copilots - in the generic, non-branded sense of that term - that enable robust search and discovery, streamline complex tasks like mulitfaceted product comparisons, improve accessibility, and even help with content planning. Interview transcript Larry: Hi, everyone. Welcome to episode number 38 of the Content and AI podcast. I am really delighted today to welcome to the show Bill Rogers. Bill is a longtime veteran in the content management and technology world. He founded a company called Ektron years ago, which was acquired by Episerver, which is now known as Optimizely. He ran a conversational AI platform long before ChatGPT came out called Orbita, and he's currently the CEO and founder at ai12z. So, welcome, Bill. Tell the folks a little bit more about what you're up to these days. Bill: Thank you, Larry. Yes. So, at ai12z, what we're doing is we're focused on building essentially a copilot, enabling websites and mobile applications, the ability to take advantage of AI to help drive experiences. Larry: Nice. And that's a nice, succinct description of what you do, but a lot of websites have chatbots or things like that. How does a copilot... Well actually, first let me back up because copilot is an interesting term. I first became aware of it when GitHub did their coding assistant thing, and then Microsoft has a whole suite of branded products called Copilots. But we're talking about a generic capability. Is that correct? Bill: That's correct. I think the term copilot, Microsoft has used quite a bit, but it is a generic term. We actually like to refer to it as a website AI assistant. And if you think about it, in the days of Ektron, we had this phrase, "What do you want your website to do?" And now we are talking about, "What do you want your AI to do for your website?" Larry: Interesting. Human needs haven't changed that much, but we have all these new capabilities. I guess what are one or two use cases that have jumped out early in your journey that are really helping people? Bill: So, when you think about, "What does copilot need to do?" So, one of the obvious things is this ability to be able to answer questions. And so when you talk about years back, when people were building chatbots, the challenge was creating the knowledge for that question and answering took a tremendous amount of work because you'd have to curate each piece of content that you're going to answer a question with. You had to create an intent model. Just an awful lot of work. Bill: Today, we have a CMS connector, we ingest the data and we can answer any question that your content actually have. You don't have to redo anything with your content in order to make it usable for question and answering. So, that's the first step, just question and answer. Bill: Then there's this concept of ReAct, which is reasoning and action. You enable these agents to do things. It can talk to backend systems like CRM systems or it could talk to any system that you have in your system. You just make a REST API available for it, and all of a sudden we can now use this data to create workflow to accomplish tasks that used to take an awful long time to go do and create, and it doesn't need to be that way at all anymore. Larry: Yeah, I know a lot of conversational designers and I've watched them work in Voiceflow and tools like that and hand crafting all those query... all the questions and answers basically, and the intent discernment stuff that they do. There's a lot to that. And so that ReAct, that sounds like a really intriguing... it's like you can get your fingers into any other system that you have. And this kind of reminds me of a... Is this in the family of a RAG architecture where you're... Bill: So, a RAG architecture would actually be just an agent to a ReAct system. So, let's just describe RAG. To the users, RAG is a way for you to, instead of using the knowledge of the LLM, you are using the content of your own content and you're answering... the LLM is answering questions based on that content. So, you have typically a vector database that when you ask the question, it gets the content and based on the content that it gets, the LLM will analyze that content and build a summary answer to it, actually very, very robust. And so that's a core piece to it. Bill: What ReAct does is that there's a large language model that does the reasoning. It thinks about what came in as a question and says, "Can I just answer that question or do I call one of my agents to help me answer the question?" And so, one of those agents can be ReAct... I mean, can be the RAG. Bill: So, why that becomes very exciting is that let's say that you want to compare two products. Your RAG has the information about each product in the system. The reasoning engine knows if you said... We'll use an example, sports example. If I said I wanted to compare the stats of Bobby Orr and Derek Sanderson, that's very tough for RAG because that one compare question, are you going to find content in your system that actually does do the comparison? And you're likely not. And so what will happen is that the reasoning engine says, "I'm going to go call the RAG for Bobby Orr, and then I'm going to call the RAG for what's the stats of Derek Sanderson." Bill: It gets the answer of those two information and then it combines the answers to do the comparison, and you get an amazing comparison around that concept. So then you take that step to the next level with a reasoning engine. And the reasoning engine, you tell them about all the tools that you have available to it: email, SMS, CRM, and the list goes on. Google Maps, Google Places. And you then say something to it like, let's say you're a hotel and you said, "What is the directions to the hotel from the airport?" Bill: And so the reasoning engine, from its system prompt, knows the address of where the hotel is and it knows where the nearest airport is, and it'll actually call an agent called Google Maps and it passes to that, the address of the airport, address of the hotel and IT generates the Google map with the full map and the link so that you can actually... so you see all the directions just like you would in Google Maps, but you can click on it and now it's in your mobile phone. Bill: So, you can see how a hotel can start looking at a reasoning engine as enabling all these third party services. Like if you said, "What are my activities?" Then the system is intelligent enough to say, "Oh, I have these eight activities, would you like to learn more?" And it gives you call to actions to learn more. And you then click on learn more and you see something about golf that you were interested in. It tells you about golf and you said, "Would you like to book a tee time?" You click book a tee time, a form has to come up to collect who are you and it collects your first and last name, your room number. And then it says, "Do you want to pick a date and a time?" So the time slots, when you pick a date, the slots are going to change. So now you pick all the information and then it might say, "Do you want to rent a club car?" Bill: And then it collects that data and it'll analyze it, send you an email, register it with the system of record that this booking has occurred.

Jeff Coyle Generative AI tools and LLMs bring the need for a new kind of content awareness in organizations of all sizes. While some have focused on content creation, Jeff Coyle has grown and accelerated his content-marketing capabilities by leveraging the content discovery and operations improvements that AI can deliver. We talked about: his decade-long history in working with NLP, AI, and content his overview of the rapid progression of AI technology over the past two years the importance to businesses and enterprises of doing a data inventory to understand their unique strengths the exponential increases in both the capabilities of the AI services he uses and their affordability the importance of creating high-quality content in this new AI landscape how to capture your org's knowledge and use it to fuel your content plans how journalists are crucial for capturing that knowledge his take on the current state of content-industry employment the importance of aligning content and its performance to organizational KPIs the crucial differences between how you wish people would consume your content versus how they are consuming it and how they might be the ongoing difficulties of marketing attribution and how new predictive models that AI affords can help address them how a "process inventory" is even more important than a conventional content inventory Jeff's bio Jeff Coyle is the Co-founder and Chief Strategy Officer for MarketMuse. Jeff is a data-driven search engine marketing executive with 20+ years of experience in the search industry. He is focused on helping content marketers, search engine marketers, agencies, and e-commerce managers build topical authority, improve content quality and turn semantic research into actionable insights. His company is the recipient of multiple Red Herring North America awards, multiple US Search Awards Finalist, Global Search Awards Finalist, Interactive Marketing Awards shortlist, and several user-driven awards on G2, including High Performer, Momentum Leader and Best Meets Requirements. Prior to starting MarketMuse in 2015, Jeff was a marketing consultant in Atlanta and led the Traffic, Search and Engagement team for seven years at TechTarget, a leader in B2B technology publishing and lead generation. He earned a Bachelors in Computer Science from Georgia Institute of Technology. Jeff frequently speaks at content marketing conferences including: ContentTECH, Marketing AI Conference, Content Marketing World, LavaCon, Content Marketing Conference and more. He has been featured on Search Engine Journal, Marketing AI Institute, State of Digital Publishing, SimilarWeb, Chartbeat, Content Science, Forbes and more. Connect with Jeff online LinkedIn MarketMuse Twitter jeff at marketmuse dot com Video Here’s the video version of our conversation: https://youtu.be/Ij18O07YnYc Podcast intro transcript This is the Content and AI podcast, episode number 37. The label "generative AI" has led many to focus on using this new tech for content creation, while the real benefits may lie in different capabilities that LLMs and other AI tools afford. In his work, Jeff Coyle has enthusiastically adopted AI, using it to identify new content repurposing opportunities, to capture and leverage unique organizational knowledge, and to dramatically reduce the costs of content operations, discovering along the way new opportunities for content professionals. Interview transcript Larry: Hi, everyone. Welcome to episode number 37 of the Content and AI podcast. I'm really delighted today to welcome to the show Jeff Coyle. Jeff is the co-founder and Chief Strategy Officer at MarketMuse. We talked on my other podcast, Content Strategy Insights, a couple of years ago, and I'm really excited to have him back because one or two things have changed since then. Welcome, Jeff. Tell the folks a little bit more about what you're up to these days. Jeff: Oh, thanks, Larry. And I am glad to be back. I am the co-founder and Chief Strategy Officer for MarketMuse, as you mentioned. I'm working on building artificial intelligence and content strategy offerings so that teams can make better decisions about what content they create or what content they update and then execute a lot faster. And so I'm sure we'll get into the details, but my background, I've been in the search space, building products, building search engines, building lead management systems, or selling them for 25 years. And I've been in SEO for about that long as well. There's probably nothing in the SEO space that you could ask me about that I haven't tackled or got knocked over by and got back up and then tackled. But yeah, I'm looking forward to this discussion. Larry: There's so much going on in that world. I really want to stay focused on the AI stuff that we might have to slip into SEO a little bit because that's an old practice of mine way back in the day. Jeff: Sure. Larry: But the first thing I wanted to do, you mentioned the details and do want to get into the details, but what I would love to get, because you're somebody who's been in this world for 20 years and you were talking about LLMs and Prompt Engineering six months before ChatGPT hit the scene. You're clearly embedded in this world. I would love to get your top-level overview of the commercial landscape around just data and data sourcing and the services around LLMs and GPTs and that whole world. Can you give us just a quick high-level overview? Jeff: Yeah. Like you said, and I've been doing natural language processing and the artificial intelligence components for now, gosh, about a decade. Thinking about ways that I can do it. I mean, I was trying to figure out how to use language technology to automatically classify documents into categories and into taxonomies, literally in a project 10 years ago. And then before that, thinking about search engine indexing and search engine strategies and building vertical search engines, building intranet search engines, and then the implications of how to use that to be really great at building content and being really great at SEO. Right now we're in a very unique, and the world is moving so fast that I think everyone really, really needs to focus on the new features and components that come with some of these language model releases. Jeff: We just saw from, and this dating this in the late summer in 2024, we saw from recent releases with Llama some of these things that have been closed and not accessible. Now you can see the way that things are working, right? The way that they're open, the waiting, things that you can tweak. You're able to learn from what's being released a lot more than you were in the past. And that's amazing just by itself. The advancing models that come out, even if you don't modify them yourself, they're progressing so fast that if you have a process in place that's using natural language processing technology or large language models, every time a new model's releasing, you're talking about savings of factors of 10 minimum. I mean, I have processes that every time something new comes out, I'm able to knock down 90% of the costs, right? When you're talking about the data side of it, there is massive, massive diamonds built into anyone that has any proprietary data source right now. Jeff: Inside your business, if you're a mid-market to small enterprise to enterprise, you should be doing a data inventory. What do we have that's special? What do we have that could be used for someone else's benefit based on how fast this market is moving, whether the use case, if you don't understand the use case, come find somebody like me. Come find somebody like Andrew Amen from 923 Studios, find somebody who is all about knowing how to make use cases with data and turning those things into potential gold mines for your business. If you have a database of customer data, if you have a database of real estate data, if you have a massive search engine index, you can use those things to do magic and you can do it on the cheap now. And it keeps getting cheaper and cheaper. And that's where I don't think people are catching up right now. Jeff: They're not catching up to how truly fast and how truly cheap it is to do things that would've cost millions of dollars. And I'm not being hyperbolic there. Millions of dollars only three or four years ago. And I'm a kid in a candy store with these things, right? I mean, I did a proof of concept that would've cost me about a half a million dollars just two or three years ago. And I shocked myself because the total cost of the entire project was a dollar. I mean it was literally a dollar. And I was like, I'm paying more for the coffee that I'm drinking right now than that cost. And I'm like, well, could we scale this? I'm like, hey, let's spend $70. And we did and I'm like, the magnitude of the things that we're doing for the cheap, it's truly staggering. And so I think everybody's really got to think what makes them special, what data do they have or what data do they know about? Maybe it's a partner, maybe it's a peer, maybe it's a data provider, and you can turn it into a partnership and say, hey, you have this thing. We could really do something special with it. That's the new economy with artificial intelligence and with content that nobody's talking about. Larry: Yeah. And as you say that, I'm thinking it's probably a rich multi-sided environment too. I'm just picturing, like you just said, if you have the data and people with the data have more opportunities, but people with ideas about what to do with that data, there's also the world of data products, but also just data as a supply for other people's stuff. It just seems like there's so much going on there. And you mentioned the use case.

Cennydd Bowles Like most designers who work in technology, Cennydd Bowles has reflected at times on the impact of his work and its ethical implications. After a couple of decades of information architecture and interaction design practice, Cennydd stepped back from his design work to explore philosophy and ethics in depth. His explorations have led him to extensive academic study as well as speaking gigs and writing on the subject, including a book, Future Ethics. We talked about: his transition from interaction design to tech ethics his origins in the information architecture world and his career, including a stint at Twitter how we as designers have missed predictable mistakes and patterns that ethicists have long known about how he got hooked on philosophy and ethics his 2018 book on the connections between the worlds of philosophy and design, Future Ethics the ethical issues that can arise in even a seemingly harmless practice like A/B testing his prediction that AI will in the not-too-distant future permit almost fully automated product development and the risks that that brings how the difficulties of measuring trust might exacerbate the trust issues that arise with AI the "magical" nature of AI his observation that "the problem with magic is it's intentionally deceptive" a new orchestrator role that he sees coming with AI his pessimism about the prospects for humans over the long term in the AI economy how Cory Doctorow's notion of "enshittification" manifests in the design and AI world what he sees coming: "rapidly iterating mediocrity rather than considered excellence" the power, albeit diminished recently, of employees to influence ethical decision-making within organizations three books he recommends (links below) his advice to designers to listen to and connect with philosophers and learn from their prior work on ethics Cennydd's bio Cennydd Bowles is a technology ethicist and interaction designer, author of Future Ethics, and a recent Fulbright Visiting Scholar at Elon University. Cennydd’s views on the ethics of emerging technology and design have been quoted by Forbes, WIRED, and The Wall Street Journal, and he has spoken on responsible innovation at Facebook, Stanford University, and Google. Connect with Cennydd online LinkedIn Cennydd.com Tech ethics books Future Ethics, Cennydd Bowles Design for Real Life, Eric Meyer and Sara Wachter-Boettcher Ethical Product Development, Pavani Reddy Ethics for People who Work in Tech, Marc Steen Video Here’s the video version of our conversation: https://youtu.be/MbfK7AnPa-0 Podcast intro transcript This is the Content and AI podcast, episode number 36. In the flurry of activity launched by AI-technology investment, ethical considerations have been left largely unexplored. Cennydd Bowles is an accomplished interaction designer who has spent the last several years studying and writing and speaking about tech ethics and responsible innovation. What he sees unfolding now concerns him, leading him to predict that the near-term future is more likely to bring "rapidly iterating mediocrity rather than considered excellence." Interview transcript Larry: Hi, everyone. Welcome to episode number 36 of the Content and AI Podcast. I am really delighted today to welcome to the show, Cennydd Bowles. Cennydd is a technology ethicist and interaction designer based in the UK. Welcome, Cennydd. Tell the folks a little bit more about what you're up to these days. Cennydd: Hey, Larry. Well, so let's see. I've just got back from America, so for the last six months, I've been in Elon University, North Carolina as a Fulbright visiting scholar. This is really a large part of my transition, essentially, from the days of UX and product design within industry, and transitioning from that into academia, and particularly philosophy, philosophy of technology, and ethics of technology. Cennydd: These days, I'm now essentially figuring out what's next. I'm finishing up a master's dissertation right now on the topic of the ethics of A/B testing, which I've got a lot of experience seeing inside companies, and think maybe I can offer something about looking at the ethics of it. After that, well, probably a lot more writing, probably a book or two. Then I think I'm probably heading down the academic path, so probably a PhD in some sort of philosophy, of technology, or computer science somewhere in that kind of space. Larry: Oh, great. I'll have to check back in. I'd love to see where... Getting into the details of this. You just mentioned, well, I guess I would love to talk just a little bit more about your transition, because you've been an interaction designer for a long time. I can't remember exactly how long, but we've talked about this and a little bit about your transition, but can you talk a little bit more about what motivated you to go from interaction design into ethics? Cennydd: Yeah, you bet. Yeah, so I started off as an IA back when that term was far more sort of current, I suppose. I read the Polar Bear Book, which some of your listeners may well know. This is Louis Rosenfeld and Peter Morville's book. I started, I guess, in about 2002, so it's been 20 plus years that I've been designing digital products. I don't like the idea that you can design the experiences, but interaction design, UX design, whatever you want to call it, for a range of companies, a lot of consulting, a bit of freelance. Cennydd: I also worked for Twitter for three years, where I was heading up the design team in London. It was after Twitter, actually, that I started to consider, well, maybe there's something that we're missing here as a community, and maybe there's something I can offer. It wasn't that I was sort of filled with horror and revulsion for what I'd seen inside Silicon Valley. It wasn't that I looked back on my career and said, "Wow, I've made a lot of mistakes." Cennydd: Of course, I have, and a few things I wish I could have ethically questioned at the time, but then that's common for all of us. I had an interest in the topic. Just even as a teenager, I was just interested in ethics as a concept, but I have no training in it. My undergrad was in physics, I had a masters in IT as well. I didn't really have any kind of philosophical or ethical background. When I left Twitter, I had sold, I got some shares, and I sold them, not huge amounts, it was a Silicon Valley thousandaire rather than millionaire, but I didn't have to rush into the next thing. Cennydd: I could afford to say, "Okay, what do I want to do? What's going to be the next right step for me?" I thought, well, I don't want to rush into a job immediately. I want to poke at this ethics thing. I think there's something here, and I don't understand it, and maybe there's something I can do to try and raise that, the profile of ethics within the design community and the technology community. I started reading. Cennydd: I got myself a reader's card for the British Library, and I sat there, and I tried to read philosophy. That's quite hard to do without any background in it. There's a reason why it's seen as a complex topic. It took me a while to find the right types of things, but eventually, I stumbled across some work that blew me away. I thought it was just fascinating, complex, and perceptive, some of the work that I was reading by philosophers and ethicists, and also writers, and artists, and critics. Cennydd: They'd been looking at the social impact of technology for decades. What occurred to me is that we just hadn't been listening. We'd been in this space, not really heeding their advice, not really listening to some of the warnings that they might've shared, and just convinced we were the smartest people in the room, and that we would figure it out for ourselves along the way. We're not the smartest people in the room, I'm afraid. You read some of this work, and you recognize a lot of the mistakes and the patterns that you see within the modern tech industry. Cennydd: It just put its hook in me, and eventually it got to the point where I said, "Actually, I think this is the direction I want to go. I don't think I want a regular kind of mainstream type design role anymore. I think I want to see what I can do to act as a translator, essentially, between the disciplines of design, and technology, and product, and the world of philosophy." That culminated in a book which I released in 2018, which is called Future Ethics. Cennydd: Then ever since then, I've been trying my best to make a living consulting on responsible design and technology, doing some academic work, talking, writing, speaking, all that kind of stuff, to try and influence the industry, frankly, to raise its standards, to consider ethics as more central to what it does. I think I've been partially successful in that. There's definitely been a change in how those discussions are happening since 2015, '16 when I started in that space. Cennydd: I'm not saying we're anywhere near winning that particular battle, but I think we're starting to see some slow change. I think that's going to be my continued role. Larry: Nice. We were talking before we went on the air that your current study, you're working on your master's dissertation, you said, and you're working specifically on A/B testing. I wonder, that seems like a really good, is that a good lens into tech ethics in general? Cennydd: I think it can be. I think one thing that makes it a good, almost sort of microcosm of how the tech industry thinks about ethics, or fails to think about ethics, is that A/B testing is very rarely questioned as something that's commonplace. Well, of course, we A/B test everything, and I've been in companies since 2007 when we A/B tested... Not really, there hasn't been a lot of focus on, "Well, should we A/B test,

Sharon Ni One of the most engaging aspects of generative AI products is their conversational interfaces. This has led many content designers working on AI products to develop skills in conversation design. Sharon Ni works on both conversational AI products and script-driven chatbots in her content design role at Cisco. She has developed her conversational design and technical AI skills by attending conferences, hackathons, and other events, by networking extensively, and by experimenting constantly with AI and conversation tech. We talked about: her work on chatbots and AI tools at Cisco an overview of the content design guidance chatbot she built her addition of "conversation designer" to her resume the evolution of the people ecosystem she works in, which now includes more engineers and data practitioners the professional development that she's done to prepare her for working with AI and collaborating with her more technical collaborators how participating in hackathons and other events has helped her advance her AI skills some of the tools she uses in her work, including spreadsheets, Miro, and Voiceflow her personal interest in building chatbots and how it's helped her in professional work the content design repository where she stores the conversational content she works with how she helps her colleagues understand how to best use AI her new responsibilities around assessing the technical feasibility of her advice to "just do it," to start building your own AI projects and connecting with others who share your interest Sharon's bio I love writing products. I hate writing about myself. So here’s five quick things about me and my work in AI: I’m a content designer at Cisco. Currently working on the Cisco AI assistant and Cisco.com chatbot. I like trying and building different chatbots myself - I recently built a content style guide chatbot that can help people review their copy and find guidelines. I’m a fierce advocate for content research and like to use data to inform my content design decisions. I have a background in Psycholinguistics and received a master’s degree from Middlebury College in 2023. Huge fan of this podcast. Connect with Sharon online LinkedIn Video Here’s the video version of our conversation: https://youtu.be/4HgM2hp5hpM Podcast intro transcript This is the Content and AI podcast, episode number 35. One of the main attractions of generative AI products is their conversational interfaces. This basic characteristic has drawn many content designers into the adjacent field of conversation design. In her work on chatbots and conversational AI products at Cisco, Sharon Ni has applied conversation design techniques and also learned a lot about the engineering side of AI, sometimes even advising her colleagues on the technical feasibility of their product ideas. Interview transcript Larry: Hi everyone. Welcome to episode number 35 of the Content + AI podcast. I am really delighted today to welcome to the show, Sharon Ni. Sharon is a content designer at Cisco, is doing really interesting stuff with AI and other technologies there. Welcome, Sharon. Tell the folks a little bit more about what you're up to these days. Sharon: Yeah, hi Larry. Very nice to meet you and excited to be here. And as you mentioned, I'm currently working on Cisco AI system for security, which is part of the Cisco AI ecosystem. And I'm also working on a chatbot that's on the cisco.com website right now. Sharon: And other than that, I am also working with the Voiceflow team to build an AI powered content design, style guide chatbot that can help our design partners to find the right guidelines and also review copy based on the guidelines, basically. It's not going to write the copy for them, but it will provide recommendations based on the good examples and bad examples that I fed into the chatbot and also the templates. So yeah, that's what I do. Larry: Well, it sounds like you're definitely earning your paycheck, at least three major things going on there. I would love to start with the content design guidance chatbot that you mentioned, because that's like... I think that'll be of interest just probably a lot of people are working on similar kinds of things. Can you talk a little bit just in general about... You mentioned that it's not so much doing, writing for people, but it's more like style and voice and tone and stuff. Anyhow, can you talk a little bit about how that chatbot works? Sharon: Yeah. So basically, I injected all of our guidelines into this chatbot. I kind of rewrite it because you can't just put the same... the guidelines into the chatbot. It's not going to recognize it very easily. Sharon: And so I work with the Voiceflow team. They help me to write the code part. And then right now I'm just adding more examples from our product, the copy and our product, and also some good examples and we also need some bad examples so that the AI will be able to recognize it and learn from it. And also the templates that you have to provide with the... what kind of response you want this chatbot to produce in a certain format. The reason why I wanted to create this was because we always get a lot of repetitive questions from... Sharon: During our office hours or in our help channels, people are asking about whether or not it's okay to capitalize certain words or sentences. And also they're asking about some words that's already... they're in our guidelines. So that's why we wanted to create this chatbot so that people don't have to look through our guidelines. They can just type in using natural language and to find the right thing that they're looking for. Yeah. Larry: Right. And you're building that with Voiceflow. And it's interesting, you still have the job title: content designer. But you're doing an awful lot of conversation design. Sharon: I know. I know. Larry: Working with Voiceflow and all that. Was that a new skill to you? Because you've been doing this, what, a year or so that you've been working on these chatbots? Sharon: Yeah, like a year. Larry: So you've kind of upskilled to become a conversation designer as well as a content designer? Sharon: Yeah, I think so. And I think I started calling myself conversation designer very recently, because I feel like all my projects right now are AI or chatbot related. But also, at the same time I feel like the conversation design work that I'm doing, just wanted to be clear that might be different from what other companies are doing or other content designers are doing. Sharon: But I think basically right now I'm doing a lot of the writing for AI and also the writing for chatbots. But also, at the same time I'm working with a lot of design team, marketing, and also sales team to just think of those strategies for AI. So it's more like a new experience to me, but I find it really interesting and I had a lot of fun with it. Larry: Yeah. And you're reminding me of... There is... It seems like generalists, or not so much generalists, but people with versatile skill sets are really going to thrive it seems like in this age of AI, because what you just described... And not just skills, but also the ability to collaborate with new and different people. Like the conventional content design roles, there's the product and engineering and design colleagues where you just mentioned that you're working... Well, this has to do with the nature of the products. You're doing the sales and marketing folks. Larry: But you've also mentioned, I know in your AI work you're working with machine learning engineers and data scientists and stuff. Can you talk a little bit about how the people ecosystem around you has changed over the past year? Sharon: Yeah, yeah, definitely. I would say in the past I've never really worked really closely with the engineers in the past. Just for our team, we mainly work and support our designers. We're more like a service. And also, because we have a super, super small team. We only have three content designers in our team. So a lot of times we're not the one who actually created the copy at the very beginning. We're more like a reviewing, we're helping them to review and also to edit their copy. And also we have our office hours and help channel to help them answer UX writing related questions. Sharon: And right now, I think I'm more embedded in those AI projects from the very start of the project. And I'm doing more than just writing. I know people are talking content designer, only 10 or 20% of their time are doing the writing work, but right now I feel like it's less than that. Sharon: We're actually doing more thinking than writing, which is really interesting to me. And I'm in this AI design team and we have our designers and we have our engineers, machine learning experts, and also AI experts and data scientists. Sharon: We work really, really closely together because what we're doing right now is we're all trying to figure out together. We don't really know what we're doing. But it's great to be able to understand and also to learn from other people and to learn what they do and also what they know. And I learned so much from especially the engineers about AI and especially the technicality side of AI and also the limitation of AI, what we can do with AI, what we can't do with AI. So I think this is super important. Larry: Yeah, that's one thing that has come up in a lot of my conversations, is the level of technical skill that is required to do this is a little more than a lot of conventional content design roles. Was that a challenge for you or did it come naturally or how did you get up to speed to work with these more technical collaborators? Sharon: Well, it wasn't easy, I would say,

Andrew Stein Like many content designers in the fall of 2022, Andrew Stein was concerned about the possible negative impact of generative AI on content and design practice. And his concern was heightened by the large number of content designers on his team. Since then, Andrew has discovered many ways to apply AI in his content design work, both in conventional digital-product design and in content work on AI products. He has also discovered a happy additional benefit of taking the lead on AI. His expertise has led to exciting new collaborations and leadership opportunities. We talked about: his work as a content design and AI leader his take on the best ways to use AI in content-design practice how to maintain focus on the fundamentals of content as you work with AI to create new content or manage and validate existing content, and a tool he is developing to automate this new content-employment opportunities that he sees emerging the clean slate on which content people can create their new AI roles and responsibilities some of his techniques for demonstrating how your content skills can help your AI collaborators: find opportunities to serve adopt a learner's mindset "just do" - experiment with tools on your own some of the people he follows and resources he has consulted as he has developed his AI expertise: Noz Urbina Leah Krauss the conversation design community, in particular Maaike Groenewege his encouragement for all content designers to find a balanced approach to incorporating AI into their career Andrew's bio Andrew is a Director and Principal Content Designer at a financial services company. He’s led content in smart home, social media, AI robotics, and FinTech. Andrew’s experience includes both consulting, and companies like Lowe’s, Wells Fargo, Truist Bank, and Meta. Andrew is currently focused on the way AI tools serve the content design process, and bringing a content-first approach to the development of new AI products and services. Connect with Andrew online LinkedIn ADPList Video Here’s the video version of our conversation: https://youtu.be/hxoMSzyDCFk Podcast intro transcript This is the Content and AI podcast, episode number 34. When generative AI burst onto the scene there were plenty of reasons for content designers to be anxious. Andrew Stein channeled his concern into a deep exploration of AI tech and how it might be applied in content work. As a design leader, he has discovered a number of ways that content designers can use AI tools, and build AI products. As an advocate for content practice, he has found that his AI expertise opens many new doors for influencing his business collaborators. Interview transcript Larry: Hi everyone, welcome to episode number 34 of the Content and AI Podcast. I am really happy today to welcome to the show, Andrew Stein. Andrew is an independent content design leader. He works currently for a big financial services firm. He also has his own consultancy on the side, does various content things including AI stuff for folks. So welcome, Andrew. Tell the folks a little bit more about what you're up to these days. Andrew: Yeah, very cool. Well Larry, super-happy to be here and as I mentioned earlier, I've seen all the episodes and get so much out of them every time, so really happy to be here. Yeah, right now, like you mentioned, I'm doing quite a bit of work both for the company I work for and on the side, working in both AI projects and traditional content design projects and really where those two merge together, both helping to build teams and build structure around how we approach AI from a content perspective, which I think is really key with all of this. And also how to bring AI into the work that we do as well as content designers working on traditional products and services as well. Larry: Yeah, I think that latter is probably the more familiar scenario for most of my listeners, I guess. I do know a number of people who are working on AI products, but I think the more common use case for many people is using AI in their day-to-day, just good old-fashioned content design work. Especially as a leader, how are you implementing that and encouraging your folks and just tell me a little bit about that. Andrew: Yeah, well I think at first, all of us were wondering does this do the writing for us? Does this replace us? There was quite a bit of fear and trepidation or looking at it very cynically like, "Oh, this thing can't do anything for me. It's not a writer, I'm the writer in the room." I think there's been a spectrum of views on it, but all looking at it as the writer. Is it going to be the writer? Can it replace the writer? No, it can't. And what I've really landed on, or at least at this point in time is that, no, it's not the writer, but it's a really great assistant to the writer. And so that's really the perspective that I'm coming at it from with the teams that I work on, with my own personal work is really seeing it as a really powerful tool. Andrew: Noz Urbina, I think he said, and maybe I've inflated the number, maybe it was 100 and now I've made it 1,000, but I believe he said, "Think of AI not as your superhero that's going to do everything and the magic bullet. But think of it as like 1,000 interns that can do way more than you can, but they can also do way more than you can really poorly with poor instructions or really great as long as you give them great instructions." And so that's really the area where I think AI fits in as a tool for content designers. It's definitely not replacing you, it's not going out ahead of you and doing all the work and then you're wondering where you fit into the picture. Andrew: It's very much human in the loop before, during, and after, and it's kind of like a companion or a sidekick that can help you do things, can serve as another person in the room or a lot of other people in the room to give you feedback on ideas. But very much from that perspective and not nearly as much as, "Oh, it can go out and do it for me. I don't really have to think about it." I think you have to think even more now to use AI well, but if you do that, it can be a really powerful tool and that's kind of the spot where I think we're coming- Larry: ...I don't know, I've managed as many as 15 people at a time, but that's only 15. And I've also done a lot of volunteer wrangling at work conferences and things like that. And that notion of managing enthusiastic and pretty knowledgeable, but really just not as far along in their professional development as you are, managing all that requires a lot of guardrails. How do you manage that? Everybody would love to have 1,000 precocious, brilliant people ready to help them, but they just don't know as much as you do about the job. How do you constrain that enthusiastic creative energy that LLMs bring to the game? Andrew: They're definitely way too enthusiastic most of the time, and I think if you try and just generate some content without a lot of structure to what you're trying to generate, you get that way over enthusiastic, too many words, too many repeated words, that young person that's learning a lot of cool words and wants to use them all. I think that's what we see a lot of times. It really is about, and I want this to be an encouragement to all content people out there, is that really the fundamentals of content creation are still there and if you bring those into the scenario, you'll have a much better experience with them. But it really is about diving into those core principles. Andrew: So when thinking about creating content or checking your content, being able to connect whatever AI tool you're using to really good sources and good existing content, so like a style guide or a content design system, having that built in and really fine-tuned so that anything you're doing is within those guardrails is really important. And obviously you can expand out from there. I think that's the base. So if you're using something like ChatGPT to help you ideate or to check your work, it's really about, okay, again, like you said, guardrails, what are the guardrails that keep that content generation or that content check or that brainstorming companion within the scope of whatever you're working on? So if you're in an organization, making sure that your LRC guidelines are built into that. That's another way to have checks and balances on the content you're creating. Andrew: Even tying into research and personas and having all these different pieces of data that can create this world that LLM or that ChatGPT tool can live within and work through is really important. But you can come at it from a few different angles. So you can think about it like that content creation and you're generating new ideas and new concepts or new content ideas and it's coming to you already within that framework, or you can take existing content and check it against those things as well. Does this match those guidelines? And that's where I think if you're using a tool, you want to build that tool in such a way that it's giving you the reasons that it's making decisions or the pieces of data that's factoring into what it's giving you. Andrew: I think that's been a really key thing for us, for the projects I've worked on is having that validation that, "Oh yeah, this made that decision considering this piece of information." Because not only does that give you a reassurance that yes, I'm within the guardrails. It also tells you where it's getting it wrong and where you can go update those guardrails to get better outputs. And then the more people that use it, you know that they're all working in the same frame ... It is just like your style guide. You want everybody to be referencing it. Well, it's the same thing now, but now you've got a tool that's also referencing it.

Anna Potapova Generative AI creates new opportunities to create and manage content at scale. And scale is definitely required when crafting content experiences for one of the world's largest ecommerce companies. Anna Potapova is incorporating gen-AI across the span of her work at AliExpress: content creation and management, localization, personalization, and other areas where her strategic-content mind guides her. We talked about: her recent promotion to a new leadership role at AliExpress which types of content are most amenable to being generated by AI the standards they use to guide the creation and ensure the quality of AI content the crucial role of content designers and localization experts in the ongoing iterative improvement of AI content at a large scale how AI enables the democratization of content creation the large percentage of user-generated content on the AliExpress platform how AI helps her team with personalization how gen-AI content helps them scale their marketing personalization efforts the importance of inviting yourself to machine learning and data science meetings to show the value you bring the value of case studies when communicating with internal stakeholders to show the value you can bring the importance of staying grounded in business objectives when developing relationships with your collaborators how a strategic approach to your work can help your org use AI most productively how the shift from hand-crafted content to AI content at scale manifests in content operations her plans to explore how AI can help evaluate content quality and conduct content audits the concept of hyper-localization, which addresses very specific regional and cultural differences the importance of proactively engaging with product and tech colleagues to ensure that standards-backed content powers AI products going forward Anna's bio Anna Potapova is Staff Content Strategist at AliExpress (part of Alibaba Global Digital Commerce group). She changed team positioning from pure localization to Content Design, built a style guide and a system to maintain it, established standards for AI generated content in multiple languages and improved business metrics while reducing production costs. Anna has been featured on several podcasts (Content Strategy Insights, Writers of Silicon Valley, Localization Leaders), joined UX Evenings @ Google and helped to build a content community in China. Connect with Anna online LinkedIn Video Here’s the video version of our conversation: lkjsdf Podcast intro transcript This is the Content and AI podcast, episode number 33. When you create, manage, personalize, and localize content at scale for a global ecommerce giant like Alibaba, you need all of the automation help that you can get. In her role as a content strategy and design leader at AliExpress, Anna Potapova is harnessing the power of generative AI tools and techniques to address customers' individual preferences, to help third-party vendors create better content, and to streamline their internal content design operation. Interview transcript Larry: Hi, everyone. Welcome to episode number 33 of the Content and AI podcast. I'm really delighted today to welcome to the show, Anna Potapova. Anna is a staff content strategist at Alibaba, the big e-commerce merchant in China. She works specifically for AliExpress. Welcome to the show, Anna. Tell the folks a little bit more about what you're doing these days. Anna: Thanks, Larry. Happy to be here again. Should I mention that since the last appearance on your podcast, I was on Content Strategy Insights with Arnaud. Since my last appearance I was promoted, I attributed exclusively to your podcast. Thank you so much for having me again. Larry: That's too awesome. Thank you. Anna: Recently, I've been talking a lot about AI and my team has been doing a lot of work in this area. Last week I actually spoke in front of front the audience of over 200 people in Chinese about how my team is harnessing the power of AI as we need to generate a lot of content every day. Larry: Nice. And you as one of the biggest commerce, maybe the biggest on the planet, I mean, there's a lot of content. Every one of those products needs something said about it. And every correspondence you have, there's so much going on there. And a lot of it I'm imagining is either routine or data-driven or in some way amenable to the use of AI. Can you talk just a little bit at a high level about how you're using AI? And also, I just want to note for folks that all of these conversations, we're going to dance around anything remotely proprietary. We're just going to talk in general about how big enterprises can work with big, vast content repositories and how AI can help. Can you talk a little bit about how you use AI to generate content? Anna: Well, first of all, it all comes down to what types of content can really be, dare I say, outsourced, can be created with AI because not every piece of content is created equal. Something very important for your product, maybe your core flaw, your gold path, all the UX copy over there. You really want it to be based on empathetic research, based on your users, based on very holistic view of this flow and challenges that people might potentially face. For things like that, I think it's very clear that you will still need that human touch. Anna: And recently, I think what I'm really excited about is that I see my company finding good niches, finding good places where AI can really benefit both business and users. We're diving into different content types and we're exploring new opportunities to see how we can create more personalized content, more engaging content where it's appropriate, where we know that it's not going to fail us and it's not going to harm the brand in any way. Anna: That ties back to quality centers. Of course, you cannot just have a large language model writing everything for you and having it all shipped without any quality control or any kind of involvement from professionals. Yeah. We've been building the system where we learn and try AI-generated content in different areas. And at the same time, we're building standards to make sure that it meets expectations to our customers. It doesn't contain any inaccurate or false information that it's surely aligns with our brand overall. Larry: Nice. And you've said that there's so much involved in it. And so it seems like developing standards is the prerequisite. You're like, "Okay, here's the threshold we have to meet in order to share this content." Can you talk a little bit about how... You mentioned some of the criteria that it's got to abide by the voice and tone of the organization, be accurate, all those things. Are there parts of that that AI can support enforcing your standards, I guess? Anna: Yeah, absolutely. But in that case, again, it requires more input from content designers and maybe even multilingual content designers as we work with many different countries and different cultures. It's important that you included your content professionals in the whole process of prompt design, scripts. And make sure that you really use the expertise of people to make sure that we can constantly build up, we can constantly build up, and we can improve from iteration to iteration. Larry: Yeah. And I can only imagine the scale of localization that you must do because you're a global company. You serve pretty much every country on the planet. Or do you constrain that in any way? Are you down to 50 or 100 languages? Anna: We serve over 200 countries and regions currently in 16 languages. Larry: Holy cow. And so that, again, that's one of those things like machine translation is probably one of the oldest forms of, if not AI, at least automation in content. But can AI help with that improving those machine translations and just making the localization person's job easier? Anna: Fantastic question. Fantastic question. I think what we're talking about here is localization in scale. When you have, for example, multiple merchants on a shopping platform, or maybe you have multiple hosts on your apartment sharing platform, you need to make sure that the content that those customers publish on the platform is actually attractive and actually interesting to people who are going to use the services or shop with those merchants. Anna: It's very important to empower people to create better content with AI, as long as you have this clear standard and you can make sure that your AI generates quality stuff. Patrick Stanford had this very good presentation recently on content design 3.0, talking about the impact of AI on the content design overall. And one of the principles that he brought up that really resonated with me is the democratization of content creation. That more people will have access to the tools to create content. And if we can guide them, if we can provide them with tools that generate better content that is better for their customers, better for their business, then it's really a win-win situation. That's also one of the areas where we've been working on in order to improve the information that comes from AliExpress sellers. Larry: That makes me wonder, what percentage of the business that you all do is third-party merchants who are doing that kind of thing? Like people who maybe don't have professional copywriters or just who content design is not their forte versus how much of it's AliExpress. It must be a huge amount of third-party sellers on your platform. Anna: Yes, yes, absolutely. I think on the daily basis, most of the content that people browse on our platform or any kind of platform I believe is coming from those merchants. Yeah. Most of the things that they see are not coming from my team. What my team creates is just a very small chunk, very small chunk,

Duane Forrester SEO has always been difficult, but generative AI takes things to an entirely new level. Duane Forrester has been immersed in the search world for more than 20 years, including stints as the Product Manager for the Bing Webmaster Program and Vice President of Industry Insights at Yext, where he developed company AI strategy. He also helped launch the schema.org structured-data standard. Duane offers plenty of AI-specific advice about how to navigate the new search landscape. But he also says that the foundations of good SEO are still grounded in timeless digital best practices: understanding your customers' needs and intentions and consistently giving them good content and helpful user experiences. We talked about: his long history as a search-industry expert and leader his high-level take on the current state of AI the true benefits of AI for content and how they relate to SEO the title of his content-and-genAI cookbook: "Common Sense" the importance of understanding the kinds of content that are resonating with your customers an interesting AI-driven SEO-localization case study that was presented at PubCon last year that demonstrates the power of understanding user intent an overview of the knowledge graph tech that underpins the search infrastructure at tech companies and big enterprises his predication that the future of search will be knowledge graph to knowledge graph conversations between companies and search engines the rapidly evolving new world of SEO and the imperative for businesses to leverage AI to keep up with the increasing need to scale SEO operations the enduring importance of providing a good user experience at the end of a search flow the importance of delivering content in video format into a search landscape increasingly driven by social media new search behaviors created by Google's Circle Search and AR tech like Meta's Ray-Ban glasses his observation that search is infinitely more complex than most SEOs can imagine the secret to search success: attracting attention from consumers, by deeply understanding their behaviors and intentions his prediction that Apple will launch an AI-powered Siri in September that will thrust ChatGPT into the mainstream Duane's bio Duane Forrester is a distinguished figure in the search industry, with a career that spans digital marketing, authorship, and leadership roles at prominent companies such as Microsoft Bing, Bruce Clay Inc. and Yext. His expertise in digital marketing is complemented by a strong understanding of AI/ML, consumer behavior and customer experience, making him a well-rounded and sought-after professional in the field. During his tenure at Microsoft, Duane was instrumental in the development and launch of Bing Webmaster Tools and Schema.org, focusing on the needs of webmasters and digital marketers. His deep knowledge of search engines and user behavior contributed to Bing's growth and success. Beyond his work at Microsoft and Bing, Duane has showcased his knowledge as a prolific author in the digital marketing sphere. He has written for most industry publications and his two books, "How to Make Money with Your Blog" and "Turn Clicks into Customers," have provided invaluable insights and guidance to numerous businesses navigating the competitive online landscape. Today, he, continues to share his extensive knowledge of digital marketing, AI, and customer experience, shaping the future of the search industry and empowering businesses to thrive in the digital era. Connect with Duane online LinkedIn Facebook Threads Twitter Duane's books How to Make Money with Your Blog: The Ultimate Reference Guide for Building, Optimizing, and Monetizing Your Blog Turn Clicks into Customers - How to deliver conversions across all online marketing activities Video Here’s the video version of our conversation: https://youtu.be/OjCH0b3isrs Podcast intro transcript This is the Content and AI podcast, episode number 32. For almost as long as people have been building websites, SEO practitioners have tried to get their content to the top of the search results. Search has always been a rapidly evolving field, but generative AI takes change to a whole new level. Duane Forrester has been immersed in the search world for more than 20 years. He offers this timeless advice for coping with the new AI and search landscape: understand your customer's intentions, and serve them with good content and helpful experiences. Interview transcript Larry: Hi everyone. Welcome to episode number 32 of the Content and AI podcast. I am really delighted today to welcome to the show Duane Forrester. If you work in anything adjacent to search, you know Duane. Years ago, he was the Bing Webmaster tools, I guess advocate or program manager. He helps launch Schema.org, that ontology that schema that you're all working towards when you try to promote your content online. He worked with Bruce Clay for a while, the search legend, and for the last seven or eight years he's been a VP of industry insights at Yext. So welcome Duane. Tell the folks a little bit more about what you're up to these days. Duane: Well, Larry, I think right now I'm hanging out with you, going to create content. I'm not an AI, this is really me. I am obviously continuing my career, moving in a new direction, excited about the opportunities that AI is bringing, looking at different areas, seeing areas that probably need investment by the leading companies, but also we don't know what they're actually working towards. So maybe they have a plan, maybe they don't. I don't know. I'm going to knock on some doors and see if I can open some eyes. Larry: Nice. So you're excited about AI and you are as well-informed as anybody. In search, I think if you're going to publish content online or share content online, you want it to be discovered and therefore a lot of people in the content world follow search. But tell me why you're so excited about AI. What do you see, especially for content practitioners, what are some of the opportunities you're seeing now? Duane: Okay, this is going to be a two-parter, Larry, because I don't think we can talk about this without touching on the topics that are negatively related to this. The idea of content theft and things like that, I think it's important to address those. I can't say that that's wrong or right. I will say that I understand where that perspective comes from. I believe that we have to be diligent, watch for it, manage it, that kind of idea. I do believe, however, that this technology... Look, you've seen this, everyone's seen this over the last say, two to three years. Every product and service has had the letters A and I appended to it. And it was a way to attract investment, to attract attention, to get PR and all of that. Whether the product or service actually did anything with AI was trivial. It didn't matter. Duane: It was just like putting the recycling symbol on something. "Oh look, we're earth conscious. Look at us". And it's like, well, okay. But practically speaking, I'm a big fan. I love the efficiencies that these systems... You and I were talking in our prep and I said, "Hey, I have an idea for a business". And so what did I do? I turned to ChatGPT and I said, "Hey, can you create an outline for what a business like this would look like in the state of California and what you have to do to start that business?" And then I made all kinds of noises like, wow, that's a lot of work and whatever else, because the amount of information that it gives you is extraordinary. It is in many ways, and I'm thinking ChatGPT as I talk through this stuff, but you could be thinking Claude, you could be thinking Mistral, you could be thinking Copilot, you could be thinking AI Overview from Google. Duane: All of these different things are capable of this in their own way. Perplexity and so on, Claude, all of these systems are capable of being that support person, that support mechanism that you wish you had. The person you can ask the dumb question of and they won't say, "Wow, what an idiot", they'll just go get you all the information and they tailor it to the level of the question. So the dumber your question, the more detailed the information, the more intelligent, more developed. And I mean, think about it, we're talking the concept of prompt engineering here. You give it more, it gets crisper, you give it less, it's a little fuzzier and it covers more ground. So I think there's a lot to that. When it comes to content, this is a force multiplier. This is muscle that you don't have. If you think this is a silver bullet, however, you are going to be sadly mistaken. Duane: And the answer to why am I not doing better in search is because your approach to using AI is flawed. And the bottom line is, it's great for ideation, it's great for rough drafts, but you still need a human with subject matter expertise to go through that content to make certain that content is accurate, factual, on point, has the right tone of voice. All of that matters. And that's huge. Duane: I think AI, the current systems that we have, the generative AI systems that we're all familiar with, I think they do a really good job of taking a lot of the heavy lifting out of a number of things. And if you can access tools that will look at large volumes of data, so they will take a look at your log files and they will pull out things that are related that you would never see, that can be very insightful and very useful. Duane: And if people are building tools that do those things for you, those can be very useful tools and a wise investment of a monthly subscription cost. I think we're at the very beginning of this, the very early stages of it, but I want everybody to think back to where we were, I don't know, I'll put a number of 25, 27 years ago.

Leah Krauss New AI products like Microsoft's Copilot can be powerful productivity enhancers, but if designers aren't careful they can inadvertently introduce into the product the bias and other hazards that can come with large language models. As a content designer working on Microsoft's Copilot for Sales product, Leah Krauss helps her colleagues understand and follow the responsible-AI principles that the company has developed. Leah's advocacy helps her design and product teams create a product that balances the need for transparency about the use of AI with the prerogative to keep customers in flow as they use the product. We talked about: her work as a content designer on Copilot for Sales at Microsoft and her advocacy there for responsible AI how she collaborates with her data science team, which had established a relationship with the content team even before Copilot on other products the evolution of their AI product-development process how their design system supports the implementation of responsible AI the six principles that guide responsible AI at Microsoft: fairness reliability and safety privacy and security inclusiveness transparency accountability how she advocates for responsible AI on the Copilot for Sales product team the balance between keeping customers in their flow and being transparent about AI features the concept of the "human in the loop" and how they apply it in the Copilot for Sales product the importance in AI product design of always being aware of edge cases and possible misuses of the product her encouragement to anyone working on AI products to stay curious, ask a lot of questions, and to bear in mind the importance and relevance of our language expertise Leah's bio Leah Krauss is a senior UX content designer at Microsoft. She works on Copilot for Sales, Microsoft's AI software for salespeople, where she also collaborates closely with the data science team. She champions responsible AI to anyone and everyone who'll listen, including inside Microsoft and at various UX conferences. Outside of work, you can usually find her reading, or spending time outside with her family - hiking, exploring cities, and hanging out on the beach. Connect with Leah online LinkedIn Video Here’s the video version of our conversation: https://youtu.be/VItdSUgzkZE Podcast intro transcript This is the Content and AI podcast, episode number 31. The introduction of AI tools like Microsoft's Copilot creates new opportunities for content designers. But as with any innovation, the new technology can be a two-edged sword. For every customer workflow that is streamlined there may also be an opportunity for bias or hazard to get into the product. As a content designer and champion for responsible AI, Leah Krauss helps her colleagues at Microsoft understand and apply responsible AI principles in their product design work. Interview transcript Larry: Hi everyone. Welcome to episode number 31 of the Content and AI podcast. I'm really delighted today to welcome to the show Leah Krauss. Leah is a senior UX content designer at Microsoft where she works on Copilot, which many of you may have heard of. So welcome Leah. Tell the folks a little bit more about what you're up to these days. Leah: Hi Larry. It's so nice to be here. So yeah, as you mentioned, I'm working on Copilot for Sales, which is a flavor of Microsoft Copilot, and that's been really exciting to be in on kind of the ground floor of AI at Microsoft. And responsible AI, which is what we're going to talk about today, is one of my most favorite topics to talk about. I've done some conference talks about it and my coworkers are really tired of hearing me go on about it. I actually serve, no, that's not true. It's only half true. I serve as actually a responsible AI champion, one of the responsible AI champions on my team. So it's sort of my thing and I think it's so exciting the moment we're at here and how big a part content designers can play in it. Larry: Yeah, I think when you're working with language models, you think the content people would have a leg up on some, but that's really, first thing I want to follow up on is that Copilot, I think it's sort of like multiple products then. It's a suite, well I guess Microsoft knows about suites with the Office suite, so there's integrations of copilot with each of the Office elements, Word and Excel and all that. And then there's also specific tools like Copilot for sales. How big is that little budding empire at Microsoft? Leah: Well, so there yeah, like there's one Copilot and then the different flavors depending on the user's needs. So as you said, if people are using Office, they'll see Copilot in Word and they'll see it in PowerPoint and Outlook of course. And if they have more specific needs like a salesperson, then they can use our more specific flavor, which has the kind of email summary that a seller might need. Did my customer mention the budget? Or things like that. And it can also pull from other sources so that the seller can have everything they need right in that one place. Copilot is a big thing at Microsoft, as you know, and I think it's only going to get bigger in the next couple of years. Larry: Yeah. As you're talking about it too, I'm reminded of how this thing is coming together. It's like there's this one, I assume it's based on one of the GPTs from OpenAI given a Microsoft relationship there. But it sounds like then that most of the applications, each of these flavors of Copilot, is a lot of your work around fine-tuning the model then for that specific task? Leah: Absolutely. I work really closely with our data science team and the first thing you sort of have to, there's the GPT prompt, but then we also write our own prompts on top of that. So we tell the GPT that this is a selling audience, for instance. And I'm really involved with working with the data science team to define what a good output looks like because while they're the experts in the model and how to create an output, I'm the expert in what makes it good and what makes it human and what makes it useful to a seller and scannable and valuable and things like that. So the great thing about this project has been that we get started early on working together. As we content designers know, sometimes content people are brought in too late. And that is definitely a danger here too, like for people who are listening, you can always do something with the data science team, but if you're there from the beginning and you're talking about the prompt together, then you can move forward and really have an impact. Larry: That's really interesting because there's all these new collaborations that are emerging along with these AI tools. So you're working, is it mostly data scientists? I've talked to other folks who've worked with the machine learning engineers and other new collaborators. What does the team you're working with look like these days? Leah: So at Microsoft it's called data science. At other places it may be called machine learning, but it's the same group of people. Larry: Yeah. Leah: There's the people who care about the algorithms, basically we can call them. And then we have the people who care about the words, which by the way is not only content designers, it's also product managers and interaction designers, but content is really leading the way. Larry: Yeah, that's fantastic to hear. And what you just said too is it sounds like there's more opportunities or is there more opportunity or have you just made it happen to get in earlier or do data scientists see the need to involve word people earlier on? Leah: Well, we've been working on AI features and machine learning features even before there was Copilot and even before I joined the team actually. So my manager who has been on the team longer than I have was working with the data scientists when we had a feature called Conversation Intelligence. And what that did was when you would record a meeting, a seller would record a meeting with their customer, then Conversation Intelligence would analyze afterward and give sort of a recap that has the main action items and a bulleted list of the highlights of the meeting. So my manager whose name is Erga Herzog, and she was the one who in the sales organization really built that relationship with the data scientists. And also I work with a lot of other content designers too. So basically I was lucky to come in about two years ago with this really strong base that was already very far along. Leah: We had a good relationship with the data science team and there was already that conversation was starting. So what we're doing now is we're sort of trying to formalize that process because sometimes it relies on the individual content design or the individual data scientist to decide when we start talking about a certain feature. And we'd rather have it be more formalized into a process that like, okay, at this point we start talking and then at this point we start looking at sample outputs and maybe we first decide together along with the PMs and the rest of the product squad and the leadership of course, what we want out of this particular AI feature. So that's something that we're working on right now, which is really exciting. Larry: That is exciting because this is also new and at some point you have to, like it must be, I can only imagine the pace of work there, but so being far enough into it to kind of step back and go like, oh, hey, this is how sort of the routine way that we, or not routine, but the common way that we do things. Is that sort of, do you think that's common across the other flavors of Copilot or, like because you mentioned you were talking to your peers as well as your immediate colleagues. Is there sort of patterns emerging around how those, like you just said,

Jack Molisani As the founder of the long-standing LavaCon conference and the principal at a technical content staffing agency, Jack Molisani gets a deeply informed view of the world of technical communication. While he sees the opportunities that generative AI presents, he raises several concerns for technical content strategy practitioners, among them the inaccuracy of generative AI content and the inability of AI tools to comprehend subtle human communication clues. We talked about: his work as the Executive Director of the LavaCon Content Strategy Conference and at ProSpring Staffing, a technical communication job agency how a change in the LinkedIn messaging interface inspired him to spend more time at in-person events his observation that many product features that are promoted as "AI" are actually capabilities that have been around for years his concerns about the ability to identify and vet the sources that AI tools cite his assessment of the job prospects for technical communicators in 2024 his exasperation with the decline in quality of applicant tracking systems (ATS) some of the tasks in technical communication that AI can help with the inability of AI tools to account for subtle human communication dynamics like facial expressions how using AI writing tools can misrepresent your own writing ability how a speed networking event that troubled introverts at a prior LavaCon led to the introduction of calming therapy animals at the event, including a therapy llama Jack's bio Jack Molisani is the President of ProSpring Staffing, an employment agency specializing in content professionals (both contract and perm). He's the author of Be The Captain of Your Career: A New Approach to Career Planning and Advancement, which hit #5 on Amazon's Career and Resume Best Seller list. The first printing is sold out. Watch for a soon-to-be-released second edition. Jack also produces The LavaCon Conference on Content Strategy, which contains an AI track. The 2024 conference is 27–30 October in Portland, Oregon. Register using referral code LSPODCAST for $200 off in-person tuition. Connect with Jack online LinkedIn LavaCon content strategy conference Prospring Staffing Video Here’s the video version of our conversation: https://youtu.be/RsgY89El1Aw Podcast intro transcript This is the Content and AI podcast, episode number 30. The rise of generative AI affects every type of content practice, including the venerable institution of technical communication. Jack Molisani runs both a tech comms staffing agency and the annual LavaCon content strategy conference, which he's organized for more than 20 years. Jack brings a deeply informed perspective to the conversation around the introduction of AI into content practice, especially its impact on employment prospects for technical communicators. Interview transcript Larry: Hi, everyone. Welcome to episode number 30 of the Content and AI Podcast. I'm really excited today to welcome to the show Jack Molisani. Jack is a legend in the textbook, communication, and technical content strategy world. He's the executive director of the LavaCon Content Strategy Conference. He also runs a staffing agency called ProSpring Staffing. Welcome, Jack. Tell the folks a little bit more about what you're up to these days. Jack: Wow, okay. As you said, I'm running around two spheres. One is producing the LavaCon Conference in content strategy. The other one is running a staffing agency for technical writers and other content professionals. Although we also have a division that does engineers, and there's some crossover there. What's interesting, and it's almost a side note but since you asked what I've been up to, is I've discovered that it's almost impossible for me to land new staffing clients over the internet anymore. Larry: Interesting. What's going on there? Jack: It used to be that someone would post a job on LinkedIn, and I'd wait two weeks. If it's still there I said, "Hey, could you use some help finding someone?" And they'll tell me yes or no. Jack: Well, a couple things happened. One is LinkedIn bifurcated your message inbox. It now has two labels, focused and other. It didn't announce this. Suddenly, all my responses were going to other and I thought I had an empty inbox. Where once I discovered this other tab, had people responding to me for two years saying, "Yes, we need help." Larry: Oh, God. Jack: By then, they don't need help anymore. Two, LinkedIn opened an API so people could use tools to email thousands of people at a time. Suddenly, mine and every other inbox is just filled with spam. Trying to weed all through that to find the real communication piece. And then, they added a third option on their reply screen, a pre-populated answer that says, "Thanks, we're not interested. Thanks, call me. Thanks, but not interested," and delete without responding. Larry: Oh. Jack: Now managers just go out and delete, delete, delete, delete, delete, delete. Not even saying, "No, I'm good," or, "Yes, please." Jack: I have discovered that I'm going old school and meeting people in-person. I've been going to trade shows. I just got back from a software engineering trade show yesterday. It's going to come back to that when we talk about AI in a second. And a manufacturing trade show two weeks ago. Two weeks from now, I'm going to a semiconductor trade show, just to go around to talk to people in-person. Going, "Hi, I'm Jack Molisani. Here's my card. If you don't need me now, maybe you'll need me in the future." I'm guessing people who have their own technical writing services or are independent contractors are like that. Jack: The other thing I've seen is now, on LinkedIn, where people post a job, it now tells you how many people have applied for that job. So within a half-hour, it says 100 people applied for this job already. You go, "Really?" A friend of mine said, "No. What really that means is 100 people clicked on the job to read it. They didn't necessarily apply for it." Larry: Interesting. Jack: I just don't trust anything I read anymore. Larry: Yeah. Jack: We've come to that point. They said it was coming, it's here. Larry: The reason I wanted to have you on this podcast specifically is because this is the new one, about AI. When we first talked, we were talking about your journey into AI. But I'm going to just jump way ahead. I think my prediction is that one of the outcomes of this is going to be a return to human connection. Here you are, exhibiting it already, going out to conferences. Thanks for validating my prediction. Larry: I'm assuming you can't ignore AI in your line of work. Both just the technical communication part of it, the programming for LavaCon. I'm assuming it's invaded your life like everyone else. Is that a safe assumption? Jack: We do have a track. Last year's LavaCon, when everyone was talking about LavaCon, it was the main theme of the conference. I observed a trend, if I may. What was it? Four years ago, everyone going, "Chatbots! Chatbots. The future of tech comm is chatbots." Next year, crickets. Year after that, "Oh my God, VR, the Metaverse. Everyone's going to be in the Metaverse." Next year, crickets. Now we're going, "AI! AI! AI!" I'm going, "Hm." Jack: I don't think we're going to get quite to cricket level on AI, but I already know that, in my conference, that it's not the main focus this year. We have an AI track, yeah. Sure. Because you got to know what's coming, what's available, what you can and can't do with it. But we're going back to the basics, treating content as a business asset that you can use to reduce costs or generate revenue. Back to basics. Larry: Yeah. That came up. I just dropped an episode of the other podcast, Content Strategy Insights yesterday, with a woman at Albert Heijn, the big grocery chain here in the Netherlands. One of her big accomplishments there was getting the enterprise to view content as an asset. I said, "Wow. How did you do that?" I love that that's a focus of yours as well. Larry: But, tell me. You're obviously not a rah-rah person. Tell me how you see AI fitting into tech comms and tech content strategy. What do you think? There are some things that are proving to be useful to people, but I gather that you perceive a lot of hype as well. Jack: Yes. More of the latter, less of the former. Or it's just not quite here yet. A couple stories on this. I can see a perfect application of AI in tech comm. Take a company like Boeing, who has 10 million pages of documentation in their content management system. Scan that whole dataset, find out how many of those pages are sufficiently similar that we can combine them, and reuse it, and maintain only one source. Brilliant use of AI. Jack: Or take your legacy documentation. If it's structured using headings, break each heading into a separate topic. Automatically add, populate the meta tags. I'm assuming your audience knows what meta tags are. Then repost that as chunked, individual content pieces. Brilliant use, I can see that. Jack: What I'm seeing now, however, is every single tool vendor in every single industry or trade show I've gone to is like, "Our tool is AI enabled." One of them was a content management system. I was talking with one of their people. I said, "Hey, that's great. Show me something in your tool that's AI." She goes, "If you create a new topic, we will pre-populate the XML for you." I said, "Hmm. First of all, that's called a wizard and we've had them for decades. What about your tool is artificial intelligence?" She couldn't tell me. She said, "Oh, let me get back to the developer." True story. Absolutely true story. Larry: Interesting. Jack: I think what's happening is a lot of these tools, they want to be seen as up-to-date and, "We're just as AI as they are.

Lisa Welchman Over the past 25 years, Lisa Welchman has established and codified the field of digital governance. With an enterprise consulting career that spans the emergence of the web, the arrival of social media, and the rise of mobile computing, she is uniquely positioned to help digital practitioners, managers, and executives understand and manage the governance issues that arise with the arrival of generative AI. Lisa is the author of the leading book in her field, Managing Chaos: Digital Governance by Design. We talked about: her career in enterprise digital governance her concern about the lack of transparency in the existing governance practices at AI companies an analogy she sees between WYSIWYG and AI tools the contrast between more mature governance models like the UX field has developed and newer digital practices like the adoption of GPTs governance lessons that new tech implementers can always learn from prior tech eras her call to action for technical experts to alert executives of possible harms in the adoption of new technology the elements of her digital governance framework: understanding team composition and the organizational landscape in which digital practitioners operate having a strategic intent articulating governance policies establishing practice standards the range of digital makers she gets to interact with in her work the importance of accounting for the total business and organizational environment when jockeying for a seat at the table the responsibility of experienced digital makers and managers to call out potentially troublesome patterns in the adoption of new tech the importance for digital practitioners of staying aware of how much agency they have right now Lisa's bio Lisa Welchman is a digital governance trailblazer with over two decades of experience. She's passionate about helping organizations manage their digital presence effectively and sustainably. Known for her practical approach, Lisa has worked with a variety of clients, from global corporations to non-profits. She’s also a popular speaker and the author of "Managing Chaos: Digital Governance by Design." A mentor and educator at heart, Lisa is dedicated to helping leaders make the digital world a safer and kinder place for everyone. Connect with Lisa online LinkedIn Video Here’s the video version of our conversation: https://youtu.be/-UIj0YWxLaI Podcast intro transcript This is the Content and AI podcast, episode number 29. Whenever new technology like generative AI emerges, organizations have to deal with both the opportunities and the challenges that arrive with it. It often falls to practitioners like content strategists and designers to alert the C-suite of potential governance concerns that arise with the adoption of new tech. Lisa Welchman sees in this situation an opportunity for digital makers to take the lead on educating their organizations about these important issues. Interview transcript Larry: Hi everyone. Welcome to episode number 29 of the Content + AI Podcast. I am really happy today to welcome to the show Lisa Welchman. Lisa is a true legend in the field of digital governance. She pretty much established the discipline, I think it's safe to say, over the past 25 years. She wrote what I would argue is the leading book on it, Managing Chaos: Digital Governance by Design. But welcome Lisa, and the reason I wanted to talk to you this week is we're right in the middle of Rosenfeld Media is doing a conference on design and AI, and it seems like AI is an area that's really ripe for a conversation about governance. Does that make sense? Lisa: Yeah, it does. I will contextualize myself a little bit in saying that digital governance is a really broad term, and my focus is really around enterprise digital governance, how digital governance manifests inside of an organization that's making and putting things online. And there's a lot of other governances around there in the internet web space that are equally interesting, but not where I specialize. Larry: That idea of enterprise. And what's interesting about that is that the big companies that are doing this stuff, that are most prominent in the field, it's all Google and Anthropic and Microsoft and OpenAI and huge organizations like that. Do you have any feel for what governance is happening inside those orgs? Lisa: I don't actually have any kind of feel. I think the types of organizations that you describe have in some capacity mature governance inside of the organization because of the nature of the types of products and services that they offer online. And just from evidence. Now, whether or not we like the decisions that are being made within that governing framework that they have, that's an entirely different concern. I am concerned about those larger organizations married with the newness of this version of AI, that's like the iceberg, the AI iceberg is finally poking its head out of the water and we're paying attention to it now. And there's a lot of stuff underground that these organizations have been doing for years that we're not really aware of. I'm a little nervous about the lack of transparency around the preamble governance that may have happened, concerned about that. Lisa: But I'm not concerned that they aren't governing for many organizations, enterprise organizations, B2Bs who are coming into this technology afresh just as it's emerging to them I'm more concerned because they're more likely to take ChatGPT, and I know it's not a great analogy, but ChatGPT feels to me like a WYSIWYG AI tool. You don't really need to know what you're doing. It's like those of us back in the day who learned HTML, we actually had to learn HTML to make things work. And then you got these, what you see is what you get tools, these WYSIWYG tools come out of the framework and anybody could code a page and it made really sloppy, nasty code on the backend, but it didn't matter because the browser served it up. Lisa: And I see some of these new tools, particularly around generative AI as like WYSIWYG tools for AI. And it makes me nervous because not a lot of people are asking "What's in that black box and what's happening and who made the decisions about it?" Which is really what governance is about, "Who was considered, what are the policies, what's the value system around making this technology?" And I don't see a lot of people asking that in the enterprise. Larry: I think a couple of things about what we just said. One, the notion that these things are black boxes, that the LLMs, and in fact, even the engineers who build them often say they can't explain what's going on underneath them. But you contrast that with, I spend a lot of time with conversation designers and other UX designers, and in that world, it's so clear that transparency and explainability are crucial to consumer acceptance and adoptance and safety. It seems like reconciling that should be on the governance agenda someplace. Is that reconciliation of intent with customer expectations, is that something that governance can help with? Lisa: It is, but I would also argue that you're comparing apples to oranges because one of the things that I like to talk about a lot that a lot of people talk about are maturity models. And the maturity model for a new technology or a new anything is that it comes out of the chute hot and heavy. People don't really know what they're doing with it. They try new things. There's a lot of craziness on organic growth. We make a big mess, a lot of harm and lack of safety come into play and somebody screams and says, "We need to govern this," or, "We need to write policy around this." Or if you're more on the operational side, "We need to write standards around this. We need to become more transparent. We need all of these things happen." And then there's some struggle and then things mature, and then you have a more sustainable model. Lisa: You're comparing a UX model that's fairly mature with a coming-out-of-the gate one. And it's not entirely fair because UX has not always been that way and experience development and the development of an online experience has been quite chaotic and a lot of harm that we see has been a result of UX not thinking through problems early on or implementing things or not understanding the foundational functionality of what they're asking for, not understanding that certain types of online interactions will create certain data pools that can be exploited by the organization. That all happened in the UX world. It didn't come out clean. Larry: I want to follow up. There are two things about that. One you alluded a minute ago to the AI tip of the iceberg. The AI has been around forever, since the seventies and eighties, and it's just now the arrival of the GPTs and in particular ChatGPT 3.5 almost a year and a half ago now. There's that, but that's where people perceive the start of this to be, and that's where it does lag far behind UX practice, but in fact, it's been around for a while. Is this a common pattern, I guess to see the technology? Lisa: Yeah, it's just how it flows. This is just how things work. This is a presentation that I give about the history of automotive, automobile safety and things come out of the gate very hard. Usually in the US, other parts of the world, people are trying to make money or trying to figure out how to exploit this new technology that's become mature enough that it can actually be used to make money and to build product. We all know there's a huge preamble to every technology where people fail and fail hard and fail sometimes for 50 to a hundred years or more. They're failing, failing, failing. Finally, somebody comes up with something that's actually viable and it comes into the marketplace and then people think, "It's new." And of course it's not new,

Rob Hoeijmakers LLM-based conversational tools are revolutionizing all parts of the content ecosystem, including blogs by independent professionals. Rob Hoeijmakers is an independent web strategist based in Amsterdam. He's using AI tools like Whisper and Perplexity to streamline and improve his research and writing workflows. This lets him spend more time on his websites' information architecture and improves the business results he gets from his blog. We talked about: his work as a web strategist and his multiple blogs his happiness with being able to delegate tasks to his LLM colleagues the freedom that AI tools like Whisper give him to research, think, and ideate as he walks how the abundance of content that AI tools provide helped him abandon his old scarcity mindset around information the huge time savings he realizes from using AI-generated summaries of transcripts of interviews how he uses AI tools to draft his blog content his insight that the real value in his blog is in its information architecture his preference for using his own images over AI-generated ones the details of his content "knitting" which stitches together his current and prior content the analytics tools he uses to track traffic to his blog how he uses his blog as a conversation starter Rob's bio Rob Hoeijmakers is a passionate web strategist with over 30 years of experience. Known for his curiosity and love for recognising patterns, he excels in crafting engaging content and innovative web solutions. Rob writes insightful blogs and is a hands-on builder of content, chat, and messaging platforms. A dynamic public speaker, he frequently discusses web strategy, digital marketing, and AI, always focusing on enhancing user experiences and client success. Connect with Rob online LinkedIn Instagram Twitter Web Strategies Web Strategies (Netherlands version) Chat voor Bedrijven (Chat for Business) Video Here’s the video version of our conversation: https://youtu.be/FRaHqLRWT9k Podcast intro transcript This is the Content and AI podcast, episode number 28. Many of the stories you read in the media about the adoption of AI tools cover enterprise workflows and other uses in large organizations. It turns out that LLM-based applications can also help tiny, one-person companies. Rob Hoeijmakers is an independent web strategist based in Amsterdam. AI tools like Whisper and Perplexity have revolutionized his research and writing workflows, letting him focus on his websites' information architecture and the business of blogging. Interview transcript Larry: Hi everyone. Welcome to episode number 28 of the Content and AI podcast. I am really happy today to welcome to the show Rob Hoeijmakers. Rob is a web strategist based in, are you in Amsterdam? I forgot. Rob: Yes. Amsterdam. Larry: Amsterdam. Yeah, in Amsterdam here in the Netherlands. I'm also here in the Netherlands. And also as part of any web professional nowadays, he blogs a lot and we were talking at an event a few weeks ago about his blogging and I said, Oh, tell me more. And I'm like, wait, I have a podcast. Let's talk about it on the podcast. So anyhow, welcome Rob, tell the folks a little bit more about what you're up to these days. Rob: Yeah. My name is Rob Hoeijmakers. I'm a web strategist and for content marketing, I blog a lot. It's not only marketing, it's also way of learning and keeping up. I am into LLMs driven chat bots. I did it with the ReSViNET, which is on the, which is RS virus thing. So that's something I'm working on currently. And then of course for my blogging, I write a blog in English, I write a blog in Dutch and I have another one in Dutch on chat for companies. That's what I do. Larry: Oh, nice. And the main thing, you do a lot, like all of us these days, but what I really wanted, hoping we can focus the conversation around is the way AI has helped you in your blogging workflow. Larry: Because when you think about blogging is like the old thing about the power of the press belongs the person who has one. We all have a printing press now. We have our own blogs, but we don't have the whole editorial staffs that giant publishers do. Is that how it feels with AI? Does it feel like you have a team now? Rob: Yeah. Absolutely. Absolutely. I feel like being the manager of a rather big team, and it's really a joy because it's so many chores I've been able to delegate and I've been able to be more productive. I. Rob: I've been able to be more deeper into things because I can have conversations, I can do research things if you have to do that through Google, and I'm basically doing all those things alone. I don't have a big group of people. I don't have a big office with all sorts. Of course, I have friends and colleagues who are into this as well, but they have busy lives. Rob: So I have loads of conversations with the LLMs to deepen my knowledge, to brainstorm, to get creative, to see relations, to see patterns to different sort of developments in society and especially in the digital world. Larry: Hey, one of the of your most recent blog posts was about the kind of epiphany or something you had around, because we're recording this just when the Scarlett Johansson thing around OpenAI came. Tell me about that blog post. I thought that was hilarious. Rob: That was extremely funny because I do a lot of research, but the thing that really helped me is Whisper and Whisper is the natural language recognition and generation within ChatGPT. So it makes me completely hands free, I put on my Air Pods and I go for a walk and I have conversations, long conversations on certain topics, which is fun, can be topics for work or also I have a lot of private things I like to figure out. Rob: Anyway, so the voice I had was Sky, and I thought it was really, really nice gentle voice. And then only till yesterday, suddenly there was a completely different sound, and it really gave me goosebumps because I thought, Hey, what's happening here? I really felt like stepping under a cold shower, it was really a shocker. Rob: Which is funny in itself, but also it worried me a little bit because I already noticed how attached I got to the voice and I was talking first person to it, but I'm talking to OpenAI, it's a company, they make a bug, they make money out of this tool, and I'm just a consumer, I'm a customer. Rob: So that was really, really a good wake-up call for me. Larry: Well, that's really interesting. As a web strategist, you probably, the time cycle of figuring that out was like, oh, wait a minute. This is kind of creepy. But it also gets at the power of the conversational. A lot of people have pointed out that these LLMs aren't that much fancier or they are in some ways, but the thing that really may have made them come to the fore is the conversational interface and especially the personalities associated with that, it sounds like. Rob: Yeah, definitely. Because it frees you up from your desk. I used to do research and I was in front of my computer, in front of my desk, and that limits your thoughts, that limits your possibilities. Rob: So because if you go for a walk, you give your eyes freedom and they can wander around and you're less time pressed. And for me, that was really a change of my life, a change of my daily life I mean, and by that of course, also my bigger life, but these are hours and hours. I can do that. Rob: As a strategist, you need to try and think a little bit deeper or not just choose for the possibilities that are already there, but come up with new things, a certain creativity, and it involves a lot of societal developments, but also of course, people and people you also need to study. Rob: How do they function? How do they work? How do they work as a team, what sort of infrastructure do we need to cooperate successfully? It could be things like very practical things like Canva. I do a lot with Canva for social media utterances or social media things. And then these are always complete worlds nowadays. So they're big. Rob: And then you need to figure out how to cooperate with an external team. Do they have to have a subscription? And if they have a subscription, can you share all your assets? So many things I needed to research I used to do in front of my desk, I now can do with the walk, but then I also noticed that you get a little bit attached to it emotionally as well, and I think that's not actually, I don't think it's a good thing. Larry: Yeah, that's really interesting. Well, first of all, the fact, I mean, I've actually done a fair amount of research around walking and creativity and ideation. It's one of the best things. If you're stuck, you just get up from your desk, go out and take a walk, but now you're out walking and you have this creative companion that you can chat with as you walk. Larry: That seems really powerful. But it's also like we talked before we went on the air, something you just said reminded me of this observation you made that some creative people feel like they're cheating when they use AI, but it's really more like delegating. And that kind of gets through the people stuff you were just talking about. Can you talk about- Rob: Have to be inherently lazy, shamelessly. And lazy because laziness of course has very bad name and we need to be productive all the time. And I've noticed that's okay. If you live in a world of scarcity, you have to be productive, you have to work hard, and you make sure to survive, etc. Rob: But when there's so much, when there's abundance, then a certain laziness and a certain things are actually tools for survival as well, because you can optimize and then you get so much information, there's so much available that yeah. Rob: I think actually it's really, really good to change your mindset with the changing tools,

Chelsea Larsson The arrival of generative AI gives content designers a whole new toolkit. As with any new set of gear, there's some learning that comes with the new capabilities that the tools afford. At Expedia, Chelsea Larsson is leading her team of content designers into the AI design future with fresh takes on the planning, design, and evaluation skills that designers have always relied on. We talked about: her work as a senior director of experience design at Expedia how she is facilitating with her teams the shift from product development design to AI design how she has identified new capabilities that AI brings and is incorporating them into product road maps how content strategists and architects help them decide whether to use generative AI or structured-content methods their shift from front-end content design to working with back-end engineers and architects how new LLM-driven applications of conventional content-evaluation criteria permit them to scale up their content design work their goal of creating good-quality content at scale how content designers are shaping the future of conversational ecosystems how AI lets content designers do more strategic thinking, in particular about how to apply their insights at scale her take on the recent rounds of tech layoffs one of the new roles that are emerging for which content professionals are well-suited, like the new position of model designer the origins of their AI program in a simple application of gen AI to partner content creation how to bootstrap the implementation of AI content practices in your org how to identify opportunities to help your customers by matching their content use cases with your AI capabilities her message to content designers: "don't be afraid" and keep learning Chelsea's bio Chelsea Larsson is a Sr. Director of Experience Design at Expedia Group where she leads the B2B Content Design team, partners on strategic design initiatives, and builds AI travel tools. Chelsea loves to chat about Content Design in genAI and UX design for travel. She shares her thoughts on both topics via the Smallish Book newsletter and conference stages around the world. Her favorite book to gift loved ones is the delightful Chirri and Chirra series. Her favorite sandwich is a turkey club. Connect with Chelsea online LinkedIn Smallish Book Video Here’s the video version of our conversation: https://youtu.be/qKr7o5aKQrM Podcast intro transcript This is the Content and AI podcast, episode number 27. The arrival of generative AI tools gives content professionals a whole new palette of design capabilities. Learning how to take advantage of these new opportunities so that they can shift from product-development design into content-driven AI experience design challenges many content folks. Chelsea Larsson sees these challenges as a chance for both her and her team at Expedia Group to stretch and grow and to scale their impact as design professionals. Interview transcript Larry: Hi everyone. Welcome to episode number 27 of the Content and AI Podcast. I am really delighted today to welcome to the show, Chelsea Larsson. Chelsea is a senior director of experience design at Expedia Group. And welcome Chelsea, tell the folks a little bit more about what you're up to these days. Chelsea: Thanks for having me, Larry. As you said, I'm a senior director of experience design. I lead the B2B content design team at Expedia Group. So we call that the partner content design team, because we work with Expedia partners. I also lead the Generative AI Experience Design Program, which we'll get into later and lean in on a couple of strategic initiatives at Expedia. Larry: Cool. And I think one way we were talking before we went on the air is we were talking about the idea of these AI learning machine, and that seemed to resonate with you as a way to describe what you're up to. Can you tell me about the machine you're building there? Chelsea: Yeah, so when I first started getting into AI, which I think was around a year ago, and talking about generative AI here, of course, I saw a kind of paradigm shift in how content designers specifically could work in AI fields, and it kind of led me to create what you called the learning machine, because when you're working with AI features, the planning is different, the designing is different, and the evaluating is different. It's not fundamentally different, but there are new layers to consider. Chelsea: And those layers led to a lot of questions on, well, how do we plan for the right AI opportunities in our product roadmap? How do we design these AI interactions, questions, when do we disclose that AI is being used? How do we signify that AI technology is being used without words? So what kind of iconography do we use? And then how do we evaluate the output differently than we would evaluate the output if humans had generated the content? So, when you think about those three different pillars of work, planning, designing, evaluating, we were led to, and I spearheaded this, create a program of critiques, guidelines, leadership forums, ways of working, which kind of has created this learning machine as you called it, which I love, and I can get into that a little bit. Larry: Yeah, and I love that, they sound like familiar practices, but talk a little bit about how each of them manifests differently in the AI world. Chelsea: Yeah, so when you think about, they're absolutely familiar processes and it's what we've been doing as product development designers for a long time, but there are new considerations to take into account. So when you think about the planning, let's start there. Your company's not going to just put AI into the experience. AI is not a solution. It is a avenue to get to the outcome that you want as a business, but you do have new capabilities now. You have text generation, you have text classification, summarization, you have multimodal content generation. You can create photos, you can create videos, you can pull out sentiment analysis. So with these new capabilities, you can matrix those to the outcomes that you already want to have or the user problems that you have in place. And by matrixing those with the new AI capabilities, it results in a change to the roadmap. Chelsea: You can plan for new outcomes because of the capabilities that you have with AI. Without understanding those capabilities, that is a hard conversation to have. So that was the change that needed to happen is educating our designers, our content designers and our product folks on what these new are. And that education at Expedia has kind of come about in these forums that I spoke about where I have taken machine learning scientists, product people, and designers and kind of for the first time, put them in a shared space and critique where we are sharing with each other ideas, capabilities and user problems. And those are kind of coalescing in new road mapped opportunities. So that's kind of a different way that we've started approaching planning these AI opportunities. Larry: Right, and I'm wondering if each of those parties you mentioned the ML folks, the design folks and the product folks, do they each bring different perceptions of those capabilities and is the mix different than it was before in those kind of relationships? Chelsea: Yeah, so there's also a fourth person, a fourth role who I've partnered with quite a lot in the past year, which I guess I'll call them a content architect at Expedia. They're called content strategists, which I know is going to be super confusing for this community. They're people who are really highly skilled in structured and unstructured content. So they're the NLP experts of the world. They understand BERT, which is a bidirectional language assessment pattern. They understand structured content in a way that makes it really easy and valuable, to have them on your team, to let you know as a content designer, if your solution should be generative AI or if it should be structured content. And that partner brings that knowledge to the table, they let you know kind of what the content landscape is and what the best content tool is to use. Chelsea: I think in the future, our roles will probably become one because they also usually typically have a writing background, a taxonomy background, a library science background, but they also have kind of a data, an engineering understanding. If content designers could lean more into that content modeling, content architecture side of things, these roles would basically overlap. But right now, those are two different roles where I work and are very helpful for partnership. Chelsea: The machine learning scientists, they bring all of the LLM knowledge, so they're helping us understand the base model, the behavior of the base model, what we can expect. They're helping us fine tune the model based on our prompts, based on our system instructions, the definition of good that the content designers are creating. They also help us understand the cost of scaling out some of the proposals that we have. We have to pay for the tokenization of the outputs, so how expensive is it going to be to generate this type of content? Chelsea: So they're the system and scaling experts, and we work with them really closely on the behavior and the output. We work with the content architects where I've talked about before on the inputs. What does this content need to be, how does this content need to be structured as an input and with the machine learning experts, how can we fine tune this output to get to the place that we want? All of these people understand the input and output, but they all have different levels of expertise where I work, I think it's different at different companies. Chelsea: And then the product folks are still doing what they've always done,

Patrick Stafford Like most tech professionals, content designers are extremely interested in how AI might affect their work and employment prospects in the future. Patrick Stafford and his colleagues at the UX Content Collective recently conducted research to explore the impact of AI on the future of the profession, as well as the attitudes and opinions of content designers about new AI tools and practices. We talked about: his work as the co-founder and CEO of the UX Content Collective the high-level findings of his recent research on the impacts of AI on content design the coincidental timing of the release of GPT-3 and the wave of layoffs in content design and other tech professions his take on the current content design job market, that it's now a more typical market comparisons of the job market in 2016-18, 2019-21, and and from 2022 through now the decline in corporate training budgets recently his take on working "with" AI as well as "for" AI products the emerging critical role of content designers in ensuring the ethical use of AI his observation that most of the new AI jobs being created are being staffed from within companies, not by hiring outside talent the growing importance stated in many job postings of being familiar with AI tools the main benefit of AI for content designers: the ability to scale the important role of content designers applying best practices and design sensibility to gen AI output how the UX Content Collective curriculum has evolved in response to the arrival of AI the surprising finding in their research that 80% of people either feel the same or more hopeful about the industry after the introduction of LLMs and AI the upcoming revival of his podcast Writers of Silicon Valley Patrick's bio Patrick Stafford is the CEO and cofounder of the UX Writers Collective. He is a former Lead Digital Copywriter for MYOB, the largest accounting software provider in Australia, and has consulted with several businesses on UX content strategy. Connect with Patrick online LinkedIn UX Content Collective The Future of AI and Content Design research report Writers of Silicon Valley podcast (reboot coming soon) Video Here’s the video version of our conversation: https://youtu.be/ijMMmsWQZKo Podcast intro transcript This is the Content and AI podcast, episode number 26. The arrival of GPT-3 and the explosion of interest in generative AI caught many in the content-design profession by surprise. Arriving as it did around the same time that mass layoffs hit the tech industry compounded the anxiety around this new tech. Patrick Stafford and his colleagues at the UX Content Collective recently conducted research to explore the true impact of AI on the profession, as well as the attitudes and opinions of content designers about new AI tools. Interview transcript Larry: Hi everyone. Welcome to episode number 26 of the Content and AI podcast. I'm really happy, today, to welcome to the show, Patrick Stafford. Patrick is the co-founder and CEO at the UX Content Collective, which you hope you've heard of. Anyhow, welcome Patrick. Tell the folks a little bit more about what you're up to these days. Patrick: Thanks, Larry. I'm really glad to be talking to you today. It's always a pleasure to speak to you. So yes, as Larry said, I'm the co-founder and CEO of the UX Content Collective. We started in 2019, and we offer a range of courses and workshops related to UX content. So that could be from a broad beginning in UX writing fundamentals to more specialist skills like content ops or even things like systems thinking, which is a workshop we have coming up, and a range of different courses in writing skills and accessibility, localization, a variety of different skills that content designers or content adjacent professionals may get something out of. So that's what we're doing and of course we have a very big interest in AI at the minute, given everything that's going on, and we're starting to delve into that as well. So that's me. Larry: That was sort of the trigger. I always liked talking to you too, but the trigger for this specific conversation was you all just recently did a study on, I can't remember the exact title of it, it was about the impact of AI on our work. And I would love to hear ... I'd love to go through it. I know there's more to it, but you share, in the report, five insights and discoveries that you made. I don't know, maybe walk through the top-level findings of that survey. Patrick: Yeah, sure. And I have to say, Larry, a big round of applause has to go to you for championing this topic, I think, because for a lot of content designers or even just people in content, in general, when generative AI came along, people felt very lost, and they didn't really have an anchor to have to ground them in the future possibilities of what's going on. And so I think your podcast is a great foundation for people who are trying to understand what's happening. So kudos to you. I just want to start that off by saying that. Larry: Thanks. Patrick: So I'll back up a little bit to get the context for why we wanted to do this. So we have been watching generative AI for quite a while. We were publishing blog posts on this in 2019, 2020 before OpenAI made their model accessible to the public. And at that point, we were really sort of just keeping an eye on it saying, "What's going to happen when this releases? It's still a way off, but we need to start thinking about when AI can do some of the work for you, it really forces you to think what is the quote-unquote work that you are actually doing?" And we've encouraged people to a very strategic mindset about their work for a while. Patrick: And so when Open AI released GPT to the world, we were like, "Okay, well, this is something we've been talking about for a while. We kind of have the context for it. We're not taken by surprise here." But we are constantly asking our students, "What are your thoughts on everything that's happening in the industry?" And for a lot of people, they felt very uneasy about AI, and that is partly due to the fact that OpenAI announced the ChatGPT model, or excuse me, the GPT-3 model was the first one they announced to the public when all the layoffs were happening in content. And so you have this mix of things happening at the same time, and there's just a lot of unease. Patrick: But over time, I guess, maybe over the next year, we began speaking with people who are interested in generative AI within the content design community, and starting getting their perspectives, people who are actually working with it. And we just wanted to hear from a lot of people in our student community what they think about AI now that it's been out for a while, now that they've become familiar with it, now that they've probably messed around with it a little bit, now that we've seen a lot of headlines about it, we wanted to actually ... now that things have sort cooled down after the initial rush and burst of energy after everything happened, we wanted to ask people what do you actually see in this day to day? And we asked about 150 people, all content designers or content design adjacent. Patrick: So we do have some people in there who are in long form content writing, but the vast majority are content designers, a few technical writers. Some people focus purely on information architecture, which I know will make you very happy, Larry. But the vast majority describe themselves as UX writers and content designers. And we found that the vast majority, so nearly 100%, have tried language models, and they've tried working with them and playing with them and just experimenting with them. And what was more interesting, though, is that 4/5 of people now use them for work. So 4/5 of content designers now use language models in their work, day to day. Patrick: Now, that doesn't, necessarily, mean they all find them useful, so we drilled down into that and we asked them questions about how useful you find it. And that was one of the other major findings was that 21% find them very useful, and 40% find them somewhat useful. So, I guess, we can talk about some of the other findings, as well, and we will do that, but already, off the bat, right there, to me, that says a lot about where we are in the content design community. 82% of content designers are using them for work, and 60% either find them very useful or somewhat useful. So that actually struck me, because I thought that the number of people who would find them very useful would be lower than that. But I'm not sure what your reaction to that was, but that was certainly mine. I was quite surprised. Larry: Yeah, no. It's interesting to me, because I'm trying to remember what the rapid adoption and appreciation of a technology. I don't know if anything like when ... I'm old. I think of going from word ... from typewriters to word processors, and from word processors to the web, and all these things. Those were more than a year for them to get to those kinds of numbers you're talking about, where 80% are using it, and 60% of those find it really are somewhat useful. Patrick: So that's interesting to me that it's sort of ... and I don't know. Maybe that's just an ... everything is accelerating, and it's like we just happen to see this acceleration. But, hey, I wanted to go back to one thing you said in there, because I think there's ... I want to tease out something, and see if you developed any insight around this from your research. Is it just a coincidence there were already other dynamics going on that were resulting in some layoffs in the UX and the content design world, and the arrival of ChatGPT? I think, whether they're related or not, they're conflated in people's heads. Do you have any thoughts about that? Patrick: I do have thoughts about that, because we ...

Wouter Sligter Figuring out how to best adopt new technology is difficult at any time for any organization. AI tech rachets up this challenge to new heights. Wouter Sligter helps companies understand the capabilities and limitations of LLMs and related technologies to create trustworthy experience-delivery platforms. Transparency is a key element in implementing solutions that evoke and support the authentic human experiences that underlie these systems. We talked about: his background as a UX-focused designer and his shift to conversation and AI design the growing number of business use cases that his work supports as well as the growing palette of tech tools that he has to work with how he creates authentic and trustworthy experiences with LLMs and adjacent tech the benefits of RAG (Retrieval Augmented Generation) the growing number of platforms that support building AI experiences the huge failure rate of conversational AI implementations, and how better design might improve the success rate the importance of being genuinely customer-centric when implementing AI projects how his background in language and music helps his AI design work, in particular the benefits of "being comfortable with the uncomfortable" the importance of companies being transparent about their AI implementations how localization manifests in the AI world the growing acceptance of chatbots by consumers his advice to jump into AI now, beginning with due diligence about how you'll implement it in your organization Wouter's bio Wouter Sligter is a Senior Conversation Designer and Generative AI Engineer. He has been a committed team lead and has consulted for a large number of Conversational AI implementations, most notably in Finance, Healthcare and Logistics. He has an innovative mindset and a sharp sense for understanding user needs. Wouter always looks to improve the conversational user experience by following iterative design patterns and verifying outcomes through data analysis and user research. Both predictive NLU and generative LLMs and SLMs are part of Wouter's toolkit. Wouter has a background in ESL and IELTS teaching at language centres and universities in Vietnam. He has developed a strong awareness for language and cultural peculiarities, with native fluency in English and Dutch and good conversational skills in Vietnamese, German, and French. Connect with Wouter online LinkedIn YouandAI.global Video Here’s the video version of our conversation: https://youtu.be/Ak0liSLR8_0 Podcast intro transcript This is the Content and AI podcast, episode number 25. One of main reasons that people have taken so quickly to AI tools like ChatGPT is their conversational nature. People like talking to each other - and to computers. In human conversation, we've developed skills and instincts that help us determine the trustworthiness of the person we're talking with. In tech-driven conversations, we often have reason to mistrust. Wouter Sligter helps companies build conversational systems that express the authentic humanity of their creators. Interview transcript Larry: Hi everyone, welcome to episode number 25 of the Content and AI Podcast. I'm really delighted today to welcome to the show Wouter Sligter. I met him in Utrecht in the Netherlands. He's in the co-working space we both work out of. There, he is a conversational AI consultant. He does conversation design and he's a generative AI engineer. He has his own company called You and AI Welcome, Wouter. Tell the folks a little bit more about what you're up to these days. Wouter: Hi Larry. Very good to be here. Thank you for inviting me. What am I up to? I think you mentioned the three things that I like most doing and that I do most often. I've come from being a self-employed freelance designer really, when in 2018, Facebook started with their chatbots on Messenger. I jumped in and quickly caught on and got a lot of clients worldwide, really building chatbots for them. At that time, I was mostly working on the content side with what you see is what you get kind of flow builders and slowly got pulled into the tech side as well. Wouter: I worked for enterprise as a consultant for a few years in the Netherlands, and then I decided last year to go back to being freelancer, and that eventually culminated in now having my own company, You and AI, with which I'm doing all kinds of outsourcing work from Vietnam. Of course, lately a lot of work is involving generative AI LLMs like RAG implementations and fine-tuning. In my bones I'm still a Uxer, so I'm always looking to build stuff that actually works for people rather than only playing around with tech that no one uses. There's really my strong point, I think. Larry: I love the way you say that. I have many engineer friends, but they're really prone to just building stuff because they can. We're both designers and I love human-centered design and human-driven design decision making. One of the things you said in there, you kind of reminded me of your heritage because you come out of conversation design kind of UX and conversation design specifically within that. That field has evolved. All these new generative AI tools have a conversational or a chatty kind of interface, but you've been working with that kind of interface, but the bones underneath these interfaces are way different now. Five years ago, it was all NLP and kind of flow building tooling. Can you talk a little bit about the transition and your skillset and the demand for your kind of talent over the last five years? Wouter: Right. Yeah, so I think in the beginning, because most of my work was involving Facebook, there was a lot of demand for the marketing use case, the sales use case, like getting cold leads to convert and to some extent also customer service. Then when the enterprise-level companies jumped in, the customer service field became much bigger. I think now today, that's still the major use case for most conversational AI. But now with the LLMs and all the generative AI functionality that we have, the possibilities have become so much bigger. There are so many more use cases that can successfully or let's say an acceptable level of quality be implemented or be used. Wouter: Right now, I'm getting all kinds of stuff in, so it can be like fine-tuning for reading Excel sheets, fine-tuning for creating posts on LinkedIn, but also still the follow-up of let's say the fall-backs on the traditional NLP bots where traditionally it would say, "Oh, sorry, I don't know that." We now often use LLMs to fill in those gaps and pull from the company website or company knowledge base to answer even those questions better than they could ever before. Larry: That's right. You're just reminding me, I kind of phrased it as an either/or an evolution in development, but we haven't left the old stuff behind. Like you just said, you're still the fallback if an LLM or another agent fails. You have just a bigger palette of conversational tools to work with, it sounds like. Is that accurate? Wouter: Yeah, definitely. Definitely. And that makes my job so interesting because we started with the rule-based stuff and then NLP came in and then we thought like, well, now it's getting really interesting and a little bit difficult. Now we're at a stage where we have these LLMs that produce or don't produce the output that we expect with all kinds of hallucinations and technical challenges, which I think make my job so much more interesting, but also more challenging in a way because you need to explain to everyone what every bit of tech does and make sure that the clients who are actually using it understand why we're using that tech so that they can also explain to their stakeholders why things work or why they don't work. Larry: When we talked a couple of weeks ago... Oh, I'm sorry. You were going to say something? Wouter: Yeah, yeah, no, go ahead. I can keep talking for ages about this stuff. I'm actually trying- Larry: I'd love to circle back to something we talked about a couple of weeks ago when we were preparing for this. One of the implications of that, what you just said, this evolution of the tooling, you go from rules-based, like it's all guardrails all the time in a system like that to NLP, which has intense understanding and all those utterance magic to these crazy hallucinating LLMs. I mean, I'm exaggerating of course there, but there's been an evolution in that practice. One of the big things that comes up just every conversation and every conference and event I go to is the importance of trustworthiness and authentic, because these things are conversational. Larry: They sound like a human, but it's not always authentic sounding. So there's this sort of combination of things, at least I conflated them in my mind, this notion of authenticity and trustworthiness. Can you talk about how you instill those kinds of... How do you help people trust these experiences as you're navigating them through? Wouter: There's a lot of levels on that question. Let me just pick one first. I think that when a business, when an organization chooses to work with the kind of AI that we have now, then they need to decide if they're comfortable with that level of risk that they're allowing in their applications because we know that LLMs are not perfect. They do hallucinate even if we put the guardrails in place. Actually, you have to decide for each use case and each implementation which level of risk you are comfortable with as an organization. For an internal use case, it might be okay if 85% of the answers are correct, but for a customer-facing use case, you might want to see 90 or 95 or even 100% depending on the context. I think that's one important thing to note. Wouter: With that extra level of quality really, of output quality,

Lasse Rindom Lasse Rindom both consults with enterprises on AI projects and talks with business and technology experts about their thoughts and discoveries. In both his consulting practice and his podcast conversations, Lasse has discovered both tremendous opportunities and potentially pitfalls when adopting enterprise-scale AI solutions. We talked about: his work as an AI leader at Basico, the origins of his AI-focused podcast, The Only Constant the unexpected opportunities that arise from the new ability to work with unstructured content that AI affords his quest for use cases that will help identify new governance structures and operational frameworks some examples of AI workflows that enable new business capabilities, like the ability for non-coders to query an agent that can write SQL queries for them his candor in his consulting practive about the possible pitfalls of AI tech, in particular the consequences of LLM hallucinations how current LLMs fall short of natural language, acting more like "chaotic code" the unfortunately common belief that generative AI can be applied one way that he is addressing the "lying robot" problem: using multiple AI agents to correct each other (instead of fine-tuning the models) the current strategic AI deficit in the market, resulting in consultants pushing untested engineering solutions the differences between how enterprises and SMBs consume tech solutions the importance of holistic thinking and staying focused on core problems as you explore AI solutions Lasse's bio Lasse Rindom is AI Lead at Basico and a leading expert on AI and automation. He has previously been global technology manager at facility management giant ISS and CDO of Baker Tilly Denmark. Lasse is a frequent debater on LinkedIn, a Gartner Peer Community ambassador and is host of the podcast “The Only Constant” in which he has deep discussions with global thought leaders on what AI and technology means for us as humans and as businesses. Connect with Lasse online LinkedIn Video Here’s the video version of our conversation: https://youtu.be/_fdAweq3Wuw Podcast intro transcript This is the Content and AI podcast, episode number 24. I generally focus these interviews on content practices, but I'll zoom out now and then to explore the broader strategy and technology landscape. Today I'm talking with Lasse Rindom, a thoughtful and knowledgeable consultant who works with enterprises on big AI projects. He's also a podcaster who talks with business leaders around the world about AI and tech. In his conversations and consulting work, he has discovered a world of lying robots, chaotic code, and strategic deficits. Interview transcript Larry: Hi, everyone. Welcome to episode number 24 of the content and AI podcast. I'm really delighted today to welcome to the show, Lasse Rindom. I'll have him pronounce his name correctly in just a minute. I don't speak Danish, apologies. But Lasse is the AI lead at Basico, a Danish consultancy that works with big enterprises in Denmark and other places, I'm assuming as well. But welcome to the show, Lasse, to tell the folks a little bit more about what you're doing there at Basico. Lasse: Hi, Larry, and thank you for having me on the show today. I'm really thrilled to be here. So my name is Lasse, Lasse Rindom. That's how you say it in Danish so people could know that. I always say it's okay to say Lasse. Everyone knows that, that's dog. Lasse: I am the a AI leader at Basico, which means I'm defining our go-to-market strategy and our products in the AI space, and we focus very much on the back office function. So that's your legal, facility management, finance, HR payroll and finance IT systems. So I'm defining how we want to approach the AI market in that space and primarily in Denmark. Prior to that, I've had stints at an analyst firm, very short stint, and I've been a chief digital officer and head of digital at an SMB and an SMB consultancy. Plus, I have also previously been very heavy in the automation space, especially around RPA, where I built the framework and the technical setup for ISS globally some years back. So I come from an automation background, but actually my major is in history. So I'm not necessarily the tech guy born, but I think I cover a lot of ground. I have a lot of long lines and I try to make sense of everything I know all the time. Larry: And you just mentioned that you're a history major and you're always trying to make sense of things, which leads to how I first discovered you is through your podcast called The Only Constant. And I just love the evocative name that like we're in an age where the only constant is constant change. Can you tell me a little bit about where the podcast came from and how it fits in your practice now? Lasse: So where it came from was basically two things or three things maybe. I wanted to do a podcast for a while. That's one thing. I think that was something I had as an aspiration. And the second thing is that I think the market needs an explorer room, some place where we can explore because we don't know what's going on right now. Lasse: The thing with the generative AI explosion last year, especially last year since November 2022, is that no one really knew what this was. We didn't have it before. It's something that took everyone by surprise. Even Gardner, even the big GSIs, everyone was taken by surprise by this. This means there's no one who can really explain what it does. Lasse: Everything at time, you've heard someone try to explain it. If you go back and look at podcasts a year ago until now and people explaining what it does and how it works, you'll see that they've been corrected so quickly. So everything gets dated very quickly if you do an explain podcast. Lasse: But if you do an explore, where you explore, what are the business problems, the things you should focus on instead and what are the big picture things, the ethics things and all those things? Then you might get to something that has a little bit more longevity and that's what I'm trying to aim for with my podcast. So it's explore more than explain it, I say. Lasse: And thirdly, I've had over the years, I have become sort of a LinkedInfluencer. Is that what we call it, LinkedInfluencer? I call it that. I've gotten to know a lot of people, especially in the automation space and also in the tech and business space in general. I've had a lot of coffee chats with these guys and I thought, "Why not turn these coffee chats that are really engaging and interesting into a format that other people could listen into?" Lasse: I think that was primarily where I thought I can get my content from there and get my good people from there. So that's why I wanted to do it, turn that into something that I could share with other people. Larry: Nice. And you've had some really great people on, and you're doing this in practice where you're working with big leaders in big enterprises in Denmark. What are emerging as some of the top level concerns of folks? And I love that so much of our world in the content world is about generative AI, around generating content and working with/in content workflows. Larry: You're at sort of another level trying to figure out how to help with what a lot of people would regard as mundane back office stuff, the HR and all that. But even there, you're finding a lot of opportunities for AI, right? Lasse: Well, the funny thing is that there's a lot of opportunities there, but they're not the typical opportunities you'd expect I think. People have been approaching this for a year as a chatbot feature, basically something that can generate stuff because that's what amazed us. But as Kurt Cagle said on my podcast, "This is a machine that lies." He was actually very fascinated by that. We've made a machine that lies. I think that's awesome saying just go back 500 years and say, "Hey, someday we'll make a machine that lies." And people are like, "What?" We have that now. But it makes it also very difficult to work with in almost any area. Lasse: It's also difficult in the typical ones we thought immediately, marketing or customer service because you can't have something that can be jailbreaked away or that can be lying or can be bland. That's not what you want. You want something that's cutting edge when you connect with your customers, right? And in back office you need accuracy. It just needs to work. Lasse: As you said, it's very transactional, it needs to work all the time, it needs to have no hiccups. This is something that just makes the business do what it does best by supporting it to do what it does best. So if you have an AI that messes that up or lies or something, then you're having problems there as well. Lasse: So where does this really fit? I don't know, maybe just telling short stories to your kids or something that's where it works out of the box or as an assistant where you can sort of chat with it and get ideas from it. That's also a thing where I think the chatbot works, if you use it in concert with yourself and your own ideas, you use it as a sparring partner you have at hand all the time. Lasse: But I think that what's emerging right now is that people are realizing that this is not just about playing canvas generation, but also about restructuring, interpreting, translating things into structures that we didn't have before. So basically taking something unstructured, getting some data from it, and then creating something structured that we can analyze on top of. Lasse: This means that we're also doing something we haven't been able to do for years in technology. We've never been able to work with unstructured data, but suddenly we have a means to do that. I think that's what people will realize over the next couple of years that this is actually something that's very,

Gerry McGovern As we navigate our paperless offices and admire our sleek compact computing devices, it can be hard to imagine the impact that our digital experiences are having on our communities and the planet. Gerry McGovern studies the environmental impact of the digital industry. He has uncovered an alarming story of unsustainable growth, toxic side effects, and human misery, which he shares in his book, World Wide Waste. We talked about: how he became an environmental activist focused on the impacts of digital the phenomenal pace of growth of digital infrastructure the impact on local communities of the big data centers that house cloud infrastructure how the compute-intensive nature of AI exacerbates his observation of the long-standing lack of transparency in the AI industry the "snake oil sales" aspects of AI the troubling use of "forever chemicals" by the semiconductor industry the material impact of computer chip manufacturing how human over-consumption and the environmental impacts of AI overlap his advice for actions you can take to mitigate your personal impact: slow down and use your brain more think local - local foods, local computer storage, etc. prefer text over images and other high-bandwidth communications Gerry's bio Gerry’s latest book, World Wide Waste, examines the impact data waste and e-waste are having on the environment and what to do about it. Gerry also developed Top Tasks, a research method used by hundreds of organizations to help identify what truly matters. Connect with Gerry online Mastodon LinkedIn GerryMcGovern.com Video Here’s the video version of our conversation: https://youtu.be/W5-BMTTEUik Podcast intro transcript This is the Content and AI podcast, episode number 23. It's easy to think of digital media and experiences - including our new AI explorations - as ethereal things that magically traverse the computing cloud to enlighten and entertain us. Gerry McGovern is here to remind you that that's far from the case, that "digital is physical." The data centers that power cloud computing are lapping up water and consuming electricity at an alarming pace, and the arrival of AI is accelerating these troubling patterns of overconsumption. Interview transcript Larry: Hi, everyone. Welcome to episode number 23 of the Content + AI podcast. I am really delighted today to welcome to the show Gerry McGovern. Gerry is the author of the book The World Wide Waste: How Digital is Killing the Planet and What to Do About It. He's also probably better known ... and I originally met him almost 15, 20 years ago when he was talking about customer care words, and subsequently out of that arose, I think, his work on top task methodology. So anyhow, Gerry's a well-established figure in the discipline, has a lot of important stuff to tell us about the environmental costs of AI. But welcome, Gerry. Tell the folks a little bit more about what you're up to these days. Gerry: Thank you, Larry. It's lovely to be speaking to you again. I suppose what I'm up to mainly is ... In a sense, I never thought it would happen, but I've become a type of environmental activist focused on the impacts of digital and how to use digital in a better way, in a less damaging way. I don't think digital can be green in any sense, but I think it can be used to help more our environment and at least to reduce the damage it causes to our environment. So, that's the main stuff I'm focused on. Larry: Yeah. Well, I got to say, I love the idea that you're an environmental activist now, because we need plenty of that. But one of the things about your work that I think has really driven home the point to me that we think of digital as this ephemeral thing happening out there in the ether. It's like no consequence. You can just throw stuff in a hard drive or share something. But this is still connected to the physical world, right? Gerry: Absolutely. And the first sentence in The World Wide Waste says, "Digital is physical," and basically, the cloud ... It's on the ground in these mega data centers that are ... They say between now and '27, data centers will add the equivalent electricity demand of a Germany, or perhaps a Japan, of electricity demand to the global electricity network. So it's growing at a phenomenal pace, the quantity of architecture that's out there. It's very, very much physical. Larry: That's just amazing. And one of the things that the people building those giant server farms and things they're good at is, you don't really hear that much about it. They're almost doing reverse PR or something. Gerry: Oh, yeah. It's one of the most secretive, least transparent industries on Earth, and deliberately so. It's all part of the plan. They will never reply to a press call, or very, very rarely. They've become a little bit more in the last, but it's secrecy, secrecy, secrecy, secrecy, doubled on secrecy, secrecy, secrecy, secrecy. When they buy, you don't even know who owns the data center until the very last minute in the process. So it's all super, super, super, super secrecy stuff, because they know they don't have a good story to tell to the local community or the local area, because data centers are absolutely horrible for a local community. There's little or no jobs, a couple of security jobs. There might be 20 people, maybe 40 people maximum in a mega, mega data center running it, so they bring little or nothing to the local community. They might bring some tax, but behind the scenes, they're often getting more in tax breaks than what they're bringing. So there's not a good story to tell, and therefore they try and stay as secretive as possible. Larry: Interesting. And one of the things you were talking about, that I'm reminded that these are often in small communities out in the boondocks, because a key driver in these things is the need for water to cool. And can you talk a little bit about that, the types of communities that are affected by this, and that thing that you said, that the local governments are giving in tax breaks but getting almost nothing back? Gerry: Yeah. Certainly a large data center, which is a lot, mainly these big ones, these super data centers ... There are these massive, big warehouses, and they can be quite nice as well, so you don't want them close to homes. You don't want them very close to homes, and they need a huge electrical infrastructure, so you need utilities and backup. A lot of them have these mega backup diesel generators so that they've all sorts of redundancies. And then they've a massive water demand, hundreds and hundreds of thousands of liters, of gallons of water a day. And with AI, that's going to grow maybe five or 10, because with artificial intelligence, it's much more processing-driven, and the more processing there is, the more heat there is in the environment. The more heat there is, the more need for cooling. So Microsoft's water demand, I think, went up 20% in a year in 2022. Gerry: So we're talking about mega water demands, and ironically, still, you find them in places like Phoenix or whatever, which is strangely ... Phoenix, Arizona in the United States, which is undergoing a hundred-year drought, which is essentially close to a desert. But water is really cheap, or certainly historically has been really cheap in the process, because they've got this massive underground aquifer that has built up over millions of years and that they're essentially draining dry. It's not just the data centers. It's the industrial farming. And now the chip manufacturers, who are incredibly water-intensive as well, are coming there for political reasons, because of the US-China conflicts. So, you've got a lot of incredible material intensity behind the scenes of this stuff. I saw one study that said that by 2030, an average European would be using as much water for their digital activities as they drink on a daily basis. Larry: Wow. Gerry: And that's just the water. Larry: I have to tell you, I lived in Phoenix. Before I moved to Europe, I was living in Phoenix, Arizona, and on a flight back to Phoenix from someplace ... I can't remember where ... I was seated next to a guy who worked for one of those big chip manufacturers, and I said, "What are you doing in Arizona? There's no water here." And he goes, "Oh, there's plenty of water." So like you just said, the chip manufacturers think that, and those are unreplaceable aquifers. Is there data about ... For example, you can probably compute when Phoenix, Arizona will run out of water, or any number of other places in the world. Are there people looking at that? Gerry: There are. I think in the US, it's not the only place, but something like 80% of US aquifers are stressed, so watersheds are stressed. Arizona has a weird plan at the moment. They're looking to send a pipe down to the ... I don't know if it's the Gulf of Mexico, the ocean in Mexico ... and pipe water from the ocean, which is going to be very expensive, because it's much more expensive to desalinate water than it is to use fresh water. Because these data centers, they need very clean water for all sorts of reasons. You can't get dust or pollutants in the pipes and et cetera in the process. Gerry: So there are some plans there, but generally speaking, there was a study there recently in the New York Times that says the East Coast of the US is saggy because they've extracted so much water. And I think if you would've driven around Arizona a bit, you would've found quite a bit of collapsed land, because when these aquifers empty and the ground subsides and collapse, they'll never fill again, even if it rains, because there's no space for them to actually fill, or at least certainly take them a million years to fill. Gerry: So, yes and no. The scientists are saying yes,

Mike Atherton Mike Atherton is well-known in the content world for his work at institutions like the BBC and Facebook and for his co-authorship of the influential book Designing Connected Content. His latest content project appears at first to be less serious. Newsbang is a daily AI-produced satirical news show. Its content is based on real historical news but delivered by AI-created stereotypical newscasters. The result is fun, but the process of creating the show has added real-world technical skills to Mike's professional toolkit. We talked about: his work as a UX writer and content designer his experiments with AI tools, including the suite of generative tools he's using to create Newsbang, a completely artificial daily news program how he accomplished his goal of creating an ensemble sketch comedy vibe his workflow for the daily production of the "news" show some of the surprising traits of his news characters that emerged as AI generated them lessons learned about the cost of producing AI programming, like the costs of prompting the variety of models he uses to build the show, including open-source models that have more lenient guard rails to permit more edgy comedic content how he creates his own guardrails to achieve the effect he's looking for in the show while still creating a family-friendly show how he developed the technical skills it takes to create Newsbang how his work with Newsbang helps in his day job his hope that more content professionals will follow him into the AI playground Mike's bio Mike Atherton brings years of experience to the UX, IA, and Content Design field, having tackled content challenges at big names like Meta and the BBC. Now, he's focused on developing UX writing systems, exploring the use of AI to do big things with tiny teams. As well as the day job, Mike is the creative mind behind Newsbang, a daily satirical news podcast that's both written and produced using AI technology. With Carrie Hane, he also wrote the book ‘Designing Connected Content’, sharing strategies for seamless digital experiences. Mike lives in the British countryside and loves working from home. Connect with Mike online LinkedIn Newsbang Video Here’s the video version of our conversation: https://youtu.be/lpDa8szujWo Podcast intro transcript This is the Content and AI podcast, episode number 22. Most of the news coverage and social-media conversations around AI and content feel urgent and important. This is serious business, but you can have fun with this technology, too. Mike Atherton has done content work at places like the BBC and Facebook, and he still does proper content design in his day job. Newsbang, his daily, AI-produced satirical news show, has given him both an outlet for his inner comedian and a venue in which to hone important new work skills. Interview transcript Larry: Hi, everyone. Welcome to episode number 22 of the Content and AI podcast. I am really delighted today to welcome to the show Mike Atherton. You might know Mike, he's probably best known as the... Well, he's best known for a lot of things, but he's worked at the BBC and a lot of other interesting stuff he's done. He co-wrote the book Designing Connected Content with Carrie Hane, which a lot of people in my world appreciate. But he's now a content designer and creative technologist based in the UK. Welcome, Mike. Tell the folks a little bit more about what you're up to these days. Mike: Well, hey, Larry, thanks for having me on. It's great to be back. Yeah, I'm a UX writer and content designer by day. I work with various product teams in different kind of companies to write everything from the microcopy, the words on the buttons, through to taxonomy and control vocabulary and all the good stuff that we UX writers like to do. And as part of that, for the last few years, I've been dabbling with these wicked AI tools that have come our way and seeing what I could do with them to try and make them generate content in a particular voice and tone or in a particular way to fit in with a brand voice or a product voice. And that's really got me interested in the styles of writing and the styles of content that models can generate if you give them the right push. Larry: Yeah, well that's why, I mean, I'm always looking for an excuse to talk to you. But most recently in December, you launched this news site called Newsbang, which is entirely AI generated. And I mean, there's a number of taglines I've heard in it, but one of them is "a taste of truth served with a side of satire,"" and it seems like there's a lot of... But anyhow, there's always to what you were just saying, well, there's so much about this project I want to ask you about. But one of the first things is there's a distinctive tone to it throughout. There's a bunch of different personalities in there, a bunch of different topics covered, but you know you're listening to Newsbang, so maybe is that a good place to start with the- Mike: Yeah, absolutely. I mean, the characters are perhaps my favorite part of it. So Newsbang, for the uninitiated, is a daily news podcast, very much modeled around a kind of nightly news bulletin. But in the kind of silly way that you might find in Saturday Night Live's weekend update or old sketch shows, like Not the Nine O'Clock News or particularly my favorite one, The Day Today, which was a BBC comedy show that just turned 30 years old this year. Mike: And a lot of these shows, the joke is the kind of bombastic self-important reportage if you like. And that happens on our show through different archetypes that one might emulate in 1980s BBC science presenter and the other with a kind of hard-hitting investigative journalist and another with a kind of self-satisfied middle-aged sports presenter. And together that gives the show a kind of ensemble sketch comedy vibe, which was really sort of what I was going for and influenced, I think, a lot by these kind of news parodies. I say the shows I mentioned previously, but what if they were actually a real show and had to sustain their length and had to really go out every day like the news does? Larry: And the setup of it is, like you just said, it sort of has these conventions around it. And if you just listened to it and weren't really paying attention, to the extent you were paying attention, you'd get that it's a parody site. But just the tone, the flow, the structure, everything about it evokes that. How did that come together? Because we were talking before we went on just a little bit about, you said, nowadays if you can imagine it, you can do it with AI. Talk me through the steps from that, imagining it and the first episode. Mike: Well, sure. I mean, like many men of a certain age, I started to fancy having a podcast. I even bought this big stupid microphone, but never really got around to doing, kind of put that away. And then I was getting into AI, which was my latest all-consuming hobby. And through that I found a now abandoned project, sadly called Crowdcast. It was a GitHub piping script where it would take a feed of Reddit posts and turned them into some chatty podcast segments and then send them to the Eleven Labs API to turn it into kind of text. I go, "Oh, this is kind of interesting," this sort of scratches is that podcast itch, but with the added bonus of not actually having to talk to anyone or do anything. Mike: So I started to kind of play around with it now. I mean, six months ago, six, seven months ago when this started, I was in no way any kind of developer. I couldn't make head or tail of Python and what have you. But about the same time or GPT-4 came out and ChatGPT was running the kind of full-fat GPT-4. And it was fantastic as a coding co-pilot to be able to make sense of these scripts that I found and didn't really understand and get them running. And whenever I have an error message, I could paste that into ChatGPT and it would debug the error. And then through a lot of baby steps and trial and error, I managed to get a prototype together, and that's a prototype, it was called Relationships on Reddit. And don't look for it, it's not there anymore, because it's kind of an embarrassing now. Mike: But basically I cloned a voice, a celebrity voice, Stephen Fry, and I was pulling in real actual live Reddit posts from a subreddit, and then it would generate a sort of Agony Aunt segment, Agony Aunt show about these problems in the Stephen Fry. And I ran that for about three days and I'd listened to it in the morning and try and figure out, am I even interested in this? Is this content sort of worth hearing? And it was on day three that I realized that there was a bug in my code that was stopping the Reddit events from passing through to the LLM. And so it was happily making up its own stories that unbeknownst to me, and that was a really kind of strange feeling that a bug in code rather than just crashing the script, crashing the computer, would fail in less noticeable ways. Mike: So it also sort of brought me to my senses a bit, and I realized that deep faking a celebrity voice, offering artificial advice to real people's problems was not okay at all. But it did, I don't know, it gave me that aha moment that you could basically turn code into a piece of media with no intervention, recording steps, no editing steps or anything really. End-to-end, you could run a script or a set of scripts that would take information from some external data feed and at the other end spit out an MP3 of a radio show. I mean, what a time to be alive. Larry: You make that sound very simple and it probably is quite doable and easy nowadays, like you said with ChatGPT. Can you just walk me through your tech stack of that? How deep down are you training the model with these Python scripts? Or is this for prompt generation? Mike: Sure. Well,

Elizabeth Beasley As AI is storming into content design and operations, Elizabeth Beasley is taking a patient and deliberate approach to adopting it in her practice. Elizabeth works on security and identity products at Intuit, so the experiences she designs have to be reliable and trustworthy, hence her identification as a "risk nerd." She has also navigated big business changes before, like the shift from cable broadcasting to video streaming, and saw in those transitions the benefits of being a cautious and curious adopter of new technology. We talked about: her role as a content designer working on security, identity, and fraud at Intuit how her background in media and technology have made her a slower adopter of new technology like AI how being a "risk nerd" informs her concern around reliability and trustworthiness in AI how her cautious approach to AI adoption may actually put her in a better position to develop trustworthy AI experiences the new collaborators she is working with as AI arrives on the scene her work on an industry standards body around new security technology the utility of having troops back at the fort to keep the old operations running as your org explores new tech like gen AI how her interest in history informs her approach to change the inherent risks in being first to adopt new technologies her "peaceful Wednesday" practice for preventing and coping with stress and burnout how times of rapid change like this can prompt useful career reflections the recent evolution of her thinking on the "seat at the table" issue Elizabeth's bio Elizabeth Beasley a Senior Content Designer with Intuit’s Identity team. She approaches life with a healthy balance of optimism and skepticism. Because everything is going to be okay, maybe. She used to have hobbies like performing improv comedy and ballroom dancing. Now she enjoys watching other people doing their hobbies on YouTube. Connect with Elizabeth online LinkedIn Video Here’s the video version of our conversation: https://youtu.be/Ny2l_mZgLXQ Podcast intro transcript This is the Content and AI podcast, episode number 21. It's easy to get caught up in the frenetic pace of generative AI technology adoption - unless you have already created rituals to help slow your life down. Elizabeth Beasley created her "peaceful Wednesday" ritual ten years ago to bring some calm to her increasingly fast-paced work life. That practice is serving her well now as she and her colleagues at Intuit develop their approach to incorporating AI tools while continuing to deliver trustworthy experiences. Interview transcript Larry: Hi, everyone. Welcome to episode number 21 of the Content and AI podcast. I am really happy today to welcome to the show Elizabeth Beasley. Elizabeth is a Senior Content Designer at Intuit, the big financial software company. Welcome, Elizabeth. Tell the folks a little bit more about what you're up to these days. Elizabeth: Hey, it's so fun to be here. Yes, I'm at Intuit. Financial services is my life lately, and I've worked in a fun space. I think it's fun, security, identity. I always describe it to my mom or my friends like, I do the part where you create your account, you sign back into your account, you manage your account and I make that easy for you with content design, they still don't quite understand that, but that's the space I work in and I really, surprisingly enjoyed. I worked in banking previously and got into security and now I'm sort of obsessed with security and identity and fraud and it's a fun, exciting space to work, and also I love it because everyone uses it, so it's very relatable and it affects many, many people. So it has a lot of impact. Larry: You can't do anything until you get past that experience that you're designing. Elizabeth: Yeah. Larry: Then you're in and then you can start doing stuff. But you sort of established your cred. You're not like some kind of a Luddite about technology. You clearly, you're deep in it every day doing that, and yet the reason we connected and the reason I wanted to have you on the show is that we connected, I think on LinkedIn, I can't remember exactly how it started, but you're sort of like a slower adopter of AI technologies. And I was like, perfect, I want to get her on the show because every one of the 20 episodes before this were all, and I'm as into it as them, just deep into the technophilia and all the new work things around AI and you're more like, yeah, it's great and you're studying it, you're staying on top of it, but you're not just diving in with both feet, fangirl about it. Tell me a little bit about how that perspective arose. Elizabeth: Yeah, it is, sometimes I feel like I'm behind, but then I'm like, I'm just a late adopter. It's okay. I'm a late bloomer. And I think it's partly because I've seen technology changes before and I worked in television for the first 20 years of my career and watched changes even basically from tape to digital and that really changed people's jobs. And the biggest one though, that makes me think of the way AI was going is streaming sources, streaming video. I worked in TBS and we made that transition from, we are cable network to panicking because everything was streaming and there was a whole TV everywhere initiative where the cable networks are trying to get you to watch their stuff on multiple devices, and that was kind of the beginning. And we were trying to figure out what does that mean? What does that mean for our jobs? Elizabeth: We have produced things everywhere. And it was intense and stressful and scary, and then fast-forward 20 years and I'm looking at it thinking like, "That didn't turn out like I thought." It evolved. Streaming is now actually a lot like cable television again, I was telling someone, I was like, this is funny because now you go to Hulu and you can add channels and build your own cable service. So I think the thing that I've been taking away is, it's a long game and if you get stressed at the beginning, you can burn yourself out and create panic and you don't really need that in your life. So I'm trying to relax into it and just sort of, you want to be aware and learn, but I'm also, you know what? I want to see how other people are using it? How is this going to turn out? Elizabeth: What's the best thing for us? And particularly with AI, which is to me, radically different because there's these moral and ethical parts of it that I don't think we have had to, I haven't had to wrestle with in technology before. Before, it was more like, is this helpful? But this is more like, oh no, is this going to be bad? So it's a little bit more weight as well, if you adopt early and kind of get in there. So I like to play the kind of watch and see where this goes and where do I need to jump into the game. Larry: Yeah, and I love that you have the credibility of having been through this kind of thing before. And as you were talking about it, I was thinking about 20, 25 years ago, I remember just fighting constantly with marketing people who wanted to violate people's privacy. And Seth Godin had come along and said, you know permission based marketing? That's the way to do it. And that's like convention today and all the laws and regulations do that, but we don't have that now. AI is still like the Wild West. It's still unfolding really quickly. Do you see, when you look at, looking especially with that lens of your TV history, I love that perspective on this, are you starting to see any things that you're really paying attention to like, this might be the thing that we look back and go like, boy, that was the wrong thing to worry about? Elizabeth: That's a really good question. I'm kind of, gen AI is just curious to me because I was talking to a teammate yesterday and she's like, "I just don't want to release it until it's reliable," and I was like, "Yeah, that's the name of the game, right?" Getting reliable results and so, a lot of times I'm just wondering, I know we're excited about it and we sometimes want to just, let's use it. It's akin to sort of like, I got this new chainsaw and I need to paint the house. I'll use the chainsaw and it's like, well no, that's not the right tool. So really examining like, what's the right tool for this job? It might be a different form of AI than gen AI, and that's something I'm really conscious of because we get really excited about it and we... Let's consider the other ways to solve this problem and find the right solution. Elizabeth: Now certainly, we have this new toy, let's see if the chainsaw can work, but we might innovate and find a special way to do it, but I'm just sort of thinking, I'm really into the use cases lately. I was like, let's look at the use cases and how do we solve this problem and then really examine if this is the right method. Sometimes you have to go down the rabbit hole of trying it and be like, that wasn't the right method. Larry: Yeah, and as you described it, I think it's like, I love that I'm going to totally steal the chainsaw for painting the house. I loved that because that kind of gets it, you feel like there's some of that going on right now but I think more of the point is just that backing away from like, if all you have is a hammer everything looks like a nail, with AI technology, and thinking back to the fundamentals of well, and in your work, this is like, there's an interesting confluence to what you were just saying about reliability and your friend's concern about that and also working in financial services and security in that, you got a double load of the need for reliability and trustworthiness. Is that part of your concern about this? Are you concerned about the trustworthiness of the experiences you're creating? Elizabeth: Yeah, absolutely. And you just reminded me, I am a bit of a risk nerd.

Maaike Groenewege Maaike Groenewege began her content career in technical communication. She is now a leading voice in conversation design for AI. Maaike draws on her technical writing background in her conversational AI practice, having observed that whether you're writing for humans or designing prompts for LLMs, you have to truly understand your audience and consistently provide clear and specific instructions. We talked about: her work over the past couple of years as a prompt designer how the instruction design principles from her days in technical writing and technical communication prepared her for her current role how her early exposure to help desk duties prepared her for the many question-answering responsibilities in her current role how her writing skills, her critical approach to generative AI, and her love of technology combine to give her a unique perspective on conversational gen AI content how retrieval-augmented generation drawing on high-quality content datasets can help set a base level of knowledge for LLMs her opinion that conversational chatbots are a transitory stage on the way to transactional chatbots that can provide self-service problem-solving the workflow for incorporating retrieval-augmented generation into LLMs the similar meaning of the concept of "chunking" in technical communication and LLMs the differences between how LLMs process language and how humans read - and the implications of this for prompt design and engineering the emerging structure for prompts: assigning a role, describing the task, providing a context the differences between conversational prompting, prompt design, and prompt engineering how she works with her engineering partners the difference between the logical inference that knowledge graphs do and the statistical inference that LLMs use how she keeps up with the rapidly changing developments in her field her invention: ALIs, application language interfaces how she uses ChatGPT in voice mode to capture and summarize her thoughts when she's out for a walk her prediction that "the future is bright for those who know how to write" Maaike's bio Maaike Groenewege is a conversation design lead, linguist and prompt designer with her boutique consultancy firm Convocat BV. She coaches both starting and more experienced conversational teams in optimising their conversation design practise, NLU analyses and team communication. Her main focus right now is on how LLMs can benefit enterprise conversational AI. Maaike is the founder of www.convo.club, an online community for more than 700 conversation designers. Connect with Maaike online Convoclub LinkedIn Connect with Maaike at these events European Chatbot and Conversational AI Summit, Edinburgh, March 12-14, 2024 UX Copenhagen, March 20-21, 2024 Unparsed Conference London, June 17-19, 2024 Video Here’s the video version of our conversation: https://youtu.be/3qxxb18BqFM Podcast intro transcript This is the Content and AI podcast, episode number 20. A false dichotomy has arisen in the AI world between conversational prompting in chatbot interfaces and prompt engineering under the hood. Maaike Groenewege works in the middle ground, in a role she calls "prompt design." She also draws on practices from her background in technical communication, after observing that whether you're writing for humans or designing prompts for LLMs, you have to truly understand your audience and always provide clear and specific instructions. Interview transcript Larry: Hey, everyone. Welcome to episode number 20 of the Content + AI podcast. I am super delighted today to welcome to the show Maaike Groenewege. Maaike is ... Well, she's a principal at Convocat, her company, and she's an actual genuine, prompt engineer. So, Maaike, welcome. Tell the folks more about what it's like being a prompt engineer at Convocat. Maaike: Thank you so much for having me, Larry, and it's such a pleasure to be here with you. Yes, I guess that I can say that for the last two years, I've been working as a prompt engineer, or perhaps rather a prompt designer. When I tell people that, they're all going like, "Oh, that must be really sexy," and, "It's the job of the future," whereas, in reality, I basically write instructions for large language models. Maaike: I guess I wouldn't really associate it with being sexy because most of this is very much getting your feet in the dirt kind of work, Excel sheets, lots of analysis, lots of document analysis and content analysis. I guess it's basically a job for ... Well, can I call them language nerds like you and me? Maaike: So, yeah, right now I'm working for a large Dutch publisher. I help them finding out what kind of work we can automate by prompting. It's really interesting. But I've also worked in situations like hyper-automation, where the prompts are not the prompts that you write in ChatGPT, but they are part of a larger workflow. For instance, a workflow where you receive an email, you want to have a first suggestion for an answer, you generate that text, you put it in an email again, or perhaps in a phone call. So it's not really visible, but it's definitely there in the background. Larry: Oh, interesting. Yeah. Well, you've done so much. I guess, first, let me ... Can you do a quick job description for what a prompt engineer does? Maaike: Absolutely. Larry: You just outlined the main duties, like... Maaike: Yeah, yeah. Larry: But what does that look like day-to-day? What's your new job? Maaike: My new job ... And it's so funny because it's actually my old job. I feel I'm back to technical writing and technical communication again, because in order to write a good prompt for a machine, I actually apply the same principles that I use for prompting humans, instruction design. So basically ... Maaike: Let's take a situation, like a real-life situation. This is not from my actual client, but it was an assignment I once got. It came from a developer. He's quite well-known in the space. He's like, "Maaike, listen. I need to prompt a newsletter or a one-pager for three different audiences, and it should be based on two or three articles from the news, from actual news, about LLMs and machine learning." He's like, "Well, I prompted it and I must say that the output is meh." I looked at this prompt, it was literally like, "Hey, generate a newsletter." Maaike: I'm like, well, when I would tell a junior editor to create a newsletter, I would give him more instructions. I would tell him something about my target audience. Who is it for? What are the information needs? What is their level of expertise? Because technical writing 101, don't write for your own level of understanding, but make sure you understand your target audience. Maaike: So it was so funny because prompt engineering is positioned as a rather technical job sometimes or a very marketing job. But my job is right in the middle. So what I do is I do a lot of domain analysis. I need to know who I'm working for, and in order to determine the information need of my target audience, I, of course, need to know a little bit of what they're doing. Maaike: I do target audience analysis, user task analysis, all kinds of stuff. Then when I write my prompt, I do find I write it in the form of a traditional instruction, and that, of course, is one. I've been doing that for 25 years. So it's almost like being full circle back into the place I never left in the first place, because even as a conversation designer, I still feel very much ... At heart, I'm always a technical writer because I always support users in completing their tasks successfully or answering questions, solving problems, and these are just new incarnations, I guess, of that job. Larry: As you talk about your background and where you are now, I'm reminded that everybody in content design, conversation design, technical writing, I mean technical writing seems to be like that's the most straightforward one. People often study that in college and then go actually do it. But everybody else, the conversation designers and content designers and UX writers I know, they all come from some crazy amalgam of careers and backgrounds. In your case, it just seems like the perfect storm of that technical writing plus conversation design. Larry: I guess tell me, because, well, you first came to my attention a few years ago as a really prominent and well-known and well-regarded and super-helpful content and conversation designer, and these new agents we're working with, the chatbots and GPTs all that stuff, they're all conversationally based things. Does my idea that you're perfectly positioned for that, does that resonate? Did you feel like this has just come really naturally, or- Maaike: Yes, and it's interesting because when I started as a technical writer, I wasn't even aware that that was a formal job. It was in the early 2000s. So little trip down memory lane, it was the time of a news group called Tech Role where people like me gathered. I was what we call the lone technical writer at the company. So I basically invented my own job. Maaike: I found that I just got really fascinated, especially by how people ask their questions. I also spent some time at the help desk at the same company where I started as a tech writer. The way people ask questions, it can be so completely different from what you think they want to do, and that's what started to fascinate me throughout my career. Maaike: With conversation design, 20 years later, I felt like I had won the lottery because, for the first time, we got our user questions handed to us on a silver plate, because especially if you create a chatbot with natural language understanding, you get the literal questions from your users. This, of course, as a technical writer. Maaike: Well,

Rebecca Evanhoe Rebecca Evanhoe practices, teaches, and writes about conversation design, a key UX practice that is taking on fresh importance in the age of chat-based AI applications. Since the publication of her book Conversations with Things (co-authored with Diana Deibel) three years ago, the tech and media worlds have fundamentally transformed, but the conversation-design principles that she teaches remain as relevant as ever. We talked about: the conversation design and UX writing courses she teaches reflections on the book she co-wrote several years ago, "Conversations with Things" and the changes in the conversation-design world since how the focus on principles in a framewwork set out in their book that helps designers decide on whether or not and how to ascribe personality to a chat agent her identification as a UX designer how she's incorporating LLMs into her course curricula her take on the misappropriation of the term "prompt" in new practices called "prompting" and "prompt engineering" and their divergence from traditional use in the conversation design field the differences in the conversation designer role in the LLM world compared with NLP the linguistic concept of "conversation repair" and how it manifests in "bot land" how to adjust confidence level in conversation design how intent classification in NLU works her preference for humans and human conversation the importance of including people with a humanities background in conversation design the ongoing importance of humans in the content and conversation design process for our ability to think strategically about how to maximize the success of conversational technology Rebecca's bio Rebecca Evanhoe is an author, teacher, and conversation designer. With degrees in chemistry and fiction writing, she's passionate about how interdisciplinary thinking can combine arts, humanities, sciences, and tech. She teaches conversational UX design as a visiting assistant professor at Pratt Institute, and co-authored Conversation with Things: UX Design for Chat and Voice (Rosenfeld Media, 2021). Connect with Rebecca online LinkedIn Video Here’s the video version of our conversation: https://youtu.be/xJkB03uH8ek Podcast intro transcript This is the Content and AI podcast, episode number 19. We're all talking to computers a lot more these days - telling Alexa to set a timer, asking Midjourney to create an image for a party invitation, or prompting ChatGPT to draft an outline for a slide deck. Rebecca Evanhoe is an expert on the interaction design practices that guide these conversations. Three years ago, her book "Conversations with Things" set out a principles-based approach to conversation design that remains super-relevant in the age of large language models. Interview transcript Larry: Hi everyone. Welcome to episode number 19 of the Content and AI podcast. I am really happy today to welcome to the show Rebecca Evanhoe. Rebecca is really well known in the conversation design world. She's a conversation designer. She's the co-author of the really excellent book Conversations with Things that came out a few years ago, and she teaches conversation design and other kinds of design work at Pratt University in New York. So welcome to the show, Rebecca, tell the folks a little bit more about what you're up to these days. Rebecca: Yeah, hi Larry, it's nice to be back. Yeah, these days I am teaching, I think you said conversation design, and specifically this semester I'm teaching a class in UX writing, which I love because it doesn't matter what kind of writing I'm teaching, it's like a chance to think about language and celebrate how cool language is with my students. And yeah, I've been teaching, I am doing some work at a cool place that I won't get into here. But yeah, it's been a really interesting couple of years. Larry: Yeah, because we last talked right before your book came out, I think it was maybe a few months before the book came out. And since then, I mean conversation had been a thing. I had talked to Phillip Hunter and several other content designers before I had you and Diana on the show, but it seems like I'm going to guess that more has happened in the last four years than in the four years before you wrote the book. Is that accurate? Rebecca: I think that's definitely accurate. Yeah, our book came out in April of 2021, and I think that ChatGPT became publicly available in November of 2022. So our book has been amazingly well received, tons of enthusiasm. It really seems to be sticking around and people are finding it useful. But if you control F and search our book, there is not one mention of the term large language model. And I think there's only one mention of natural language generation. Rebecca: It's been interesting to look at our book through the lens of the fact that technology keeps changing. And I think, and other readers think as well that it's based enough in principles that it really applies to any conversational technology, or at least that's the hope. And when I think back about the things that have happened in the last few years, when the book came out, I remember people kind of wanting us to put a couple things in the book that we didn't. Rebecca: People really wanted more information about how you build an Alexa Skill or a Google Action. Those were very visible at the time. People also wanted us to put a list of prototyping tools for conversations into the book. And we didn't, and I think things like that future-proofed it a little bit, because Alexa Skills and Google Actions... Like Google Actions aren't around anymore, Alexa Skills are very much de-emphasized. And a lot of the prototyping tools that we had a few years ago were acquired or were kind of sunset. So yeah, I think we made some lucky decisions to future-proof it. But certainly it doesn't have a mention of LLMs, which is- Larry: Well that's really... I got to say, this is super interesting because I remember from, we talked on the Content Strategy Insights podcast about this, that you and Diana both emphasized principles. And I don't know that you specifically stated that, but in retrospect it's like, yeah, that's way better than focusing on any specific technology or practice. Can you talk... I remember you covered those really well in the book. But is it possible to do a quick overview of some of the guiding principles? And maybe more to the point, how are they helping you through the arrival of LLMs and generative pre-trained transformers and all that stuff? Rebecca: Absolutely. I think that one of the concepts from the book that has become even more important today is the idea... And in our book we call it level of personification, and it's in the personality chapter. So I think a lot of people are thinking more about personality design, but also specifically how much of a character, how much of a mind the AI is sort of presenting itself as. Rebecca: So is it presenting itself as a fully realized character that's your friend and it refers to itself as I, or is it behaving more like a machine? So the example that I always give is if you have a remote control where your voice is the input, it doesn't need to be named Sandy, and it loves, Thanksgiving is its favorite holiday, and... It doesn't need to be a character in mind. So thinking through that spectrum really for any AI experience you're creating, I think is really important. How much of a person should it present itself as? I think that becomes a lot more visible. Rebecca: And an example that I would give for the LLM world, it's like, if you talk to chat GPT or Claude, those bots use I. And you can ask them a little bit about themselves and they'll tell you, they'll generally clarify like, oh, I'm an AI so I don't have feelings, but I can describe feelings or talk to you about feelings, stuff like that. But then there are other LLMs that they don't have any personification at all. Rebecca: So for example, Perplexity AI is a platform that is an LLM and you can talk to it and it does all the LLM-ey stuff, meaning you could ask it to summarize, you can ask it to give you bulleted lists, you can ask it to imitate a turn-taking conversation with you, but it doesn't really present itself as a character at all. And I think those kinds of decisions are still very much ones that conversation designers should be involved in, because that level of personification really impact user expectations, how they're going to behave toward it, and then how successful those interactions are going to be. Larry: Yeah, that's really interesting. How do you make that decision? Because I can picture making the wrong decision for good reasons. Oh, we like our customers, we want to be close to them, so we're going to act like their friend, where it would probably be more appropriate in a business setting to not be that way. Are there sort of guidelines around that, how you decide the kind of personality? Rebecca: Yeah, I mean in our book there's sort of a framework that walks through a lot of the facets of it. But generally I would say I think people over-personify these interactions. They think that having a character must be more interesting and fun, and they forget that people want their thing fixed, they want their task completed, they want their problem solved. And people also forget that lots of people are very happily solving these problems already through an app, through a website. People do like to solve their own problems, and conversations are not necessarily easier and more efficient unless they're designed to be so. Rebecca: So yeah, I think one of the things that we think through in the framework is first defining interaction goals that are independent of the conversation. So an example that I always use is, if you're making a voice bot that takes orders for a drive-through,

Andy Crestodina Andy Crestodina has been developing high-quality content for his business customers at Orbit Media for more than 20 years. As they have incorporated AI into their workflows at the agency, Andy has discovered that the best use of these new tools is to improve the quality of their content and service offerings rather than simply doing more. We talked about: his work as co-founder and CMO at Orbit Media how he uses AI to do audience research, develop personas, and address their needs through gap analysis his playbook for querying and validating information that AI generates for him: prompt, response, edit how they manage prompts at Orbit Media how their business operations are evolving to incorporate AI practices into their operations how he uses AI in his marketing analytics how the comprehensiveness of coverage that AI brings to his content helps with conversion the essential skills that persuasion copywriters need to develop to work effectively with AI his concern that some LLMs may be getting worse, not better the importance of setting aside a focus on how to be faster and instead focus on how to be better - to focus on quality over quantity of content Andy's bio Andy Crestodina is the co-founder and Chief Marketing Officer of Orbit Media, an award-winning 50-person digital agency in Chicago. Over the past 23 years, Andy has provided digital marketing advice to 1000+ businesses. Andy has written 500+ articles on content strategy, search engine optimization, visitor psychology, analytics and most recently, AI. These articles reach more than three million readers each year. He’s also the author of Content Chemistry: The Illustrated Handbook for Content Marketing. Andy gives up to 100 webinars and presentations per year and is a frequent repeat speaker at many of the top national marketing conferences. Connect with Andy online LinkedIn Orbit Media YouTube Video Here’s the video version of our conversation: https://youtu.be/yNZiusTV5kY Podcast intro transcript This is the Content and AI podcast, episode number 18. Creating content that gets found by Google and then persuades potential customers to act is a core competency for modern marketers. Andy Crestodina and his colleagues at Orbit Media, the agency he co-founded 20 years ago, have built websites for hundreds of businesses and created content for them that helps turn their prospects into customers. Andy uses AI extensively in his work now. His top finding? Focus on how AI can improve the quality of your work, not just your productivity. Interview transcript Larry: Hey everyone. Welcome to episode number 18 of the Content and AI podcast. I am really happy today to welcome to the show Andy Crestodina. Andy is the co-founder and CMO at Orbit Media. It's a Chicago based agency that does website development and a lot of other marketing stuff. So welcome, Andy. Tell the folks a little bit more about what you're doing these days. Andy: Sure, Larry. Well, thanks for having me. 20 however many years ago, 2001, co-founded an agency. An agency totally focused on the website itself. So we build sites and we improve them forever after doing optimization work, both search and conversion optimization. It's a 55-person firm that we grew strictly from organic and content marketing. So I'm someone that you might see if you go to an event like Content Marketing World, Social Media Marketing World, MozCon. I speak at a lot of search events and analytics events, but I'm an old-school content marketer who's built an agency focused on websites. Larry: Nice. Yeah. We must have run into each other somewhere along the line because I ran in that world a lot back around that same time, the early 2000s. But one of the things that's so interesting ... So we've seen this evolution together and seen a lot of the ... There's always been a lot of drudgery associated with our work and a lot of intellectual work involved and you're really excited about, and have experimented as much with, the new AI tools. Let's start with the customer and the audience analysis stuff that you do. That was really interesting to me when I read about that. Can you talk a little bit about that? Andy: Sure. If you write a prompt that says draft a blog post of 2,000 words that talks about supply chain ... You're going to get something pretty boring. It's going to be inherently undifferentiated. I joke that AI stands for average information. AI ate the internet. Literally the Common Crawl is 85% of the internet. We know that ChatGPT was trained on the Common Crawl. And it comes back and just gives you vanilla. It tastes like water. Of course it's boring. It's not for anybody. It's generic. All you did was say, write me a blog post. So all of my most successful adventures in AI, and these are daily, begin by teaching it or training it on your target audience. Now, if you've got battle tested, ideal client profiles or marketing personas, you can upload them and it will read them. Andy: You can also write prompts that will do it. Create the persona of a job title within an industry, at a company size, in a geography with an objective and a challenge and then tell me their hopes and dreams, tell me their pain points, their frustrations, their fears, and their decision criteria for selecting a company like mine. It's going to write you a persona. It will be incorrect. Of course, AI is not accurate. Don't ever expect the AI to be accurate. Go fix it and prove it, validate. And now once you've improved that persona and you believe in it and it looks good, now ask it to write an article for that persona or to draft an outline or to write a headline or to write a social post or to research a keyword or to suggest an influencer, or to do whatever it is you want to do. It's going to be far, far better results if you began that conversation with AI by teaching it who you're talking to. It's absurd for marketers to believe that they're going to get any good response without focusing on the audience. It's weird, right? I think it's weird. Larry: It is. And it's like that thing that anybody ... I'm sure you've done a lot of presenting over the years. You always analyze your audience and figure out what they want first. What you're saying reminds me that so much of the fuss the last year plus since ChatGPT-3 was introduced, has been about the generative capabilities of AI. We were talking a little bit before we went on the air and you were like, "Nah, it's not about efficiency and time saving. It's more about better stuff." And like you just said, it sounds like you're getting ... How do you feel about the personas that you're developing now compared to what you're doing before you had ChatGPT to query? Andy: Well, if I'm on a conversation with a client or a prospect or a friend and they say, "Hey, check out this thing. Is this good?" I've done digital strategy forever and I've been part of the planning process for more than a thousand projects and I'm an SEO and a conversion person and persuasion copywriting nerd. So people are frequently asking me to evaluate something they made. But it's really hard for me to quickly understand their audience. So instead, if I just begin with a persona prompt and together with the person I'm talking to, we get to where we believe, yeah, that is my buyer. Yeah, that looks like them. That feels right. Okay, good. Now I'm going to copy and paste in that thing that you wanted me to review and I'm going to have the AI tell me what it's missing. Andy: AI powered, persona driven gap analysis on any page on your website. It will immediately tell you you failed to meet your audience's information needs, or you did not address an important objection, or there's a critical unanswered question with your persona after reading this copy. Those are things it's very hard for a human brain to do. To look at something and say, what's not there. Human brains are simply not good at doing that. AI is amazing at doing that, but only if you train it on the audience first. At that point, now everyone has a sense for it. And do we agree? Do we just automatically assume it's correct? No. Here I joke, AI stands for another input. Earlier I jokingly said, AI stands for average information, which is what it does. If you just ask for general prompts, you're lazy prompting, it's going to give you back average information. Andy: But now I'm actually using it as a mini-consultant or research assistant or once I trained it on the persona, giving it a piece of copywriting and it's giving me another perspective. Do I have to take it? No. Is it useful? Maybe. But it was a fast exercise that put me in the mindset of my audience and it's got me looking at a key piece of content, a sales piece, a service page or a product page or a sales page. That exercise, sure, it is fast. I don't love it because it's fast. I love it because it's going to help me generate more leads. But that exercise is 15 minutes, less, and the improvement that you might make from that ... Dammit. I forgot to mention this important thing. Will be a durable improvement. Go fix your homepage and it'll be a better homepage for the next 10,000 visitors you'll have over the lifespan of that page. To me, this has been the most successful use of AI. Starts with the persona, give it a piece of copy, have it do gap analysis, and then just take it or leave it. But you have an opportunity now to do better, you could say, just conversion copywriting. Larry: Yeah. You're reminding me now that the common and ubiquitous modern affliction is attention when none of us have it anymore. We're incapable of paying attention for long periods of time. But these machines are just like, once they know what they're looking for, they're just on the job. They're not picking up their phone and looking at it. So their attention to detail, I get that.

Markus Edgar Hormess Markus Edgar Hormess offers this advice: "Never prompt alone." Markus was working with AI long before the current wave of excitement. He experimented with early versions of ChatGPT and quickly identified new opportunities to collaborate with both his human colleagues and his new AI coworkers. He's currently building a community - Teaming with AI - to study and share these new practices and to explore the future of teamwork in the age of AI. We talked about: his background in strategic prototyping and how he's applying it in his Teaming with AI initiative his first exploration of AI, in 1986 one his first applications of current AI tech, a use of ChatGPT-2 to accelerate service design prototyping activities his work and experimentation on ways to engage AI tools as collaborators on design teams how to consume research on AI, but also the importance of getting out in the field since research develops more slowly than professional craft his insight that you should "never prompt alone" so that you and your collaborators can eliminate bias and get better answers some of the opportunities that AI creates for real-time research and accelerated implementation of research insights how important it is "to put people in the center of this" the benefits for design practitioners of diving in and experimenting with AI tools, always with collaborators Markus's bio Markus Edgar Hormeß is a well-known consultant, practitioner and educator in the field of service design and design thinking. In his daily work, Markus helps organizations tackle complex business problems and make team cultures more agile and human-centered. The focal point of his work is strategic prototyping, where he constantly pushes the boundaries of what a dedicated team can achieve with limited resources. Markus is a strong believer that we should break down the perceived boundaries between technology, design and business – and that cheap experiments and prototypes are efficient tools to move your company, your strategy, your team, or your project forward. Based on this mindset, he has shaped multi-year programmes to help multinationals shift towards a more hands-on, pragmatic and effective approach to customer experience and innovation. Markus has a passion for good design, human technology, practical experiments, authentic services, and playfulness in all things. He is co-Founder of WorkPlayExperience, a service innovation consultancy which helps organizations worldwide change how their staff, partners, and customers work together – and – how they can strategically discover and create new products and services. His practice builds on his experience of service design and business consulting, and on his background in theoretical physics. In 2010, Markus co-initiated the world’s biggest service innovation event: the award-winning Global Service Jam. This was soon followed by the Global Sustainability Jam and the Global GovJam, and Markus has been a leading figure in establishing the culture of experimentation and prototyping which Jammers worldwide call “DoingNotTalking”. Markus co-wrote “This is Service Design Doing” and “This is Service Design Methods”, top-selling books which have become the standard reference books for many practitioners and academics. He teaches service design, innovation, and sustainability at various universities globally, and is adjunct professor for service design thinking at IE Business School in Madrid. In 2023 he co-initiated the Teaming with AI conference and community. His growing interest centers on how AI influences our approach to teamwork and collaboration, as well as the broader impacts on innovation and the development of strategies that are resilient in the face of future challenges.. Connect with Markus online LinkedIn Teaming with AI website Video Here’s the video version of our conversation: https://youtu.be/HlHhpsr2lW4 Podcast intro transcript This is the Content and AI podcast, episode number 17. As AI tools arrive in our workplaces, we're discovering that this isn't just another technology adoption cycle. The generative nature of tools like ChatGPT permits rapid iteration on ideas and quicker learning about their impact. For a prototyping strategist like Markus Edgar Hormess adding these AI agents to his service-design teams has been a boon, letting him and his colleagues collaborate and experiment in ways they couldn't have imagined just a few years ago. Interview transcript Larry: Hi, everyone. Welcome to episode number 17 of the Content + AI podcast. I am really happy today to welcome to the show Markus Edgar Hormess. I first met Markus a year ago at a service design workshop in Amsterdam, and we've been talking ever since about getting him on the show. So it's great to finally have you here, Markus. Larry: Markus, he's one of the co-authors of the book This is Service Design Doing. He's real active in the service design community and in that world he's really focused on strategic approach to prototyping, which is what we first wanted to talk about. And then AI came along. So we're on the Content + AI podcast. So anyhow, welcome, Markus. Tell the folks a little bit more about what you're up to these days. Markus: Hey, Larry. Thank you for having me. Yeah. So you mentioned it, so I'm super interested in strategic prototyping and prototyping in all kind of aspects. And when this whole wave of AI came about, we thought, "There is no books, there is no papers that tell you how to do this, so we need to prototype our way into this new world," and that's why we set up an initiative, which is called Teaming with AI, where we focus on the impact AI tools have on the way we collaborate in teams. So a small group of people that have a common goal, that trust each other, hopefully, and try to make something happen in the world. Might be nonprofit, for-profit, wherever you are. Markus: And so we set up a couple of events, a little Unconference early last year and one in the middle of the year. Then we started writing a white paper about this. This is about to be published soon, so hopefully we get some conversation about this. But all of this is really about giving a space, a play space for people that are interested to explore what is happening there. Only a few people actually focus on that team aspect. That's why we have a strong focus on that, because you know it, service design, what we always say, it's what is the key skill that you have to have in service design? That's facilitation. That's working with a group, whether you're part of that group or if you're facilitating a different group. And now one part of that group is AI and how does it change things? It changes it, and it doesn't change it in other parts, but certainly a lot of shift going about. Larry: Yeah. There's two things in there that are really interesting to me. One is that we're all still humans and we're going to be throughout this, whatever this AI thing turns out to be, but also the fact that, I feel like, you're living in the future a little bit, because when I met you a year ago, you were already deep into this and really exploring it. And now you're way into this collaborative paper and you've given it a lot of thought and you're going to be providing these materials that you just said didn't exist yet. So thank you for that. But tell me a little bit about when and how did you first get interested in AI? And how does it fit in specifically with your... Because you first came to my attention or you really stood out in that workshop as the prototyping guy. And so talk a little bit more about that. Yeah. Markus: Yeah, sure. I gave this a bit of thought, and then I remembered something, that back in 1986, I think I was in seventh or eighth year in school, I did a big presentation about the state of AI at the time. That was during one of these first waves, big promises in AI, "We're going to fix this by the end of the decade," and it never happened. But that was still when there was this kind of, "Oh, we can maybe do this." So this was time of programming languages like LISP and stuff. I think that was where I got curious. Then I forgot about it for a long time. And then just after I finished university, I started to work at the Bavarian Research Center for Knowledge-Based Systems, which basically was a spinoff of the chair of AI at the LMU University. But that was, again, during a time where we were in niche use cases. The machines weren't fast enough to do the big stuff that we can do today. But that's where I learned that, yeah, niche use cases can be useful and they still are to this day. Markus: And then fast-forward, me getting into service design and innovation. And three or four years ago, no, three years ago now, when GPT-2 came out, it was accompanied by a wave of tools that would allow you to come up with better marketing texts. And that's where we pick them up and use them in prototyping. Because in service design, if you design a new customer experience or service, how do you make this tangible, right? And one super simple way is to create a little advertisement for a new idea that doesn't exist yet. It's easy to test because people know the format. So it's a really good way to test the waters if people like that or value that way you're trying to sell. Markus: And using these tools, there's this little, "Oh, give me 10 variations of a Facebook advertisement or a Google ad." And then the teams would just use these tools within our workshops. We get these 10 and then curate the ones where it's, "Oh, yeah, that fits what we thought." And they could go faster, which is, I'm not obsessed by faster. There is a caveat there, but within the design process, being able to get something faster means you can iterate more, and that means you can learn more. So you can reflect on, "Oh, what does this do?

Dan Porder A few years ago, Dan Porder was teaching poetry to university students. Now he's at IKEA training large language models to generate useful, usable content for user experiences. He's picked up new skills along the way, like Python programming, but much of his work still relies on well-established content and design crafts like content strategy and inclusive design. We talked about: his role as a senior content designer at IKEA, where he focuses on AI some of his early experiments in composing and evaluating poetry his longstanding interest in AI and the development of his tech skills how content designers can leverage their skills to work in AI his perception that there is currently more opportunity than threat to content professionals in the AI world the make-up of the cross-functional teams he works with: data scientists, engineers, developers, content people, designers, subject matter experts how to brief and guide generative AI to get the outputs your users need how writing abilities prepare content designers to do prompt engineering the stack of data and technology that underlies AI and the orchestration mechanisms that connect them some of the tools he uses in his AI design practice the role of data in content design for generative AI the importance of staying aware of bias in training data and always wearing your inclusive design hat the role of explainability in AI ethics the importance of knowing how to ask data scientists and engineers questions that reveal as much as possible the inner workings of the "black box" in which AI content is generated his take on democratization opportunities that arise with the arrival of AI tech Dan's bio Dan Porder is a Senior Content Designer and Content Engineer at IKEA. His recent work focuses on the intersection of AI, structured knowledge, and experience design. Outside of work, he runs an international writing community. Connect with Dan online LinkedIn Video Here’s the video version of our conversation: https://youtu.be/VFXLG4h6ylE Podcast intro transcript This is the Content and AI podcast, episode number 16. AI is quickly changing the way content designers work. New content duties are emerging that require fresh skills, but at the same time traditional skills like content strategy are becoming more important. In his work as a content designer at IKEA, Dan Porder has developed new skills, like Python programming, and has applied the writing skills he perfected as a poetry teacher as well as the inclusive design practices he developed earlier in his content design career. Interview transcript Larry: Hey everyone. Welcome to episode number 16 of the Content and AI podcast. I'm really happy today to welcome to the show Dan Porder. Dan is a senior content designer at IKEA, where he's currently focusing on AI stuff, and his title is content designer, but he is really more of a content architect. So welcome to the show, Dan. Tell me a little bit more about your AI and content adventures. Dan: Hey Larry. Thanks for having me on. Yeah, maybe I could just start by giving a little bit of background. I think at heart, despite what I'm doing now, I think of myself as a writer, and that's been my life's focus since I was young. Writing poetry, writing fiction. I did my bachelor's in English literature and later did a masters, masters of fine arts, actually, in poetry. Some of it was more on a conceptual side, thinking of language as data. So there was some unusual experiments in the tech world even then for me. Using Google data to create poems. So imagining Google queries as a representation of the collective zeitgeist, and how can we leverage that data to create meaning in poetry? Or using NLP to find meaningful relationships in texts where you didn't know they were there. But all of that then led me into copywriting, so like brand copywriting, product copywriting, ads, copywriting as creative direction. Dan: And then eventually back to the Google data, so SEO copywriting and SEO strategy. And I focused for a while on optimization, research, data analysis for SEO, some technical SEO. And then, yeah, my recent journey has been more in the design world. Content design, content strategy, user experience design. And I'd always been interested in AI and the question was always, how do you do that as a job? Particularly from the position I was coming from as a former student and teacher of poetry and writing. Of course, when ChatGPT came out, like for many people, the connection became clear to me and I started incorporating it immediately into all my work. Dan: I realized that I also needed to brush up on my coding skills, and particularly get more invested in Python. And I took some courses specifically on generative AI and machine learning for that purpose, just to make sure I was prepared. But now I think I'm leaning more into the world of knowledge, thinking about the data that we need for AI. The data structures that create meaning for these systems to ingest or to retrieve or to do with what they need. And in the case of generative AI, this is content. This is a task that requires a content designer, content strategist. It's going to be primarily images, text, audio. So that's what I've been up to lately. And yeah, I'm excited to talk to you about it. Larry: Well, that's great. I got to say, it's hard to imagine anyone better prepared for this stuff, because to go from playing with Google and poetry stuff, the notion of vectorized word embeddings was just like, "Oh, cool, that's another way to do that." I can almost picture this evolution going pretty smoothly for you. But a lot of content people are not as technically curious as you are, or haven't had the same technical opportunities. And you have a lot of colleagues who are more like conventional content design kind of folks. Have you thought about how people who are less natively technically inclined can jump more into AI stuff? Dan: Yeah. I think it's about leaning on their expertise, especially abstracting that expertise. So for a content designer who maybe imagines themselves more as a UX writer or comes from a copywriting background, it's an understanding of information, of messaging, of what content works best for people in what scenarios. And that kind of knowledge, that's less of the craft side and more of the wisdom of content, is incredibly valuable to data scientists and to engineers working on AI. Dan: This is some of the expertise that's needed, is subject matter expertise, including on content. So generative AI consumes data and puts out data. That data is content. You need a content person to figure out what it will be, what the use case is, and what content you want these models to produce on the other end, either for a system or for an end user. So you're giving up a bit of control on the craft side, but on the strategic side you're actually, if you're willing to have those conversations with the technical people, you are asserting control in a way. Larry: Right. That's so interesting because, as you're saying that, I'm picturing... it's sort of like the way, a lot of these models, there's attempts to capture subject matter expertise and incorporating that in there. But you also need that subject matter expertise to train the models. To write the prompts, do all the other stuff as well. Can you talk a little bit about that relationship between... and this gets at people's concerns of AI replacing them, because if we capture all that subject matter expertise, then all of a sudden it's like, "Oh, we don't need content designers." I personally don't think that's coming, but what do you think about that idea? Dan: Yeah. People have talked a lot about this. I think some of the concerns are overblown. Of course there's a grain of truth in this. Theoretically, if we were to all give all of our best data, most of which is just in our minds as experts, so it doesn't exist in the right data format, but if we were and we were to train models that somehow are still usable and not unwieldy as a result of that, you would start to replace people. Dan: That's not what's going on right now. That's not the technical capabilities. Anyone who's using these tools or working with them can see that. And also just the actual process of properly curating the data and testing and iterating on methods of fine-tuning and reward functions, and getting the right feedback from the right experts. That's a lot of work. That's a lot of resources, even for small use cases. So I don't think that's the worry. I think it's more like an opportunity. This is an exciting opportunity to make your work more scalable faster. I think, especially from the content design perspective, also to be able to assert governance over content creation through the consistency of machines that doesn't necessarily exist in people always. Larry: Right. And what you just said, I realized that my question was sort of like I'm projecting the alarm that I feel in a lot of circles. But I think more often the answers are like what you just said. It's much more hopeful and optimistic in that, at every juncture, there's going to be more need for our expertise that will, probably not for the next couple of decades, anyway, be codified in machines. So that kind of leads me back to one of the things that wanted to talk about a little earlier, actually. It's just, done both conventional content design for regular, old digital products. And then now you're working more on the AI side. Can you talk a little bit about the evolution of the practice as you go from one realm to another? Dan: Yeah. Well, I think, as we were just talking about, one thing to notice is the importance of cross-functional teams. So having not just the tech people in there, the data scientists and engineers and developers, but also the content people,

Rebecca Nguyen In her work as a content designer at Indeed.com, Rebecca Nguyen is finding new opportunities to assume a leadership role on teams working with generative AI. Rebecca feels fortunate to work with teams that recognize the value of writing and design skills. She's also finding that generative AI is the perfect place for content design to take the lead. We talked about: her work as a senior UX content designer at Indeed and her recent shift to focus on product teams using generative AI how well-suited content designers are to AI products the unique challenges of working with non-deterministic large language models their process for designing prompts and how they evaluate them her learning curve around the loss of some language control that you get in conventional content design the main differences between prompt engineering (the how) and content design (the what) her ability as a content designer to lead more in the AI space than in prior design roles how they balance the use of outsourced LLM solutions like OpenAI versus developing their own models the lack of genuine intelligence in LLMs how her fear and concern about AI is eased the more she works in the LLM world how the evaluation component of designing content for AI creates more work for content folks one of the main benefits of LLMs - their ability to take on tedious rote content work the child-like nature of LLMs the surprising liberating effects of simply not worrying about whether or not you have a seat at the proverbial table Rebecca's bio Rebecca Nguyen (she/her/hers) is a Senior UX Content Designer at Indeed. She’s been part of marketing, UX, and product design teams at Bankrate, Northwestern Mutual, and LPL Financial, where she established the content strategy practice. A Confab speaker and workshop instructor, Rebecca is also an award-winning memoirist. Connect with Rebecca online LinkedIn RebeccaAnneNguyen.com Video Here’s the video version of our conversation: https://youtu.be/8WnxlXXKxeY Podcast intro transcript This is the Content and AI podcast, episode number 15. Just as content design was emerging as its own craft and profession, along came generative AI. At first it looked like ChatGPT and large language models might displace content designers (unfortunately, it appears from recent layoffs that some executives may still think this is the case), but at Indeed.com, Rebecca Nguyen has found that working with LLMs has given her more work, not less, and that her content design efforts are now more interesting, rewarding, and impactful. Interview transcript Larry: Hi everyone. Welcome to episode number 15 of the Content + AI podcast. I'm really happy today to welcome to the show Rebecca Nguygen. Rebecca is a senior UX content designer at Indeed. Welcome, Rebecca. Tell the folks a little bit more about what you do at Indeed. Rebecca: Hey, thank you so much, Larry. Great to be here. Yeah, I'm a senior UX content designer at Indeed. I've been there for a couple of years now, going on two years, and I work on product teams to make sure their content is useful and useful and accessible and inclusive and all those goodies that we're used to. And in the past six months or so, my role has really shifted and I've been almost exclusively focused on working with product teams who are using generative AI in their products. Larry: And that's why I wanted to have you on the show is we talked about this a while back. And that's one way to think... One way I think about that is all of a sudden we have new collaborators in two senses. One, we have these new, we're talking to machines in our work because they're generating some of the language we work with, but there's also a lot of other new collaborators. Tell me a little bit about how the people around you have changed over the last six months. Rebecca: Yeah, that's such a great point. So we're probably, if we're working in product content, we're used to working with product managers, we're used to working with UX designers, engineers. And that has shifted in that the team that I am partnering with now is made up of engineers and product managers, but we're also working really, really closely with data scientists and we do not have a UX designer or UX researcher on the team right now. So UX content design is really the entire voice of UX in this group, which is really cool. Larry: That's really interesting because often we're the last one in. How does that feel going in there as a sole UX person? Rebecca: It's exciting. It's been a little bit intimidating, but I haven't found myself feeling completely lost or anything. I think it's been great. As we were chatting earlier and you said we're really... We're creating a content product when we're working with these language models. The output is text and language, and so who better could be suited to drive and design the language when working with one of these models? It's been a really natural fit. And then the activities and tasks and approach has been different from anything I've done before, but it's well-suited to a content designer skills, I would say. Larry: Well, that's it. So what has that transition been like? You said the activities and the tasks differ. It sounds like it kind of rhymes with your old conventional product work, but how is it different now? Rebecca: Like that. Yeah, I sort of talk about it as if we think of a sandwich and in that content creation moment, that's the meat. That's sort of like when we're going through a design thinking process, we're doing some discovery or research or we're deciding on the problem that we want to solve, and then we get to that moment where we make the thing, we design the thing and we might be writing words. And after that we are iterating and getting feedback and seeing how it performs and measuring and iterating more, et cetera. Rebecca: The difference for me with generative AI has been spreading my focus out and becoming more of the bread. So instead of the meat, that creation moment, when you're working with a language model, the model takes on that task. They're the ones creating the content. And your focus as a human is all of that stuff on the periphery of that, so the prepping, which we would be sort of the prompt engineering and design where we're telling the model what we want it to do, and then the evaluation piece where we're looking at what the model did and saying, "Okay, was it successful? Did it follow directions? Could we do it better?" Rebecca: So it's almost like you become a teacher of content design instead of a content designer where you're actually making the thing yourself. Larry: Interesting. I have not heard it articulated that way, but that makes perfect sense because... Well, they're called learning models and you're the teacher. That's great. And you mentioned both prompts and one of the things you just said made me think that people always talk about prompt engineering, and you talked about engineering and designing prompts. Do you go into prompt creation with a designer hat on because you're working with engineers? Do you think more as a designer in that world? Rebecca: Yeah, I definitely think so. Particularly as a content designer, thinking about how does the language inside the prompt impact the output and to make sure that content design considerations are represented in the prompt as much as possible to make sure that we're getting the output where we want it to be. We're sort of preemptively correcting mistakes or anticipating mistakes that could happen. Rebecca: For example, when you get familiar with a model like ChatGPT, you can see, and we all can see as content designers sort of that out-of-the-box tone that the model assumes, the model that's been trained on the internet. So it's a very casual tone. It is, in my opinion, it's overly friendly in a way that can be kind of annoying. There's lots of exclamation points, there's a lot of celebration for small things that may not require such celebration. It helped you with a task and it's like, "You're so welcome. Awesome." And you're like, "Calm down." Rebecca: That tone and that voice isn't always appropriate for a product. And so when you're getting in there and designing prompts, you have this opportunity to modify as best you can. And the cool thing about prompt engineering is that you can do a lot of playing around and you can see how different instructions impact the outputs and then tweak and adjust from there. But that was surprising to me because I think that on my team and at my organization, at first we were sort of thinking about this on the other end, once we see the output, then let's evaluate it and give feedback. But the problem is that once the output has happened, it's too late. It's not like working with a human where you can revise it and create this static thing. It's always going to be different every time. It's that non-deterministic nature of a large language model. And so really we want to get in at the prompt stage to try and drive and direct before the output happens. Larry: That's so interesting. But you're still getting some feedback from it, too. You mentioned earlier how one of the pieces of bread is about iteration in your sandwich. And then, as you're talking there, I'm also reminded back when you said that you don't have UX researchers on the team. Are there more automated ways of getting feedback? Like for you, because you're still looking at it after the fact to see compliance with... Not compliance, but sort of alignment with voice and tone and that kind of thing. I guess tell me a little bit about that loop. Rebecca: Yes. So we have content design at the beginning, which would be the... Actually, even before we do prompt design,

May Habib May Habib is the CEO at Writer.com, a generative-AI platform that has been helping enterprises use AI since 2020. Her company builds its own award-winning large language models and is pioneering approaches like "headless AI" to help employees across an enterprise use AI to be more creative and productive. We talked about: her work as CEO at Writer.com, a "full-stack generative-AI platform," for the past four years her decade-long work in the AI and NLP space, beginning with translation solutions her take on the "over-chat-ification" of AI products, the reliance on chat interfaces as opposed to other ways to access AI capabilities her prediction that 2024 will the "get real" year for AI the use of fine-tuning and/or RAG to connect learning models the inadequacies of vector databases for knowledge retrieval and their exploration of knowledge graphs to fill the gap a new role, the "AI ontologist" another new role, the "AI program director" which includes a mix of left- and right-brain thinking and technical skills some of the use cases for "headless" AI their approach to securing and protecting the various kinds of data used in their LLM how she sees the role of data scientists in AI their tactical approach to building knowledge graphs for specific business use cases their work at Writer on no-code and low-code tooling to help their customers build solutions and tooling on the platform new content job roles that are emerging as AI takes hold in enterprises May's bio May Habib is CEO and co-founder of Writer, the only fully-integrated generative AI platform built for enterprises. Leading companies, including Vanguard, Intuit, L’Oreal, Accenture, Spotify, Uber, and more, choose Writer to help them deploy generative AI across their businesses, allowing them to automate and augment key operational activities and increase employee creativity and productivity. Writer’s family of large language models (LLMs) are state-of-the-art, topping leaderboards for natural language understanding and generation. The company’s security-first approach means that Writer’s large language models and generative AI platform are deployed inside an enterprise’s own computing infrastructure. Launched in 2020, Writer has seen immense success with customer adoption, has grown revenues by 10x in the last two years, and has over 150% net revenue retention. May and the Writer team have successfully raised over $126M in funding from notable investors, including ICONIQ Growth, Balderton Capital, and Insight Partners. May began her entrepreneurial journey as a teenager, and founded her first language startup, Qordoba, a localization software company, 10 years ago. May is an expert in AI-driven language generation, AI-related organizational change, and the evolving ways we use language online. She has been recognized for many different awards, including the recent 2023 Forbes AI 50 and Inc.'s 2023 Female Founder Award. She is a MELI Fellow with the Aspen Institute. She graduated from Harvard University and spends her time between San Francisco, where Writer is based, and London, where her two children live. Connect with May online LinkedIn email may at writer dot com Video Here’s the video version of our conversation: https://youtu.be/lFTfA4X8CkA Podcast intro transcript This is the Content and AI podcast, episode number 14. Over the past year and a half, innovative artificial intelligence startups have taken the tech and content worlds by storm. In her position as the CEO of the generative AI platfom Writer.com, May Habib has been right in the middle of the excitement, and out in front of it. Writer and their clients were deploying LLM-driven generative AI programs inside of large enterprises long before OpenAI's ChatGPT 3 captured the headlines and launched the current wave of AI disruption. Interview transcript Larry: Hey everyone. Welcome to episode number 14 of the Content and AI podcast. I am really delighted today to welcome to the show Me Habib. May is the CEO and co-founder at Writer, an app many of you're familiar with. They're just having a great year and I was excited to get her on the show towards the end of 2023 to talk about topping the MMLU leaderboard with the Palmyra, their LLM, closed the nice funding round. Sounds like things are going well at Writer, May. May: Oh, thanks Larry. It's so nice to come back and chat with you. Yeah, we've had a great year, thank goodness. I've got our last all-hands of the year after this conversation, and so it was definitely nice to look back. We do these weekly updates to the whole company. I write them, and I went back and looked at week one and compared it to week 52, and then one's like, "Oh, let's go back a little further." I went 2022, week 52, and then 2021, week 52, and yeah, it's awesome to see things build and all the progress. Larry: Yeah. Well and one thing, and you've been part of that progress. ChatGPT, which is where the current kerfuffle is all about, that's barely a year old, but Writer's older, and Cordova was even older than that, right? May: Yeah, well we've been in the NLP space for a decade, me and Waseem, and starting in machine translation. I think we were able to come to the world of transformers with maybe two distinct advantages, I think, over the folks that are in the space now, OpenAI and others as well included in that. May: One is, we were very much less a technology and search for a problem. Because we saw so many content challenges in the enterprise that could be solved with AI, having come from translation. So, it allowed us to really take a solution-based, outcome-based approach to thinking about how to productize this cool technology versus not. May: Now, obviously a general-purpose AI-based chat has captured the imagination, and has been an incredible thing that the OpenAI team has introduced that we obviously didn't think of, but in a lot of ways what it's done is open up what people thought could be possible with AI, and it's made room for solutions like ours to really explode, because we really serve that enterprise need, that very solution-specific application that is enterprise-ready, is secure. So anyway, it has been a fun road and a really fun four years with Writer. Larry: One of the implications, and you know as much about this stuff as anybody, in fact you're right up there with OpenAI in terms of your accomplishments and the power, the service you offer. I'm curious, what are you like... And I think there's a couple of things in this question and that I'm hoping to get out of this conversation. One is just the general state of the AI market. A lot of what you just said, I think it's going to help people ground themselves and feel it. But I think one of my questions is, for example, is this just another SaaS app that the software in the background is an LLM, or will there be fundamentally different things you think that content folks have to consider as they go into both working with these tools and working on these tools? May: Yeah, I think maybe taking that question a couple of ways. One, the user-experience cut of the market, and then the where-are-dollars-being-spent cut of the market. I think it'll allow you to see the gap that we see and that we feel, actually, looking at it in these two ways. I think from an end-user perspective, that cut of the market, there is this over-chatification of what AI can do, and everything is a fricking dialogue to get stuff out of AI, and it's just so early, and the interfaces obviously shouldn't all be chat UIs, but that's kind of the case right now. Whether somebody gave you a Copilot license or you're personally paying for ChatGPT Enterprise, I think most people aren't getting the value they thought they would, given all of the headlines. May: That adoption gap isn't because the capabilities aren't there because we are building the capabilities. They are fricking crazy magical, and I think when we chatted last, I said probably something along the lines, if this was 18 months ago, I probably said, "Larry generative AI is like giving everybody an assistant and a chief of staff." I mean, that's not what it's like anymore. It's giving you the best version of yourself, 20-years expert into the future. There is so much, even in 18 months, so much that the models can do. May: Anyway, all to say that the end-user experience cut of the market is super under-optimized and today, despite all of the hubbub, I can't go into my sales force and say, "I'm in London in January, who should I see of our deals that are closing in Q1?" So even the AI that's supposed to get built into all of our systems of record, isn't really doing the things that we want it to do. Folks who are trying to connect Copilot to their Microsoft data aren't seeing the kind of answers they would like. And I think power users who have figured out how to get a lot of value from ChatGPT are, but your median user really isn't. So, that's that cut of the market. May: In terms of where there are real dollars being spent here, I think the enterprise is probably over-investing in the infrastructure and the utility model layer, and are trying to rebuild from scratch every use case, and there are a lot of things that are breaking about that experience. And the total cost of ownership, I think, isn't making sense for a lot of companies. The accuracy and business impact of some of these pilots and POCs isn't materializing. May: So, next year is going to be get-real year, which is exciting. I think we'll see a lot more exciting end-user interfaces and experience that build on the toy making and piloting of tools this year. And then I think enterprises are going to be really looking for just more comprehensive solutions to filling generative AI needs internally. Larry: Yeah, a couple of follow-ups. First thing,

Laura Costantino Laura Costantino is watching the emergence of AI in content professions from two interesting and valuable perspectives: as a content designer working on LLMs at Google and as an active participant in the social-media communities where content professionals gathers. In their work at Google, they have returned to their roots as a content strategist to manage the challenges that come with designing content at a massive scale. Through their interactions in the community, they have had the chance to hear the concerns of content designers who are navigating the new world of AI - and to inspire them with advice and success stories. We talked about: their work at Google as a senior content designer training LLMs how their content strategy background is helping in their current work the difference in working with content at a huge scale, as is required in their work with large language models how their work is operationalized in the ever-changing workflows at Google the community of knowledge sharing that has arisen organically among a variety of content crafts at Google their advice on how to cope with the rapid pace of change in the world of AI how they works with data scientists, machine-learning engineers, and other AI collaborators their cautiously optimistic view of future of the content-design profession their advice to content designers for taking a proactive and curious approach to new AI technologies and practices Laura's bio Laura Costantino (they/them) is a senior content designer and strategist working on AI and large language models (LLMs) at Google. For the past ten years, they have worked at the intersection of UX, content, and marketing for some of the world's largest tech companies. Laura developed a passion for storytelling early on and received a MA in Cinema Studies in San Francisco, where they worked as a curator for a range of film festivals and cultural institutions around the Bay Area. Outside of work, Laura is committed to mentoring people transitioning into UX and tech, advocating for content, and sharing advice on LinkedIn. They currently live in NYC, were born in Southern Italy, and speak both English and Italian fluently. Connect with Laura online LinkedIn Video Here’s the video version of our conversation: https://youtu.be/EdgyXGC3xlI Podcast intro transcript This is the Content and AI podcast, episode number 12. The arrival of large language models and chatbots like OpenAI's ChatGPT, Anthropic's Claude, and Google's Bard is creating both existential concerns and new opportunities for content professionals. In their work as a content designer at Google and through their extensive professional networking, Laura Costantino has the chance to witness the full range of work experiences and personal emotions that come with the rapid adoption of new artificial intelligence practices. Interview transcript Larry: Hi everyone. Welcome to episode number 12 of the Content and AI podcast. I'm really delighted today to welcome to the show Laura Costantino. Laura is a Senior Content Designer at Google, doing really interesting work around AI and content stuff. So welcome Laura. Tell the folks a little bit about your role there at Google? Laura: Hi, Larry. Thanks for having me. It's so nice to be here. Yeah, so I've been at Google for about a year and a half, but somewhat recently, maybe three and a half, four months ago, I moved from my previous team to my current team, and I am at the moment working as a senior content designer, training large language models. So that's my new job. Larry: Well, training large language models at one of the biggest tech companies in the world, that's pretty interesting, especially for folks in the content world. There's so much to ask about that. I guess the first thing I'd ask is what's the biggest change? What's the biggest difference in training a language model versus the content design work you were doing a year ago? Laura: Yeah, that's a great question. I came up to content design through content strategy and to an extent marketing as well. And so I think for me, it really helped to have that content strategy background, meaning really being familiar with content at scale, content governance. And I think that's been the biggest difference for me, that in my current role, I had to go back to my past and brush up on some of those skills that I think I learned more in the past, versus in my most recent roles as a content designer. I think my day-to-day was still a little bit more writing strings and felt a little bit more like bespoke and... I don't want to say in the moment because of course, ideally it wouldn't be in the moment, but unfortunately sometimes it is in the moment when someone asks you to write a string or edit a string, versus right now I do think my role, it's a lot more focused on the strategy at scale, and I do think it's a function of the role more than say, me growing in my career or something. Larry: That's so interesting because when you think about it, because most content design roles, like you just said, you're embedded in a specific product working on just strings and error messages, but also the narrative of the whole product and all that stuff, but then you move up a notch to this kind of thing and all of a sudden like, "Boy, I'm glad I have this content strategy background because I need it again." Larry: Tell me a little bit about how that manifests in training a large language model? It seems clear, I think I get why you need to be strategic about it, but can you talk a little bit about why you've had to go back in your toolbox for your content strategy stuff? Laura: Yeah, of course. So training data for a large language model, of course, we're talking about volume of data that are really hard to wrap our heads around, and two techniques and one in particular that we've been using that are used as to train large language model or fine-tuning and reinforced learning. And there is all sorts of methodologies that are used and most methodologies require to look at content at scale, like ingest. And some of the technicalities, I admit, I don't fully understand myself, but I create metaphors in my mind or images of how I think certain things work. And I always imagine these large quantities of data, which in this case is really content, sentences and words and so on and so forth being ingested into these box and that then creates more content out of it. Laura: And so for me, I think in that sense, working on content at scale because some of the content is content that is created by UX, but we also work with a lot of other people. So it's not so much like me as a content designer, I have a full handle on all the strings that are going to go into a flaw, that's just not going to happen. And so it's more creating the guidelines. And some of that of course, is the work of a content designer, but I think here it becomes even a little bit more not just the guidelines in terms of style and voice and tone, but also operationally, how do we make sure that creating content at scale can work for the team to a scale that is big enough that it helps training the model? Larry: Yeah, as you talk about that, I'm wondering, first, there's two things in there that really interest me. One is you're using content as data, and then data as a design material. So are you looking for patterns in the data and the content or... Because at scale, you can't just look at every data point and go like, "Oh, we'll treat this one this way." Tell me a little bit about that? Laura: Yeah, that's exactly it. And I think that's, again, going back to what I was saying, my days as a content strategist and doing some sort of, for example, taxonomy work or thinking about in the past, how to label certain kinds of data. And this isn't necessarily what I do now, but when I did that in the past, when I work on categorizing content, a lot of what I had to do was looking at patterns and trying to figure out... And I have been in my head a little bit and loosely wanting to use the Pareto principle. If I remember correctly, 20% reflects the rest of the 80%, you only need 20%. Maybe this isn't a really good explanation, but that's what I think, sampling through the data and trying to find patterns, just like you said, and seeing how the model is responding. And from that, figuring out how do we constantly improve it with new training data. Larry: And you talked about that because you're actually in there training the model. And you mentioned two terms there, fine-tuning and reinforcement learning. Here's my little tiny brain's interpretation, is that fine-tuning seems like go on one level deeper than prompt engineering and doing higher level fine-tuning of the model itself. And then reinforcement learning, as I understand, is a neural network thing that's like a Skinner box kind of reinforcement, giving little food pellets to the model when it gets something right. Is that how it works? Laura: From my understanding, yes, the reinforce is a little bit more like saying, "This is good, this is bad," in a very simplified way, and that's one side of it. And then the other side, the fine-tuning, right now for me has been more working with a really large scale of content, like a really large amount of model responses. Larry: Yeah. Hey, and when you first started talking about this, you mentioned that the ultimate goal out of all this work with the modeling and your work in general is to operationalize it, to get it ensconced in your day-to-day work, I guess. How does that differ? Because I've seen them done a lot of that kind of work in the content design world, but in the AI world, not so much. How does operationalization look in your world? Laura: Yeah, that's a good question because I do think we're still figuring it out.

Chris Cameron At Booking.com, they've been helping travelers with their trip planning for many years. The arrival of generative AI has given them new ways to help travelers with this business-critical task. Over the past year, Chris Cameron has applied his UX writing and content strategy skills in ways both familiar and new to help build a new AI-powered Trip Planner tool that integrates with Booking.com's travel-booking app. We talked about: his work as a principal UX writer at Booking.com on their "writing system," which is sort of like their version of a design system for UX writers his recruitment to a "tiger team" at Booking to develop a new travel-planning AI chatbot for their travel-booking app the key differences between his prior product work and his work on this AI product the new kinds of collaboration that have arisen in his work on a generative AI product, in particular his work with machine-learning engineers the transition from the prototype of the app to its current position as an established product the product-feedback mechanisms that are built into the Booking "Trip Planner" how to jump start your learning if you're new to working on generative-AI tools how they were able to leverage components in their current design system to build the new Trip Planner app the prompt engineering skills he developed by creating an AI "story robot" for his three-year-old son his optimism about the employment prospects for UX writers how traditional content strategy practices like establishing voice and tone and consistent terminology manifest in AI product design how new AI practices are just as likely to show up as enterprise productivity improvements as in customer-facing products and features Chris's bio Chris Cameron has over 13 years of professional writing experience across journalism, marketing, and UX. As a Principal UX Writer at Booking.com, Chris oversees UX Writing Systems, managing the tools and workflows that enable over 80 UX writers to efficiently create high-quality content localised into over 45 languages and dialects. Born in Boston and raised in Phoenix, Chris now lives in Amsterdam with his wife and son. Connect with Chris online LinkedIn Video Here’s the video version of our conversation: https://youtu.be/bptOvimY4uU Podcast intro transcript This is the Content and AI podcast, episode number 11. As generative-AI tools are introduced into consumer products and enterprise workflows, the core work of content designers and UX writers still feels familiar, but the context for the work and many of its details are evolving. Over the past year, at Booking.com, where he has been working on an AI-powered travel-planning app, Chris Cameron has seen first-hand how the traditional concerns of content strategy and UX writing manifest in the world of generative AI. Interview transcript Larry: Hi, everyone. Welcome to Episode #11 of the Content + AI Podcast. I'm really happy today to welcome to the show Chris Cameron. Chris is a principal UX writer at Booking.com, the big travel booking agency based in Amsterdam. Welcome to the show, Chris. Tell the folks a little bit more about what you do there at Booking. Chris: Well, thanks, Larry, for having me. Yeah, I'll give a bit of my background as well. Like yourself, I started in journalism and then got into copywriting. And after moving to Amsterdam from the US at a very young age, 25, I guess, I eventually joined Booking in 2016, a little over seven years ago. And back then, the role was actually called copywriting. There was about 25 of us. And over the years we sort of discovered that we were actually UX writers, and we've become now this community of over 80 UX writers. And now, I am a principal UX writer, and the area I look after we call writing systems. And what that is is sort of like the writing version of design systems, but it's not so much a system, it's more like the tools and the workflows that we use to get our jobs done. So my role is to work on those tools and work on those workflows and make sure it's easy for our writers to get their jobs done in an efficient and easy way so they can create high quality content. And more recently, one of the areas I've been interested in looking into is GenAI and how we might use that to improve our workflows. Larry: Yeah, that's why I wanted to have you on the show. You told me about this product you developed, the Trip Planner, that's based on AI. Can you tell us a little bit about how that project arose and how you got involved with it? Chris: Yeah, definitely. So my involvement with AI and GenAI in general started when ChatGPT came out. I think a lot of people took notice back then. That was late last year, 2022. And I started playing around with it. I'm always a bit of a nerd and early adopter of technology, so I started using it for different things. I have a toddler at home, so I was actually using it to create bedtime stories for him. I would say, "Let's ask the story robot what kind of story you want to read tonight", and he would just generate a story idea, and ChatGPT would help us with the rest. It was a lot of fun. Chris: But professionally, I started thinking, "Okay, how could this be useful for our writers at Booking or how Booking as a company could use it?" And early this year, 2023, the company was seriously looking at GenAI and thinking, "Okay, what are we going to do with this?" And because I was already exploring it, I got pulled into some early discussions, and I thought, "Okay, we're going to have some brainstorms, some chats about how GenAI did," but actually the company was already like, "Let's go build a GenAI chatbot and put it in the app, and this is going to be the only thing you focus on for the next couple of months." And I'm like, "Okay, let's do it. Let's roll." Chris: And so basically, a task force was formed within the company, sometimes called a tiger team, we called it sometime, and it was representatives, multiple people from writing, design, research, product, and then also machine learning, our iOS and Android engineers, of course, data science, and marketing and legal. It was a big team. In the end, it was almost like having a little startup within the company, it was about 70 people. And the UX work stream was sort of one half of it, and the other half was all the machine learning and the engineering that was going on. Chris: And this sort of kicked off in mid-April when we started this, and two months later, we were able to launch the AI Trip Planner in June. Just so people understand what it is we built, we built basically an AI chatbot into the Booking app, and people can chat with it and ask their travel questions, and it can help them get inspiration for where to go or what hotel to stay at or build an itinerary, these sorts of things. And it integrates some of the traditional booking experience, like with carousels and images and property ratings and things like that, right into the chat so it feels a bit more natural. And then if they tap on a property, they can go straight into the booking process and make a reservation. Chris: And so a lot of it uses some of our existing machine learning knowledge we've built up at the company over the years and then relies a bit on OpenAI ChatGPT to do that generative AI piece and really create a nice conversation. So if people want to try it out, if they're in the US or they can VPN to the US, they can sign into the Booking app on iOS and Android and make sure their language is set to English, and they should see the AI Trip Planner right on the home screen. Larry: That sort of gets at some of the complexity around this, because I know you localize into 50 languages and cultures. Chris: Yeah, I think 45. Yeah. Larry: And so right now it's just English only and in the US, so that's interesting. And really, as you talk about that, I'm wondering from a user perspective, it's almost like just a UI thing. For an end user, you could almost perceive it that way. "Oh, another way to interact with this thing and do my trip planning." But on the backend, like you said, it's 70 people on this tiger team that put that together. How similar was it to other products, because as a principal you've worked on a lot of different projects probably at this scale, how much of it was familiar and similar and how much of it was new? Tell me a little bit about that. Chris: Yeah, definitely. There was a lot that was familiar to just a normal building a product, but there were some key differences. For example, for working with a GenAI product specifically, it's such a new thing that there's not a lot of existing research. So if you're going to go to your researcher and say, "Okay, what do we know about GenAI?" It's like, well, they're still learning too. So a lot of that was involved, looking at what is out there in the market, what competitors are doing, but then we were also able to combine that with the existing understanding of user needs, because essentially this is a search experience that we've been dealing with for a long time at Booking. So we know a lot about what the user's looking for in that moment when they come to the app. So those needs didn't change, but the way they were expressing those needs is the whole new thing. Chris: And in the early stages, when we were trying to test something, it's not that easy to build a GenAI prototype. If you're building a prototype in Figma, you can't really insert the AI part in there very easily. Maybe soon that will be a thing. So we had to wait until we actually had a working build of the tool where we could play with it internally, and that's when we started actually doing a lot of the understanding of, "Okay, what's working, what's not?" that sort of thing. So there was that challenge. Chris: But from a content and writing perspective,

Lance Cummings Education often lags behind tech trends. Not in the case of AI. And not when Lance Cummings is involved. Lance conducts academic research on AI content operations and has worked both with technical communicators and with content entrepreneurs in the creator economy. Along the way he has discovered concepts and practices around structured content that apply across prompt engineering, tech writing, and influencer content creation. We talked about: his work as a rhetoric and writing professor and research on the creator economy his view of content operations and workflow, especially new practices around AI how the introduction of the idea of "AI content operations" clarifies the writing process for content creators of all kinds, including the technical writers that he teaches how a structured-content approach can help writers of all kinds cultivate a garden of ideas how the real value around your content lies in interactions with your community, not necessarily the content itself his approach to collaborative prompting, knowledge management, and development of AI tools how standards and practices like DITA and object-oriented knowledge management how structured content can actually make us more creative why creative writers generally excel in the tech writing field Lance's bio Lance Cummings is an associate professor of English in the Professional Writing program at the University of North Carolina Wilmington. Dr. Cummings explores content and information development in technologically and culturally diverse contexts both in his research and teaching. His most recent work looks at how to leverage structured content with rhetorical strategies to improve the performance of generative AI technologies and shares his explorations in his newsletter, Cyborgs Writing. Connect with Lance online Cyborgs Writing LinkedIn Video Here’s the video version of our conversation: https://youtu.be/lneGOV6tNbY Podcast intro transcript This is the Content and AI podcast, episode number 10. We are quickly discovering that AI can help content professionals across the span of their work. Lance Cummings is a consultant and college professor who is exploring intersections that most content folks haven't had time to ponder. For example, he has found that his approach to AI content operations can clarify and improve the writing process for both technical documentation authors at big enterprises as well as fiercely independent members of the creator economy. Interview transcript Larry: Hi everyone. Welcome to episode number 10 of the Content + AI podcast. I'm really delighted today to welcome to the show Lance Cummings. Lance is a professor of English in the professional writing program at the University of North Carolina in Wilmington. He does a lot of interesting research, and we'll talk about that as we get going. But one of the interesting things in the intersections of his research and academic interests is this notion of applying structured content, looking at structured content, rhetorical strategies, and AI technologies and workflows around that. I'm really excited to talk about all this stuff with you, Lance, but welcome to the show. Tell the folks a little bit more about what you're up to. Lance: Yeah, so I'm a professor in rhetoric and writings, which generally just means that we study how people write, make meaning, get things done with text. And more recently I've been researching the creator economy, actually before AI. That's how I stumbled across AI in 2021. And if you don't know what the creator economy, it's what this podcast is. It's people creating content directly to audiences using the various digital platforms out there, and oftentimes either making some money or a lot of money or making a living even off of doing this. I would say different from influencers, I would say creators are creating useful content for their audiences that they can use and very specific audiences. And since COVID that has risen 50% every year, but when you get deep into the creator economy, they really think about content in terms of workflow and how do you create a process to develop content consistently, and how do you be creative? Lance: Because as a content creator, you have to consistently build content for your community. I stumbled upon AI and I thought, "Well, we're all going to be using this in two years," and here we are. And so I've been exploring then how AI works into the writing process, both in my own content development and also among creators. And then thinking about that in terms of technical writing, content and content management. One of the things that I think content specialists or tech writers have to offer us is a more structured conception of content and how that works into this idea of workflow. I was at a conference, I go to a conference every year in Krakow called SOAP, and last year the topic was content operations, which it seems like that's a term going around a lot. And we just simply defined it as the people, processes, and things that are around content creation. Lance: So thinking about writing as a networked activity of relationships between us and people and the technology, and then thinking about how that shifts and changes. And that's what we would call a workflow, rather than thinking about, "Okay, here's step one of the writing process, step two, three, four, five," thinking of it, "Okay, here's the network of things that are happening to make this content work." Larry: One of the things we talked before we went on the air a little bit about, that workflow, I think anybody who has done any content operations has a conception of workflow in their head, but there's a specific meaning to workflow in the academic world, which I wasn't aware of. Can you talk a little bit about, well, first that academic meaning of workflow, like when you're teaching professional writers or professional writing skills, how you think about workflow and then how that is applied in your current work in AI. Lance: So workflow is really, we're trying to make a shift from thinking of writing just as a process. So the traditional way of teaching writing is actually based on the canons of rhetoric, but you brainstorm or come up with ideas, you organize those ideas, then you draft, get peer review, and then publish, right? Well, I guess thinking in terms of workflow is thinking about content much more from the software development side where content is actually constantly changing, shifting, and people who create content or write are constantly thinking about how to change or tweak their workflow. So it's not like a number of steps set in stone, but rather this network of actions or interactions that we have with people and things that are constantly changing on their own, but then we can also find points where we can tweak those to make it more efficient, more creative, more interesting. Lance: And I think that tech writers or content specialists have been doing this for a while. The best content writers that I know are constantly tweaking their workflows and adapting, and I think that's why you see a lot of content creators, tech writers, adopting AI fairly early because it's an easy next step to think about AI as where does it fit into this workflow, rather than how does it take over this part of writing? So integrating AI in a way that extends what we're doing and enhances our workflow, but doesn't necessarily take over. Larry: Yeah. That's a great way to think of it. I think some executives just think of it, "Oh, we'll just replace people," but it's like, "No, we can extend and enhance the work that we're already doing." As you talk about that, some of the specific ways that AI can fit into this, because conventionally, I just think that AI is, I think Sam Altman said AI is really good at tasks, but not very good at jobs. And so are you looking at it that way, as what are specific tasks in these workflows that AI can help you with? Is that where you start? Lance: I think task is an important way to think about AI because if you don't give it a clear task, it doesn't necessarily know what to do, but then you have to think about writing your workflow as a set of tasks. So when you start to think about what you're going to create for this blog, what is the task that you're technically doing in this workflow? I think a lot of times, especially good writers, a lot of what we do is intuitive, implicit, but we don't necessarily always explicitly can say what we're doing as writers. And actually, I think this is one of the best things that AI can do for writing education, is to force us to think about, "All right, what exactly am I doing when I'm creating this piece of content? What are the tasks, and can AI do this task? Should AI do this task? Will AI do this task?" And I think that's part of what I would call AI content operations, is actually deciding what AI can do or should do and when, and I think task is probably one of the better ways of thinking about it. Larry: Yeah, I'm really curious now how you tease that out, because as I think about that, so much of writing used to be perceived as magic, just like you just think and magical things happen. But like you've mentioned, both the creators and the creator economy and tech writers are really good at analyzing what they're doing. Are they exemplars or just lucky in the kind of work that they do requires them to be reflective and curious about where did that intuition come from? How can I tease that out and get better at that? Does that make sense? Lance: Yeah, I think tech writers are forced to do that in their ... Obviously it's going to depend on the context, but if you're working with people, you have to make your workflow explicit to them if it's going to be successful. If you're working with AI, actually, you have to make your workflow explicit.

Dave Birss (AI-generated) Dave Birss has had a busy 2023. Since developing his first AI course for LinkedIn Learning early in the year, he has produced five more courses and has become the learning platform's most popular AI instructor. We talked about: his experimental approach to teaching AI how he helps companies understand the true benefits of AI the importance of using AI to augment people's skills rather than just to try and save money the elements of his AI manifesto use AI responsibly be ethical support your employees assign leaders keep learning always add a human layer to AI output the importance of critically consuming advice from anyone who proclaims to be an AI expert the importance of companies learning for themselves because there are few reliable consultants available now how unlocking the true benefits of AI can change companies' perspectives and help them see new opportunities the crucial task of understanding people and addressing their needs as AI is adopted his observation that it "cannot be AI or human, which is the way that a lot of companies are seeing it, it's got to be AI plus human" how the adoption of AI supports his point of view that generalists have an equally important role in the modern workforce as specialists Dave's bio Dave Birss combines the analytical mind of an AI geek with the butterfly mind of a former advertising creative director. This helps him make the ever-changing world of AI approachable, relevant, and occasionally entertaining. At the start of 2023, he launched his first LinkedIn Learning course on Generative AI. Since then, he’s released another five courses, all of which have gained fantastic ratings and reviews. In July LinkedIn announced that he’s now the most popoular AI instructor on the platform. But Dave isn’t just about online courses. He’s also a globe-trotting educator and public speaker, helping companies and individuals get more value out of Generative AI. He’s also a best-selling author with several books on creativity and innovation. And a former broadcaster and film-maker. As a sought-after keynote speaker, Dave speaks about AI, innovation, and creative thinking with a blend of science and dad-jokes. He’s a Scotsman who lives in London with his Haitian-American wife and two delightfully confused children. Connect with Dave online LinkedIn DaveBirss.com Video Here’s the video version of our conversation: https://youtu.be/2QL01qN6uzY Podcast intro transcript This is the Content and AI podcast, episode number 9. Over the past year, we've all been getting up to speed on AI. Over that time span, Dave Birss has become the most popular AI instructor on LinkedIn Learning. Dave would be the first to tell you that he's not an expert on artificial intelligence. But he's a very experienced technology professional who has witnessed several major earlier tech revolutions, and he's an experienced teacher and consultant, so he brings a very pragmatic approach to incorporating AI in your work life. Interview transcript Larry: Hi, everyone. Welcome to episode number nine of the Content and AI podcast. I am really delighted today to welcome to the show Dave Birss. Dave is an educator, author, and consultant currently focusing on AI and AI education. He's the most popular AI instructor at LinkedIn Learning. Welcome, Dave. It's great to have you here. Tell the folks a little bit more about what's going on these days. Dave: Thanks, Larry. Yeah, I've been creating courses on AI this year, really. And I can't really call myself an AI expert. I guess I'm an enthusiast and I am an experimenter. I guess I do research to find out what works best, and then I share that knowledge with people. Dave: If you told me a year ago that I was going to be doing AI as my main thing, I wouldn't have believed you because OpenAI only released ChatGPT on, I think it was the 30th of November last year, so it's still less than a year old. And when they launched it, I just threw myself in, absorbed as much as I could, created some frameworks, easy ways of being able to teach people, and then I just released these as courses. Dave: I've got now six courses on the platform. Just released another one last week. And I'm about to release some courses on my own website as well. Yes, that's my life these days, doing courses and then helping companies get onto their AI journey in the best possible way because I think a lot of them have got the wrong attitude. They're not looking at AI in the right way. Larry: Yeah, interesting. Tell me more about that, because I think we all have opinions about AI. What are you discovering? Dave: Well, of course, companies, as you know, they will tend to have, "Here's our quarterly target, here's our quarterly goal. Can we make more money and spend less money in this quarter?" That's what they do. It feels as if that's the responsibility of a company is to do that. Dave: Now, if that's your attitude towards AI and that you're only interested in using it to save money, really, it's all about productivity, then you're really missing out on 90% of the benefits of AI. Because the real benefit of AI is not just to help you do less work and do work faster, it's to help you do better work. And when you do better work, that gives you an advantage in the marketplace. If you think that you've got this line across here, that this is the profit and loss of a company, you can only nibble away at that by starting to replace humans and tasks by AI. And what you do in return is you do a deal with the devil, which is you embrace the fact that AI is fantastic at adequacy, which means that you're going to get stuff that's all right, maybe just about average that you're going to get from AI. And if you're replacing humans by AI, you will save money, but you get adequacy in return. Dave: When you use AI, on the other hand, to make humans more capable of doing phenomenal work, of stretching further than they were able to stretch before, then really, the sky's the limit. At that point, you're gaining profit rather than trying to save cost. And the problem is that most companies are so focused on saving costs and this incremental growth, they're missing out on what is the real potential of embedding AI into your company, into your system, which is to do better work, to reach higher, achieve more. Larry: I love the way you're contextualizing that because going from that... I love that they're fantastic at adequacy. And it's like- Dave: They excel at it. Larry: They excel at adequacy. But the real potential here is in unleashing way more human potential on this work. And this speaks to the need for... Because that quarterly focus of enterprises, it's just notorious in any number of circumstances. Have you had any success or do you see ways that companies might get past that and start to think more strategically about how to embed the benefits of AI in their orgs? Dave: Well, when I talk to companies about it, they get it. But there are so many companies that their form of motivation for people in senior leadership is keep cutting costs. We're only focused on this quarter. And that kind of short-termism, I think, will really come round and bite you in the butt when it comes to business. Dave: One of the things that I've been doing from conversations with businesses over the last, well, this year since I've really been doing this AI thing, is that I've developed a manifesto that I'll shortly be releasing. Let me see if I can find my cursor on here and I can maybe bring up my manifesto. There we go. This manifesto is all about helping companies understand what they need to do to embrace AI properly. Dave: Zoom is doing funny things for me here. If I go back here, I can then share what I've got. I've created this manifesto, and I'll quickly take you through some of the points in the manifesto. I think that this first thing is what I was talking about, is that it's important that we use AI to augment people's skills rather than just to try and save money. I think that that's the main focus for companies that want to get real success and value out of AI. Dave: Obviously using data responsibly is an important thing to do. And that's something you have to communicate to your staff, what we mean by using data responsibly. You've got to be ethical because you've got to be guided by your head or your heart. Companies are used to being guided by laws, but we don't have those laws here yet. There will be lots of court cases that is going to generate some laws over the next few years, but you do not want to become a legal precedent. Because of that, you should make sure that you're thinking properly and guided by your heart and ethical responsibilities when you're making your decisions. Dave: You need to support your employees. That means give them training, give them guidance, let them know. If there are employees that are worried about this, you need to give them emotional support as well to help them on this journey. I think it's important of leaders. You need a butt to kick and you need a back to slap. And it's important to keep learning because this stuff's changing all the time. Dave: Just in the last few days, OpenAI has pretty much exploded as a company internally when Sam Altman was fired. And it looked as if he might be joining again, but now he's joining Microsoft. And everything's changing so fast. And then two weeks ago at OpenAI's Dev Day, they introduced so many things that really, really changed the whole world of AI. And then you've got some- Larry: I've got to interject real quickly. We're recording this on November 20th. And by the time this airs in a couple of weeks, it'll be completely out of date. But I think the elements of your manifesto are timeless. Yeah, sorry. Dave: Yeah. Yeah,

Lisa Jennings Young Over the past five years, Lisa Jennings Young has pioneered the adoption of AI tools in content-design practices at Twitter and Microsoft. Lisa has watched in real time the realization of the benefits of natural-language AI tools to help govern and create content, as well as to assist with content-design research and operations. We talked about: her pioneering work with AI when she as at Twitter her thoughts on the important role that natural language processing (NLP) plays in content-design governance now natural language generation (NLG) can help content designers how she sees NLP and NLG helping her scale content-designer operations the principles that guide the implementation of AI at Microsoft: is it good for Microsoft? is it good for individual teams? is it good for our customers? how her work aligns with Microsoft's strategic objectives some of the work that content designers do that she doesn't see AI replacing anytime soon: stakeholder alignment, customer research, journey mapping, content ecosystem analysis, etc. how implementing AI tools has resulted in new communications opportunities with cross-functional partners the importance of prompt engineering skills her hot take on AI and content design: "It's not about replacing writers, it's about affecting them. So AI won't replace writers, but writers working with AI will replace writers working without AI." Lisa's bio Lisa Jennings Young is the Head of Content Design for Microsoft Teams. She has over 20 years of experience creating content strategies that scale, with a passion for bringing life and voice to digital products. With extensive experience in process design, tooling, writing AI, and content moderation, she helps teams do more than write digital interfaces. She helps them create human experiences. Before heading up Content Design for Microsoft Teams, Lisa was Head of Content Design at Twitter. While there, she built a team that set a global example for how social media can be more inclusive, accountable, and equitable for everyone. When not spending time with her husband and four kids, Lisa loves to read nonfiction, tend her Oakland garden, and cook for crowds. Oaktown Spice is her home away from home. Her spice game is on point. Connect with Lisa online LinkedIn Video Here’s the video version of our conversation: https://youtu.be/gVFsTiSuxWs Podcast intro transcript This is the Content and AI podcast, episode number 8. Few people have had as good a front-row seat as Lisa Jennings Young to see the emergence of AI tools for content-design practice. First at Twitter, where she pioneered some of the earliest use of natural language processing tools in a content-design operation, and now at Microsoft, where she leads a team of content designers and technical writers, Lisa has led the way in showing how AI technology can both help content professionals and democratize writing skills for non-experts. Interview transcript Larry: Hey everyone, welcome to episode number eight of the Content and AI podcast. I'm really happy today to welcome to the show, Lisa Jennings Young. Lisa is a principal content design director at Microsoft Teams, and welcome to the show, Lisa. Tell the folks a little bit more about what you're doing these days. Lisa: Thank you so much, Larry. It's great to be here. So yeah, so I am at Microsoft Teams right now, leading content design for that product. I've been there six months. I resigned from Twitter last November, so almost a year now. And yeah, I've been settling into Microsoft. It's a huge company, getting to know the lay of the land, really connecting with my amazing team there. So yeah, so it's been a good six months. Larry: Yeah, that's interesting. I think when we talk historically, I think of you and I were just chatting a year and a half ago at Confab, and it was just a normal conversation about work stuff and things. And then we connected again earlier this year in February. I put together a panel for Tracy Playle, that Utterly Content, about AI. And you were the first person I thought of for that panel, because you've been working with AI tech, and you had been doing stuff at Twitter, and I know you're doing it again at Microsoft. So anyhow, I just want to observe that our relationship over the last year and a half has been a microcosm of all the craziness we're all going through, watching this stuff evolve. Lisa: Yeah, we were just having a casual conversation and things changed. Larry: Tiny bit. Yeah. But the thing that's been a steady across that is your interest in, and use of, AI. I don't know, maybe talk a little bit about the stuff that you and Jordan had done at Twitter, because that was really interesting and promising. Lisa: When I first started at Twitter, one of the things... Because my career started as a tech writer. Then I moved into content strategy, enterprise content strategy, marketing websites, etc, etc. And so I've always been really interested in how you work at scale. I had usually been a team of one, a content strategist of one or a content designer of one. And how do you really make an impact? So one of the first things I did when I joined Twitter was I put together a proposal for bringing in this AI platform I had heard about right before I left Advent Software called, at the time it was called Cordoba, and they've since rebranded as Writer. Lisa: But yeah, that's one of the first things I brought in, because I really wanted to, we actually were a very small team then, there was two of us who were working on the consumer side of Twitter. How could we do that at scale? And I was very intrigued from content, brand adherence... At the time, there wasn't Gen AI, there was one type of writing AI, this was back when I started Twitter, which would've been four, five years ago? Oh my gosh. Lisa: And it's natural language processing. And especially at a place like Twitter, the words are at the center of the design. And we learned that over and over. And when you're at the center, everything is magnified like a thousand times. So I've always been fascinated by, and intrigued by, and motivated by, how do we ensure that the words we craft so carefully remain intentional, consistent, and relevant over time? Lisa: So it comes down to governance, content governance, and how we can achieve that singular consistent brand voice at scale. So at the time, the role of AI in Twitter's content design practice was brand adherence, enforcing our brand guidelines. A way to ensure that consistency and cohesion over time, so that whoever writes the copy for the next experience just doesn't have to start from scratch. I mean, you're talking about all the user research you do, the drafting, the revisions, the experimentation. You don't want to lose that. So at the time, writing AI for us, again, was natural language processing, or NLP. Lisa: So NLP is that branch of artificial intelligence concerned with giving computers the ability to understand text and spoken words in the same way people do. At Twitter, we used it like we're doing at Microsoft now, to keep our writing on brand and ensure our guidelines, ensure we can enforce them across all those touch points of of microcopy that can make the digital experience. Lisa: So when you're working on a global... It's like the one constant, it does come down to build on our successes, rather than starting from scratch each time. So that's the NLP part, kind of basically where we were when we were at Confab the last time, is what we were talking about. Larry: Well, and actually, you're reminding me even before that, I think it was at Lavacon about the time you started at Twitter, but I think I saw a Cordoba demo, and they likened it to a writing coach standing over your shoulder going like, "Oh no, that's not how we say that here." And that's NLP in action. It understands what you're doing and says, "Well, actually we do that a little differently." But I gather you were just going to talk about the other thing that's kind of been more in the headlines lately about AI. Lisa: Yeah, and it's funny, because when we were talking last Confab, the way I was presenting writing AI was like, "Hey, we as content designers aren't using NLG, Natural Language Generation." This, again, this is over a year ago. And at the time, it didn't have the research insights, and I still believe this today, when you get to content design, AI doesn't have those research insights. It doesn't understand our product strategy, our business goals, our user needs, which is why writing AI won't replace content designers for absolute sure. Lisa: It's not aware of our content formats at this time, or our surfaces, or of our cultural zeitgeist. But since that time, and I don't know, the audience probably knows NLG, but just in case not, NLG is that branch of AI that produces natural or spoken language from both structured and unstructured data. And so again, so at the time, I had a different perspective on AI, and it's evolved over the year, and I really see now that I've been in it into it for a while, how it can really give writers that boost. Lisa: It's like having the jet pack on your back, and that's because of the structured and the unstructured. So for example, at Microsoft, we'll be using it not only for the rewrite, simplify, shorten, which is great when you're working in tiny boxes, five characters or two words can make a huge difference. But also for that structured part, we were able to work with and build our own custom templates, so we can feed it the inputs and the outputs ,so that when, for example, there's absolutely more writing than my help team can do. Lisa: That's one of the exciting things that I do love about Microsoft, is I get to have work with content designers and help and support.

Claudia Francesca Mueller At Trusted Shops, Claudia Francesca Mueller and her colleagues have built an AI-powered chatbot called Piuma that lets non-writers access content guidance through a natural-language interface. It took just a few weeks to launch the initial version of Piuma, building the chat interface with Voiceflow and using the LangChain development framework to access both their content design guidance and OpenAI's API. Even though the chatbot's functionality matched their users' expectations almost perfectly, they still find that they have to constantly collaborate with their partners to fully understand their needs and communicate the benefits of the product. We talked about: her work as a content design and localization lead at Trusted Shops Piuma, the AI chatbot they have built at Trusted Shops how Piuma arose from research and discovery work they did around how to best share their content-design guidance how they developed Piuma using Voiceflow with guidance from a conversational design expert the learning curve around incorporating LLMs into a chatbot like Piuma how they decided which parts of their voice and tone guidance to include in the chatbot how Voiceflow works with the OpenAI and Langchain the need to sometimes adjust the source documentation that the LLM is consulting to get the answers you want in the chatbot how her multilingual background helps her understand computer languages the challenges of getting designers to adopt a new tool like Piuma her ongoing communication with designers to understand their needs and how to address them how she balances evangelism and outreach with collaboration around improving Piuma the tendency of humans to stay with familiar patterns and routines Claudia's bio Claudia Francesca Mueller is a multilingual content designer living in Amsterdam. She speaks Swiss-German, Italian, German, English and Dutch daily, and feels at home when languages are mixed up in one sentence. That’s how she has also learned to bridge culture gaps with style and the right tone. Her love for languages, words, culture, shapes and colours brought her to content design. A discipline that became her passion and that she loves to live as an expert, leader and coach. Her background and career in multiple content roles have strongly shaped her thinking. She believes content is a holistic discipline where words are only one of many tools to convey a message. Currently, Claudia works at Trusted Shops as Principal Content Design and Localization. Connect with Claudia online LinkedIn Video Here’s the video version of our conversation: https://www.youtube.com/watch?v=43688rk97rc Podcast intro transcript This is the Content and AI podcast, episode number 7. On any one digital product team, there are never enough content designers or UX writers. So when interaction designers or engineers have to write interface copy, they typically have to consult content-design documentation. AI creates new ways to share this kind of content guidance. At Trusted Shops, Claudia Francesca Mueller and her colleagues have built an AI-powered chatbot that lets non-writers access content guidance through a natural-language interface. Interview transcript Larry: Hi, everyone. Welcome to episode number seven of the Content and AI podcast. I am really delighted today to welcome to the show Claudia Francesca Mueller. Claudia is a multilingual content designer and a localization lead at Trusted Shops. Welcome, Claudia. Tell the folks a little bit more about what you're doing these days. Claudia: Hi, everybody. Thanks a lot for having me, Larry. As you said, I'm a content design and localization lead at Trusted Shops. Trusted Shops, for the ones that don't know what Trusted Shops is, it is a German e-commerce software as a service company. We certify shops. And if you're a trustworthy shop, you will get a badge and you can start collecting reviews. We offer a review system management to the customers or shop owners. And for the consumers, we offer a buyer protection. And I'm part of a UX team at Trusted Shops. We are seven designers at the moment. And in the content localization team, we're five people. Claudia: Together, I do the math, we're around 13, 14, 15 people in the UX team. And I'm responsible for the content design craft at Trusted Shops. I jump into, let's say, projects that have a high impact on business, but I am also responsible to develop the craft. That means helping others in the team to write UX copy. One of the things that actually was part of this lately was developing a UX writing body called Piuma. That was one of the projects I worked on it this year. I can tell how we came actually to build Piuma the chatbot. Larry: That's why I wanted to have you on. And also, something you just said, we talked about this a little bit before we went on the air, but your official title is principal content designer at Trusted Shops. And I think what you just described is a classic principle role that, "Hey, there's this new thing. Got to do it. Somebody super experienced has to take this on." I just wanted to get that back in there because it sounds like you're genuinely doing principal-level work. But I'd love to hear the origin story of Piuma, especially the name. Claudia: Piuma is my cat. And she's always here in my home office. And when we started working on this project, and I worked together with a lovely technical writer in our team, I said, "Well, we need a name." And he said, "Well, we just call it like your cat, Piuma." And I was like, "Yeah, that's a good one because I really love Piuma and she's always here." That's how we actually got a name for the project, but also for the chatbot. And the whole idea of this chatbot ... Well, I have to go back in time a little bit because when I started Trusted Shops, we were talking about consistency. There was no consistency. There were a lot of voices, casual, funny. You could see whatever you wanted to see just in our content, in our UI. We thought, "Well, we need consistency, so let's develop a voice and tone guideline." Claudia: And that's also what we did. But after just doing all this work that actually took a year because, as I said, I have other tasks, that was just side project, we implemented those, so this voice and tone guideline, in the design system, in the existing design system, so we could call it content and design system. Also, actually for advocacy reasons, of course, when it comes to content design. The next step was like, okay, so now we have this documentation, but it's just a documentation, so how do we get people using this documentation? Because we all know it's not really attractive to have a documentation lying somewhere. And people just forget about it. How can you do that that just designers that actually are busy mostly with other things, more the visuals, how can we get them using this documentation? We thought about a lot of different softwares and we involved the designers as well in this process. Claudia: We thought about Ditto and Frontitude. And somehow, all of these softwares were not convincing. Designers were like, "Yeah, we don't know. It seems like a lot of work." And so we were like, "Okay, so if this software are not the solution or are not actually what you need in your daily life on your daily work, let's find something that works for you." We did a survey and we did research with the designers and OpenAI came up. One of designers just said literally, "Wow, it would be so great to just have a plugin in Figma and we can just ChatGPT." And then I was like, "Hmm." And then at the same time, I saw somebody from Expedia that was posting on LinkedIn about a chatbot. And I was like, "Okay." I just combined ... I was just making the connection. Just have our documentation as a knowledge base and data that we can use actually for the chatbot and we build a chatbot in Figma. Claudia: That's actually how this whole idea started on this project. And everybody was like, "Oh, yeah, that's such a great idea. Let's do this." Of course, there was missing knowledge in this whole project that I couldn't fill because I've never worked ... I've never built a chatbot. I know OpenAI, yes. I slightly knew what the LLM is, but how do we connect this data? How do we get a chatbot, the user interface? We needed an expert. And luckily enough, I had one at hand here in the Netherlands and a conversational designer expert. And so we asked her, "Would you be interested to help us for this project?" And she was like, "Oh, yeah, that's such an interesting project. I really want to help you." And so we hired her and she helped us with the first MVP and the concept of talk to your style guide. Claudia: And so she recommended to use Voiceflow. That's one of the programs out there. It's a commercial program, of course. Platform that you can use and that you actually ... It's actually really accessible because you're going to work especially on the conversational flow with blocks. And it's really intuitive, so it's easy actually to ... I would say for content designers that are not really technical yet, that you just work on the conversation, let's say. And of course, there's prompt engineering involved because you have to tell, let's say, the LLM and the whole data what it has to do, that you get the answer you want. But still, for starting up with conversation AI, I think, and building a chatbot, it's a great program. We started working on that together also with a technical writer that helped me with the data. It's really easy. Claudia: You just upload a file or website. It's like the most easy you can think of. And then you're just busy actually with the conversation. And of course, when you start working with it, you get curious because you want to know how it works because if you know how it works,

Kurt Cagle(AI-generated image) Kurt Cagle has been reporting on and participating in the tech world for several decades. He's never seen anything like the pace of change around AI. His advice for staying ready to work with AI: stay nimble, pay attention to what's going on, don't get tied to any one technology, and always bear in mind that your work will have an impact. We talked about: his interest in the intersection of AI and knowledge graphs the rapid and vast advancements in AI tech, especially recent developments at OpenAI [referring to the product announcements in early November, not the Sam Altman excitement later in the month] how building AI products in the current environment is "like building a luxury hotel on top of quicksand in an earthquake zone" the impact of open-source and component-based thinking in the AI ecosystem and how those dynamics are democratizing AI how a Disney character can help you understand LLMs how tokenization work the difference between how a query to a typical database works and how vectorization identifies things that are similar how knowledge graph technology can help solve some of the problems in the LLM space his advice for staying ready to work with AI: be nimble, pay attention to what's going on, and don't get tied to any one technology Kurt's bio Kurt Cagle is managing editor of The Ontologist, Generative AI, and the Cagle Report, and is a thirty-year veteran in the knowledge management space with twenty-five books and several Fortune 500 clients and government agencies. He lives in Bellevue, Washington with his family and cats, where he likes watching the rain fall. Connect with Kurt online LinkedIn kurt dot cagle at gmail dot com Video Here’s the video version of our conversation: https://youtu.be/enaZSqj5vkM Podcast intro transcript This is the Content and AI podcast, episode number 6. Developing software has always been challenging. AI takes things to a whole new level. Kurt Cagle says the current pace of development in the generative AI world is "like building a luxury hotel on top of quicksand in an earthquake zone." Kurt has reported on and participated in many eras of technology advancement, but he's never seen anything like this. His advice for thriving in uncertain times like these: stay nimble, pay attention to what's going on, and don't get tied to any one technology. Interview transcript Larry: Hi, everyone. Welcome to episode number six of the Content and AI podcast. I'm really happy today to welcome to the show Kurt Cagle. Kurt, he's the editor for The Ontologist. He's a managing editor for the Cagle Report. He's been a technologist for more than 35 years. He's written 25 books. He's been blogging for 20 years. He knows one or two things about AI, so welcome, Kurt. Tell the folks a little bit more about what you're up to these days. Kurt: Thank you for having me on the show. I really appreciate it, Larry. I am busy in AI land and in the intersection of AI and knowledge graphs, and have been trying to find out where the two meet and otherwise contributed to one another. I do run a small consulting company. I have a Calendly account if someone wants to contact me directly for setting up an open office hour, and always looking for opportunities. Thank you very much. Larry: Yeah, cool. Well, and speaking of opportunities, we met through the knowledge graph community which I've been involved with for a few years now, and as the LLM explosion of the last year has come along, I've been like, "Where's the connection between these?" And all of a sudden, the last, I don't know, few weeks it almost seems... We're recording this on November 7th, just for people's reference because you need an anchor in this rapidly changing thing. This episode probably won't air for a couple more weeks, but we're recording this on November 7th and just either today or yesterday, OpenAI announced a bunch of new product features that include integrations of chat agents and knowledge graph stuff as well as some other stuff. Can you give us a quick overview of that big product announcement that just dropped? Kurt: Yeah, it was huge. Quite honestly, if you're involved in the AI space and have already put something together into a product, the announcement of a couple days ago probably has completely upended any apple cart that you might actually have. There's a whole lot that goes in. Better performance, much bigger context, which is essentially how much information you can send over the wire and how much you can actually refer back to. There is, and I'm pulling this up so if it looks like I'm looking off into the space here, I am, but a whole new Assistants API, new integration with Retrieval, what are called RIGs, and code interpreting, so you can actually define function calls. It now incorporates GPT-4 Vision so that you can effectively put up an image and say, "What is this?" Or even, "Here's an invoice. Do something with it." It has a new text to speech module, which is kind of cool. I'm still playing with that one. And then a lot of business-esque things, making it cheaper to use, cheaper to modify, cheaper in general. Kurt: I think a lot of people were going, "This is too expensive. We need to be able to pull things down." And that was the bulk of it. There's some other things, improvement with DALL·E, which is their image rendering API, and I've been having some fun just playing with that for producing surprisingly good images. But overall, what you're seeing with this is essentially a shift away from, "Here's a single product," to, "Here's something that is intended to be much more fully integrated." There's a new multimodal modality that is also associated with this that allows you to integrate what had been largely separate plugins into a single framework. So you can load in files. You can create files. You can really get access to any kind of information that you're looking for and do things to manipulate it. It's a really, really cool piece - and it's also causing a lot of problems with other companies that are now going, "Oh, this is going to be so fun." Larry: Well, it sounds like they're trying to become the one-stop shopping solution for all of your... Is it mostly about generative AI? I was trying to listen to all this stuff you said. It sounds like it's mostly generative AI stuff. Kurt: It's mostly generative. Admittedly, most of what open AI is currently concentrating on is generative. There's some agents and agent technology that's coming in that is along the edge of that and gets more into, "How do you build companions?", "How do you build avatars?", "How do you integrate?" And to a significant extent, what it does is it raises the bar. There are others playing in this space like Hugging Face, like Google, like Amazon, and it was very definitely a shot across the bow that says, "Okay, you need to be able to match this at a minimum." Larry: Yeah. So we're going to be facing that for... One thing that's occurred to me, and I'm almost embarrassed to admit this, in the two and a half weeks since I conceived of and started this podcast, I first thought of it as mostly sharing information so that people could use this technology better. But I've talked to a number of friends already just in the last few days who work at big enterprises and big tech companies who are doing product content work on these tools themselves. I can't say which companies they're at, but there are things like OpenAI... I haven't talked to anybody at OpenAI, but I have talked to people at similar companies doing similar things. Do you have a feel for what it's like to be a product person or designer, an engineer working on these teams that are creating these new AI products? All the stuff that went into this announcement we just covered? Kurt: Yeah, they're not sleeping. Nobody is sleeping at this point, which is a little scary. Realistically, this industry tends to go in waves where you'll have nothing and nothing and nothing. Then all of a sudden, you get a whole bunch of announcements at once and a paradigm shift that occurs in how things get done. The last major one that I can really think of was probably Hadoop and that, well, Hadoop and then the machine learning data learning or data science development. Before that, it was the rise of mobile phones. And in each of those cases, there was something of a mania aspect to it. People basically trying to get something up there, hoping it sticks, and hoping that someone else doesn't come in. I cannot remember ever seeing it moving so fast that you're literally having to wake up in the morning and spend an hour just keeping track of what happened overnight, and then take that and put it into sense and be able to play with it. Kurt: So it's a challenge for a lot of people because it means that there's a lot of people that are playing with it. A lot of people that are looking at it from a business perspective or a use case perspective that are fascinated by what they see, can see potential, but they're also, in some respects, I think they're afraid to necessarily push too fast simply because things seem to still be moving. As I put it, it's like building a luxury hotel on top of quicksand in an earthquake zone. Things will change. It's guaranteed that things will change, and when it does, you just hope that you have slightly more firm quicksand than your competitor down the road. Larry: Wow, that is some scary context for this, but I think people can relate to that. That's how it's felt, like we're all quivering in the quicksand trying to figure out what the heck's going on. One thing, we had a preliminary chat about this last week and I'm really glad we didn't record then because now we got to break this news about the OpenAI announcement. But one of the things we talked about then, it's addressed in this stuff we just reported,

Tane Piper "Leading-edge technology" may not be the first thing that comes to mind when you walk into an IKEA store, but maybe it should be. IKEA is using AI technologies across its vast collection of businesses to deliver better content experiences to its customers. Tane Piper leads an engineering team at Inter IKEA - the business unit that owns the IKEA brand - that is building their next generation of content and artificial intelligence tooling. We talked about: his role at Inter IKEA the scope of AI activities at IKEA how their knowledge graph provides a "ground reality" for the info they share enterprise uses of AI at IKEA how narrowing the scope of models to your own enterprise improves quality and reduces costs the importance of testing implementations of AI technology how their knowledge graph helps connect content across the enterprise - and offers new content metrics and analytics benefits how their systems facilitate content discovery and reuse how he uses ChatGPT to accelerate his business research his thoughts on AI technologies can add a qualitative dimension to content metrics how AI and machine learning practices may reduce the amount of data that enterprises need to collect and store how they are developing prompt engineering skills at IKEA the importance of taking a pragmatic approach to AI adoption Tane's bio Tane Piper is a self-taught software developer with over 22 years of experience. He has worked across a diverse set of environments, from startups and creative agencies to his current role as a Software Engineering Leader at IKEA. Here, Tane focuses on projects that blend content strategy, knowledge graphs, and artificial intelligence. His approach to leadership is centered on teamwork, innovation, and nurturing growth within his team. He enjoys experimenting with a wide range of technologies. He is involved in the open-source community, releasing various libraries over the year, and writing technical articles sharing his findings. When not engaged in software development, Tane can often be found in his garden, a hobby that provides him with a peaceful counterbalance to his professional life. Alongside his wife, he is also dedicated to the ethical breeding of Polish Hunting Spaniels, reflecting their shared passion for animal welfare. Connect with Tane online LInkedIn Video Here’s the video version of our conversation: https://youtu.be/9qX8fUpWFgQ Podcast intro transcript This is the Content and AI podcast, episode number 5. When you think of the iconic furniture retailer IKEA, leading-edge technology may not be the first thing that pops into your mind. But it should. Like most enterprises now, IKEA is exploring the many ways that LLMs, machine learning, knowledge graphs, and other AI technologies can help them sell more furniture and understand their business better. Tane Piper leads an engineering team at IKEA that is building their next generation of content and artificial intelligence tooling. Interview transcript Larry: Hey, everyone. Welcome to episode number 5 of the Content + AI podcast. I'm really delighted today to welcome to the show Tane Piper. Tane is a software engineering leader at Inter IKEA. And that's the first thing I want to ask you about Tane, is IKEA is this big sprawling complex organization. Tell me about Inter IKEA and how that fits in with the overall IKEA brand. Tane: Yeah. Thanks, Larry. So Inter IKEA, as many people know, you go to IKEA, you go to a shop, but what a lot of people don't know is it is actually a franchise system. So, Inter IKEA is the owner of the IKEA concept, so it owns the furniture side, the range we call it, the supply, and also the retail concept, which is where I work. So we come up with the ideas behind IKEA, how the store works, what the concept is when you go to an IKEA store, this kind of Swedishness of it all. And when you as a customer go to the store, you're mostly dealing with franchisees, so somebody who is working with us to build the IKEA brand in a new market. Larry: That's it. And having responsibility for the retail concept of one of the most iconic brands in the world, there's absolutely no pressure in this job, I'm going to guess. Tane: Oh, no pressure. No pressure at all. No. In some way, yes and no. I mean, yes, it's a big task. We are a very big, well-known brand around the world, but in insight when we're working on things, I think we're pretty normal. We talk about pretty normal things day-to-day. I mean, at the moment why I'm here with you today, we talk about things like content and AI and how we can use these to leverage them to improve not only the lives of our customers, they're many people, but also what we do day-to-day. Larry: Great. And that's the thing about AI is it's so sprawling and especially in a big enterprise like Ikea, it's going to be everywhere. Actually, I want to start because where we met, we met a couple of years ago at The Knowledge Graph Conference in New York City and we were talking then about knowledge graphs and knowledge representation and that. And we've subsequently talked about the work that Adam and Katariina are doing up in Sweden with the knowledge graph and all that stuff, but there's so much more going on. Can you talk a little bit about the scope, like there I know that they're doing the recommendation system that's driven by the knowledge graph, but there's so much else going on. Can you give a quick overview of how AI technologies are manifesting at IKEA? Tane: Oh, absolutely. I think in many layers we're looking at it. I think when you think about what AI is, I mean AI is kind of a catchall term for a lot of things. So I mean on one end you have machine learning. So where we're really looking at existing things like lots of unstructured content for example, in terms of an organization, we have lots of things in PowerPoints and PDF files that you can be understood by using something like machine learning. And then, yeah, very much on the other side with generative AI, certainly the opportunities that are there, the future opportunities of doing things like home furnishing recommendations and doing that in such a way that the machine understands the customer context, like the size of the room or where the location where they are. Tane: And can tailor and help to give the right recommendations based on that with that knowledge graph at the bottom. I think that's the thing that when we talk about AI and machine learning, especially with a lot of the LLM stuff that is coming along is we know that's very unstructured, unatributed. So by having that kind of ground reality with knowledge graph, that's really what we are looking to build upon, at least in the area where I work, with content as well. So yeah, I mean, we have a group inside where it's an informal kind of group, but we chat across multiple areas in the business and I think we're all kind of looking at how can we bring AI to different parts of the business because there are different use cases and what use case works for our area does not necessarily work for say supply. Larry: Oh, interesting. Yeah. Well I guess the one that comes to mind immediately for a lot of people, because it's been all the fuss for the last year, is degenerative AI stuff. And the famous thing about that is its proneness to hallucinations and things like that. And when you're running a brand like IKEA, it's really important to not hallucinate, and in your communication, and you talked a minute ago about the knowledge graph as the grounding your information in reality. Can you talk a little bit about that, how you can benefit from, because I'm assuming you can benefit from technologies like generative AI but still maintain your brand and maintain the accuracy of the information you're sharing? Tane: Absolutely. I think one of the things I like that what we've done is we've not necessarily rushed into building a lot of stuff with AI and really going all in with it in many ways. And I think what that's allowing us to do is as an organization and as brand look at what is it we need to do to show going forward that there isn't anything that can harm the brand perception, or harm the brand itself. I mean out there are already things like there's somebody created a model that allows you to create IKEA style manuals for things and it's a specific model to do that. That kind of stuff is very difficult to say what effect it would have on the brand, but I think as long as people understand it's not coming from IKEA. Tane: I think that's the thing is when you put a brand out there like that, the people have a bit more of that brand themselves. So I think that's the thing we have to take away from it. But obviously, there is protection of the brand, and yes we have to look at when we bring these generative AI services in, especially where people will be having conversations with them, putting those guardrails in there that mean that if you're talking to a home furnishing bot or agent, you're only getting home furnishing information. It's not going to give you information on philosophy, or science, or anything like that. It's keeping within the bounds of what should be doing. Larry: Yeah. Well, you mentioned both a minute ago when you were talking about machine learning, the benefits of learning from what you already have, and then as you just mentioned that, that kind of understanding of the world you operate in. So tell me about the relationship between understanding your current ... Because you have not only product information that we all see on the web, but there's tons of information, I forget what you call them, but the folks working in the stores and other enterprise employees at IKEA use. Can you talk a little bit about that ecosystem and how AI and ML interact as you're both understanding and generating content? Tane:

Sarah O'Keefe The arrival of AI affects every area and aspect of content practice. In the technical documentation field, Sarah O'Keefe sees three immediate impacts on the work she does for her clients: how AI agents can support technical documentation workflows, the ability to create content with generative AI, and the ways that AI is changing the delivery of technical content And wherever she looks in the content and AI landscape, she sees the need for governance guardrails and strategic thinking. We talked about: her work at Scriptorium, which focuses on scalable, efficient technical documentation her take on the current impact of AI on technical content the unique concerns about generative AI that arise in the technical communication world how chat-based user interfaces will change the delivery of technical content how users will always hack systems to use them as they wish the looming role of trust and reputation as important factors in online interactions how techniques like RAG (Retrieval Augmented Generation) can help LLM-based applications deliver better results the importance of thinking about the content life cycle as you assimilate and integrate AI into your practices and workflows a very simple AI-risk-analysis heuristic open questions - many of them complex and non-obvious - around copyright issues in the AI world Sarah's bio CEO Sarah O’Keefe founded Scriptorium Publishing to work at the intersection of content, technology, and publishing. Today, she leads an organization known for expertise in solving business-critical content problems with a special focus on product and technical content. Sarah identifies and assesses new trends and their effects on the industry. Her analysis is widely followed on Scriptorium’s blog and in other publications. As an experienced public speaker, she is in demand at conferences worldwide. In 2016, MindTouch named her as an “unparalleled” content strategy influencer. Sarah holds a BA from Duke University and is bilingual in English and German. Connect with Sarah online LinkedIn info at scriptorium dot com Video Here’s the video version of our conversation: https://www.youtube.com/watch?v=FOfdOSD8C1A Podcast intro transcript This is the Content and AI podcast, episode number 4. The arrival of generative AI, large language models, and other AI technologies obviously affects us all. In the world of technical documentation, Sarah O'Keefe sees three immediate impacts on the work she does for her clients: how AI agents can support technical documentation workflows, the ability to create content with generative AI, and the ways that AI is changing the delivery of technical content - and across them all, the need for guardrails and strategic thinking. Interview transcript Larry: Hi, everyone. Welcome to episode number four of the Content + AI podcast. I'm really happy today to welcome to the show Sarah O'Keefe. Sarah is the CEO and founder at Scriptorium, which is a company that does technical communication and documentation stuff. Sarah, tell the folks a little bit more about your work there at Scriptorium. Sarah: We're interested in the question of how do you apply systems and technology to what we call enabling content, which is technical product learning, knowledge base, all of the things that are content that enables you having purchased a product or service to actually successfully use that product or service. And we do a ton of work around content management systems, translation management systems, and basically helping companies scale their content operations into something that works. Right? Because typically, somebody shows up on our doorstep and says, "Well, we're doing it this way and we've been doing it this way forever, but this way isn't working anymore. We acquired a couple of companies. We got a lot bigger. We're doing more and more localization and we're just drowning in content, drowning in inefficient content processes. Please help us." That's our typical problem set. Larry: You're the cavalry riding in to save the day. That's great. But I think like anybody in the content world these days, there's this new kid in town, the AI, especially the large language models that are kind of... They seem to be finding their way into every corner of the content world, and the reason I wanted to talk to you is you're probably the first person I thought of when I thought of technical content. What's going on with AI in the technical world? So, a pretty broad question, but we talked a little bit before we went on the air, but I think just the context for how AI is affecting your work. Sarah: First, I'll say that, I mean, people are trying all sorts of things and experimenting and working through what does this look like and what are we doing with it and how are we going to make it work for us? Fundamentally, I think this is going to end up being a pretty straightforward tool, and I compare it usually to a spellchecker. Right? I mean, it would not occur to anybody in this day to write content without using a spellchecker. It's just part of the groundwater. It's part of the fundamental tool set, that bag of tools that you're sitting around with when you actually go to create content. So, in many ways AI is going to be that spellchecker. Sarah: It's going to be, "Hey, can you write an abstract for me? I wrote the article, but I was told to do a summary and I don't feel like doing it, so can you write it for me? Show me all the places where I've used jargon in this article that I should not have," this sort of supporting tool set that identifies patterns, good or bad, and then helps me work through cleaning up those patterns. Or, "Hey, does this follow the same pattern as that other article I wrote? Are they consistent? Show me that." You had a great example about, "Can you rewrite this in the style of Dr. Seuss," which sounds super fun and possibly not totally productive, but I'm here for it. So, those types of things. There's this idea that the AI world in general can give you the ability to take your tools or to take a bunch of tools and apply them to what you're trying to do and make it better, faster, cheaper. Sarah: Now, separately from that tool set, we've got generative AI, and I think that the technical content world is looking at GenAI a little bit differently than most of the rest of the universe because, especially if you're doing content that is compliance related, so in other words, there's a regulatory body somewhere looking at it, and/or you have products where there are health and safety implications. So, I mean, the obvious example of this is medical devices. It is important to me and to you and to all of our friends and family that the instructions for how to use a medical device are in fact correct because some very, very bad things could happen if we generate a bunch of instructions and they are wrong, and in the worst case, it could injure or maim or kill somebody, and that seems bad. Sarah: Now, we're not talking here about AI with bad intent. We're just saying that if you plug a bunch of stuff into ChatGPT and say, "Generate instructions for using this product," there's a pretty good chance that it's going to generate some nonsense. And nonsense is okay for certain kinds of scenarios, but it is not okay when I'm trying to figure out how to configure a pacemaker. I mean, that's just bad. Larry: Yeah. Although, at the same, I do want to say one thing right there. We were talking before we went on the air that one of the things, it's not like you don't rewrite it into solid Dr. Seuss, but you can sort of tailor communication. One of the beauties of the... You mostly work with DITA, this very structured content, and by having the core knowledge and information that you're working with there, you can do stuff with it. One of the examples you gave was this notion of a customer, an end user looking at this documentation going like, "Well, that's not quite how I understand or how I learn. Can you give it to me a different way," sort of the way we often will prompt to ChatGPT or something like that to rephrase things. Is that an existing use case now or something that's coming soon in the tech comms world or... Sarah: That's kind of the third use case because we've got the AI tool support use case. We've got the help me generate content use case, which is again, terrifying, but provided that you have enough guardrails around it and you have people checking what it's generating could be useful. But the third use case for particularly chatbots and generative AI is this universe of not authoring but rather delivery. So, as a consumer of the content, I might look at it and say, "Oh, I don't understand this content. It's too complicated," and I would ask ChatGPT to rewrite it for me now, into say, a seventh grade level or into simpler English, or I could tell it use simplified technical English only, and explain this thing to me. So, there's a whole bunch of stuff I could do there. Sarah: Now if I'm the producer of the information, the owner of the product who's producing all this tech comm content, then it's pretty likely that what I would actually prefer is for that content or that process to take place on my website within my guardrails and sort of my controls. So, I deliver all this content. I put it in a data store of some sort, and then I wrap a generative AI, a chatbot over the top of that, and I point it at only my approved content. But of course, what's pretty likely to happen is that your end user is going to access a webpage somewhere that you and I carefully produced and vetted and approved and et cetera, and they're just going to copy and paste it into ChatGPT, and tell it to rewrite it. Right. You really can't stop that. Sarah: So, I do think that not so much the large learning models per se,

Noz Urbina Modern content projects begin with research to create lifelike customer personas and build detailed customer journey maps. Whether you're on a tight budget or have a team of UX researchers at your disposal, AI can help accelerate and improve the development of these personas and journey maps. Noz Urbina has developed the RAUX (Rapid AI-powered UX) method to help omnichannel content strategists develop realistic personas and craft effective customer journey maps. We talked about: his work at his consultancy, Urbina Consulting, and his learning hub, OmnichannelX the RAUX AI method he has developed to accelerate user research, customer journey mapping, content design, and content development and drafting his simple equation for doling out information in complex content environments how AI can help you aggregate and understand your sources of customer information to help build personas how he looks at customer journeys and journey mapping how content fits into his customer journey maps, and how AI facilitates the tedious work that precedes and informs how to address key customer needs the AI-driven persona-development prompt methodology at the core of the RAUX methodology how to prompt AI agents in ways that mitigate the biases that often come with public data sources how you can query an AI persona that you have developed with the RAUX prompt methodology to help you fill in the details of a customer journey map how LLM's propensity to hallucination is actually a benefit when you're trying to conjure human feelings, questions, and queries how AI lets us all become programmers without becoming coders how AI can help with content creation, especially tasks like brainstorming and drafting the importance of thinking about how to use AI at every stage of the content lifecycle Noz's bio Noz Urbina is one of the few industry professionals who has been working in what we now call "multichannel" and "omnichannel" content design and strategy for over two decades. In that time, he has become a globally recognised leader in the field of content and customer experience. He’s well known as a pioneer in customer journey mapping and adaptive content modelling for delivering personalised, contextually-relevant content experiences in any environment. Noz is co-founder and Programme Director of the OmnichannelX Conference and Podcast. He is also co-author of the book “Content Strategy: Connecting the dots between business, brand, and benefits” and lecturer in the Master's Programme in Content Strategy at the University of Applied Sciences of Graz, Austria. Noz's company, Urbina Consulting, works with the world’s largest organisations and most complex content challenges, but his mission is to help all brands be able to have relationships with people, the way that people have with each other. Past clients have included Johnson & Johnson, Eli Lilly, Roche, and Sanofi Pharmaceuticals; Microsoft; Mastercard; Barclays Bank; Abbott Laboratories; RobbieWilliams.com; and hundreds more. Connect with Noz online Urbina Consulting noz at urbinaconsulting dot com Video Here’s the video version of our conversation: https://youtu.be/svFi95biaSU Podcast intro transcript This is the Content and AI podcast, episode number 3. These days, designing content experiences starts with detailed customer persona development and extensive customer journey mapping. Whether you've got a six-figure budget or you're doing scrappy do-it-yourself customer discovery, AI can help you accelerate and improve your research process. Noz Urbina has developed a detailed methodology that he calls RAUX (Rapid AI-powered UX) to help you develop realistic personas and craft effective customer journey maps. Interview transcript Larry: Hi, everyone. Welcome to episode number three of the Content and AI podcast. I'm really delighted today to welcome to the show Noz Urbina. Noz is an omnichannel strategist at Urbina Consulting, but I know you do a lot of other stuff there too, Noz. Tell the folks what you're up to these days. Noz: Yeah, absolutely. Yeah, as you said, I'm an omnichannel strategist, which involves a lot. It's a lot of customer journey mapping, a lot of stakeholder ecosystems, content modeling, metadata modeling, working with people like ontologists and taxonomists and systems architects to really build full workflows that support omnichannel. Noz: My job is I'm founder and I lead a lot of the projects at Urbina Consulting. I also have founded an organization called OmnichannelX, which was a conference but has been pivoted to a year-round buffet of learning opportunities. We used to do an annual conference, the usual four-day thing. And we found that we weren't going to be able to do physical after COVID. It was just the conference market is too difficult. And we only had one physical conference before COVID, so we never really had a chance to establish ourselves as a physical conference. Noz: We were going to do it online anyway. Why not make it an all year thing where people can come and they can pick and mix and they can go look at things from the archives and the library? We run the podcast regularly there too. It's actually doing a little bit too well because sometimes, what I'm talking in business situations, people go, "Oh, yeah, well maybe OmnichannelX can come in." And I go, "Oh, no, no, no." Urbina are the consultants. OmnichannelX is our learning hub where we try to advance the industry and provide learning resources for all the good people out there. Larry: Well, that's great. It's always good to have a little too much success, so congrats on that. Noz: Thank you. Larry: But hey, the reason I wanted to have you on the show today, this is the whole point of this new podcast is about AI and in particular using how folks are using AI in content practice. Now, my first two episodes were background setting and level setting on the whole field of AI. You're really the first person that I've had on, this is how I want the whole rest of the episodes to be like. You're out there in the world doing cool stuff with AI in your practice today. All that stuff you just mentioned, the customer journey mapping, content muddling, all those things. Tell me how... Well, you have a specific model, I know called RAUX, the Rapid AI-powered UX. Tell me a little bit about how that came to be and what you do with it. Noz: All right. Okay. You got to pronounce the cool way, which is RAUX. Larry: Okay, now I'm on board. Okay. Noz: The RAUX methodology, I couldn't resist the acronym. It's not just for UX strictly speaking. Depending on how you define UX. It's for any type of experience or content design, but it also extends into the early research stages and also the content development and drafting stages. Noz: What we found was that in order to be able to do content design, content strategy properly, we really had to do customer journey mapping. Because if you Google customer journey map, you get some very disappointing diagrams which are aligned with a couple boxes superimposed over it. And so I get a newsletter or see an ad and I click a landing page and I download a thing. And it's just this happy path of contact points and they put some notes on them. Noz: That's really inadequate for content people. If we're really trying to get into the informational needs and the informational journey and requirements of our people, the level of customer journey mapping that we were getting from your usual UX design process was not what we needed. Noz: What we've started doing at Urbina Consulting, because we're an omni-channel consultancy, is we're trying to put together a methodology which works for everybody. You can just use it for designing product, you can use it for designing digital experiences of any kind. We've come up with integration requirements. And for example, we realized that we needed to pull this data from the CRM, so in real time we could show on the website what was happening in the call center. Which is not your usual kind of content requirement, but it comes out through properly analyzing the journey. Noz: But it's a lot of work. What RAUX is about is using AI to accelerate that process. Taking the research you do have or starting wherever you are and using AI at several points in the whole content life cycle from research all the way out to delivery. Larry: Nice. And I know I do a little bit of content modeling and journey mapping myself. And typically, especially in journey mapping, that is one of the, we're all used to these giant grid things. We all work in spreadsheets all the time. But a good journey map is the ultimate, giant deepest spreadsheet you've ever worked with. And there's so many cells to fill and this is what this helps with. Right? Noz: Yeah, exactly. What we like to do is have a decent narrative. We want people to have a story that's told in the first person. We don't do, the user does this and then they click here and then they do that. Because I've literally found this in workshopping. When you speak about the user in the third person, you start to objectify them. Noz: That if you really want to drive empathy in the team when they read the narrative, they should be role-playing in their heads. They should be living the story that this person is going through. That's, the writing of that is a bit of an art. You have to be able to empathize and you have to be able to think in the first person and get yourself out of your own head as the person who might own these assets that are in question or be very close to the problem. Noz: That's a decent amount of work is also the figuring out the questions over time. Our journey mapping methodology at its simplest, you were saying there's all these cells, all these rows. At its simplest, it's literally just that.

Paco Nathan Most of us are learning about AI on the fly and just got started in the past year or two. Paco Nathan has been working with AI since the 1980s and has been doing digital business nearly as long. His background in both the technical and commercial sides of artificial intelligence gives him a unique perspective on the field that can help newcomers like me and you get oriented to this new landscape. We talked about: his extensive history in the AI field, including work with some of the earliest chatbots how graphs can serve as a way to ground and contextualize unstructured content how content that is structured properly can help help users and drive action the tech stack underlying the current generation of AI tools two technologies at the base level of the stack: sequence-to-sequence and diffusion the benefits of SSM, small specialized models, over LLMs his take on the impact of LLM chat agents on content and editorial practice four take-homes from his recent immersion in AI conferences and gatherings: the superiority of small, specialized learning models (SSMs) over LLMs the issue of losing domain knowledge as experts age and retire the importance of using your own data sets the need for detailed task analysis as you begin building any AI model the contrasts and interplay between AI developments at large, well-funded entities like Alphabet, Meta, and Microsoft and the smaller, more diverse ecosystem around open-source AI projects Paco's bio Paco Nathan, Managing Partner at Derwen, Inc., and author of Latent Space, along with other books, plus popular videos and tutorials about machine learning, natural language, and related topics. Known as a "player/coach", with +40 years tech industry experience, ranging from Bell Labs to early-stage start-ups. Werner Herzog is his spirit animal. Board member for Argilla.io; Advisor for KUNGFU.AI. Lead committer on PyTextRank, kglab. Formerly: Director, Community Evangelism for Apache Spark at Databricks. Long, long ago, when the world was much younger, Paco led a media collective / indie bookstore / performance art space / large online community called FringeWare. Beginning in 1992, this was one of the first online bookstores and likely the first commercial use of a chat bot on the Internet. Connect with Paco online Derwen.ai Argilla.io Video Here’s the video version of our conversation: https://youtu.be/bjU_q36cggw Podcast intro transcript This is the Content and AI podcast, episode number 2. You'd think from news stories and social media that AI is mostly about large language models like ChatGPT and big companies like Microsoft and Google. In fact, there's a large, well-established community of open-source AI projects and a variety of technologies in addition to LLM-based chat agents. With more than 40 years of experience in artificial intelligence and in the tech business world, Paco Nathan is uniquely qualified to orient us in the current AI landscape. Interview transcript Larry: Hey everyone, welcome to episode number two of the Content and AI Podcast. I'm really happy today to welcome to the show Paco Nathan. Paco, we could talk literally for 20 hours about this stuff we're going to talk about today. But what Paco and I are going to talk about today just kinda get you grounded in making sense of the AI ecosystem. Paco's been doing this stuff forever. He's studied AI back in the, what, the 80s or something like that. Anyhow, welcome, Paco. Oh, and one last quick thing. Paco is the managing director of Derwen.ai, his consulting company. So welcome, Paco. Tell the folks a little bit more about your work at Derwen.ai and some of your discoveries around AI lately. Paco: Oh, fantastic. Thank you very much, Larry, I really appreciate it. Yeah, Derwen, we're really focused on open-source integration to support machine learning in general. But we focus a lot on natural language and graph technologies. And for what it's worth, I got into doing graph work, which is how we met, I got into that because of natural language. I was working with a family of algorithms. There's some research that had come out of, basically, taking a raw text and being able to start to put structure into, and turn it into a graph by using natural language. Paco: So we ended up using, like I say, these kinds of technologies, mostly in open-source, for enterprise customers. Really, to help power, help them build applications of knowledge graph, and now large language has become very popular. And, been doing this for a while. Paco: One of the projects I'm involved with, there is an open-source project called Argilla, based in Spain. I'm in Spain right now, actually. We started six years ago in natural language, when some of the first open-source large language models were coming out, LLMLP templates, things like that. Argilla has been doing a lot of those open source integration paths with Spacy and other kinds of NLP projects. But using them in enterprise, like I say, for the past six years. It's been interesting because, five years ago, we decided, we made a business decision to focus on large language models in enterprise business use cases. Back then, people would be like, "Language models? That seems very narrow. Why do you want to focus on this?" Larry: Yeah, but you're not an "I told you so" kind of guy, but you still must have some fun with it. Yeah. Well, that's great. Larry: And what you were just saying, too, about the natural language stuff and the graph stuff, there's this ... One of the things we've seen lately is the large language models, which are notoriously hallucination prone and not very bright, being merged with techniques like retrieval augmented generation to access a knowledge source, like a knowledge graph or something like that. Paco: Like a knowledge graph. Larry: Yeah. So that gets into the ecosystem part of this. It's not ChatGPT all the time. As you just said, you've been doing this way before they came on the scene. I'd love to get just your quick overview of that ecosystem. There's the natural language, ML flavored stuff, the knowledge representation stuff. What else is relevant, especially in terms of content practice, do you think? Paco: Well certainly, we can also talk about the ecosystem more, but let's first focus on where the building blocks are. Obviously, a lot of people are interested in chat, we'll touch on that later. You know, I've been working with chat apps for a long time. Going back to the early 80s, that's what we used for our class projects. I used to TA a course that Andrew Ing eventually took over and made popular. But we would teach Eliza to people doing chatbots, back in the early 80s. Paco: Yeah. Not all the world is chat. There is a lot of the world that has to do with text, and images, and video. And a lot of the text is structured in ways ... For instance, we work a lot with manufacturers here in Europe. And you might think that manufacturing data is all about process controls, and factories, and automation. It's not. The stuff that we work with, and so much of the important data is all PDF documents. Because you've got patent applications from your possible competitors, you've got environmental impact reports where your competitors might disclose. You've got scientific papers are being published that you have to keep up on. You've got your regulatory norms that you're publishing to European Commission, or whatever. And, just on, and on, and on. You end up with hundreds of millions of PDF documents. Paco: To be able to use those, you can't really do much with that in a data lake, you have to process it. So you need to use NLP to extract out the information and the relationships. And then, the next step is gosh, this is all linked. The scientific papers are referencing things that are also in the patent applications, and that has a lot to do with our competitors' factories. And by the way, if we've got thousands of vendors in a network, at the end of the day, you end up with a very large graph. This is how you make sense of it. This is how you rationalize it, is by grounding in a graph. Which is, like you say, with retrieval augmented generation, yeah, people are realizing, "A knowledge graph might be good for grounding our data." Larry: Yeah. And the way you just talked about that, too. There's so much of the fuss in the content world is around generative AI, and just creating content. But you just talked about just one use case for the understanding what you've got already. There's huge power in that. In fact, I just saw a paper the other day where somebody ... I don't know exactly which technology there at play, but a lot of the technologies just take random 500 character or 500 word chunks of a document. Paco: Right. Larry: And this was a new technique to take the inherent, implied semantic meaning of headings in a PDF doc and do the chunking that way to get better results. Paco: Oh, yeah. Larry: But that's just an optimization of this kind of stuff. So there's both the analytical understanding part of what you go. When I look at a lot of those PDFs I'm like, "Oh, for crying out loud. Why didn't you hire me 10 years ago?" I think this might get to what you're talking about with those knowledge graphs, that having a structure, and meaning, and semantic attributes of stuff - before you create it, that's just a pet project of mine. How can AI help with that kind of stuff? Like workflows, maybe, around that. Paco: Yeah. Well, it really cuts both ways. And actually, I'm here in A Coruña doing a talk about this. It's about this intersection of graphs and language for industry AI applications. So it's really cutting both ways. Paco: On the one hand, usage, it's good to be using graphs to organize things because, when you think of ChatGPT or any of the chatbots,

Dan McCreary Dan McCreary has years of experience selling AI solutions to executives. He uses a metaphorical story to show the importance of making your enterprise as intelligent and nimble as possible. His story of the the evolutionary heritage of jellyfish and flatworms seemed to me like a great way to kick off this new podcast. We talked about: the importance of helping an executive audience visualize the benefits of any technical solution, in particular the role of storytelling that will help your message stick the jellyfish and flatworm metaphor that he uses to help executives visualize their competitive environment how a knowledge graph lets companies build internal maps of their company and environment how a knowledge graph can enable micro-personalization how adding precision to a model improves your ability to predict customer behavior his simple description of embeddings: a way that we find when two things are similar his take on the benefits of labeled property graphs over knowledge graphs the idea of "reference frames" articulated by Jeff Hawkins and how knowledge graphs come closest to modeling them how three main ways of representing data - neural networks, knowledge graphs, and reference frames - are all based on graph network models the importance of freeing data from spreadsheets to enable the full productivity benefits of AI his insight that knowledge representation is the hardest part of AI Dan's bio Dan McCreary is a solution architect focusing on AI and generative AI architectural patterns. In the past, he worked at Bell Labs with the creators of the UNIX operating system, with Steve Jobs at NeXT Computer, and founded his own consulting firm with over 75 employees. His background includes topics such as scale-out enterprise knowledge graphs, high-performance computing, and NoSQL databases. He is the co-author of the book "Making Sense of NoSQL" and is a frequent blogger on AI strategy. He has been closely following the growth of knowledge graphs and generative AI. He is a huge fan of GPT-4. Connect with Dan online: LinkedIn Video Here’s the video version of our conversation: https://youtu.be/SwK73iQ7_j8 Podcast intro transcript This is the Content and AI podcast, episode number 1. As I was getting ready to launch this new show, Dan McCreary shared on LinkedIn a story that he uses to help executives understand why they need a smarter approach to their data and knowledge management. I always appreciate a good origin story - especially when I'm in the process of starting something new - so his comparison of the evolutionary heritage of jellyfish and flatworms resonated with me. I hope you like the story, too, as well as Dan's take on knowledge representation, which he thinks is the hardest part of AI. Interview transcript Larry: Hey everyone. Welcome to episode number one of the Content and AI podcast. I'm delighted to start off the series with Dan McCreary. Dan is an AI consultant based in Minneapolis, Minnesota in the US. Welcome to the show Dan, tell the folks a little bit more about what you're up to these days. Dan: Thank you very much for having me. I have been working on the field of knowledge representation for most of my career. My background is - early on I did chip design for Bell Labs. I worked in the super computing industry, worked for Steve Jobs for a couple of years, and then I've been doing a lot of starting my own companies and consulting. And then I just recently left a Fortune five healthcare company where I ran a generative AI center of excellence there. Larry: Nice. Yeah, so you've been doing this stuff for a little while and that's why I wanted to start off. I've been thinking of launching this podcast for a couple months now, and I saw this article that you wrote that said, "Aha, there's my trigger here. Let's go." You wrote this brilliant piece that you've been kind of shopping around because just for everybody's background, Dan has been explaining to executives for decades about how to get the most out of computers and computer stuff. And his latest thing - as an AI consultant - is helping people understand where to make smart investments in AI. And so he's been looking for ways to explain that he came up with this brilliant metaphor of the jellyfish and the flatworm. Tell us about that, Dan. Dan: Well, first of all, anybody who's in technology and has an intimate understanding of how bits move across networks and then goes into a room full of executives who maybe have a finance background or a healthcare background but can't visualize the difference between two databases, you can get very quickly frustrated trying to guide them. And one of the things that I've always learned is that if your audience can't visualize what you're trying to explain, they won't make the right decisions. AI today, say your executives are pondering a million or a 10 million or $100 million investment, they have to be convinced that it's the right thing. And what's interesting is they'll often have you in a one-hour consulting meeting and then they're going to go away. And the question is what will they remember? They're not going to remember data and facts and bites and bits and all this stuff, but they will remember a story if that story attaches to their emotional memory. Dan: We think of emotions attaching memory to our brain. And so the idea here is to develop a compelling set of stories that when you're not in the room, they can talk about it and they can say, "Hey, are we a jellyfish or are we a flatworm?" And then they'll make the right decisions if you give them the right metaphors, right? So that's the whole thing about being a good thought leader is having really good stories. By the way, they have to be accurate. You can't just make things up. They have to be able to talk to their friends and say, "Hey, do you understand Dan's jellyfish flatworm story?" And they have to say, "Yes, that makes sense," but well, let me just tell you the story real quick. Okay. So in the evolution of animals on planet earth, about 600 million years ago, we had two animals. Dan: One, the jellyfish and the jellyfish live in a very simplified open ocean environment and they have to have a very simple set of rules. And they just hope that by following those rules go towards light, go down in the dark if there's prey around very simple rules to hope that fish wander into their tentacles. On the ocean floor, though, the world was very different where these flatworms were crawling around and they had to know how to move towards their prey or they were often considered the first hunters and also avoid their predators. So they had to remember things and they had to remember maps of where the good places to go and where the bad places code. And they had to have complex rules so that if they turn around, they're going to move away from their predators. So motion is complicated. It really tells you about you have to have a map of the world around you. Dan: That's the flatworm. And most scientists say that the flat one was probably the very first animal to have a central nervous system. And most of other animals that move around their environment evolved from that. So I use that as a metaphor for asking companies to understand, are you floating in a competitive environment that's simple? Do you sell one product to one consumer and you have no competitors? Right? And there are a few companies that do that, right? They're specialized manufacturers, they make one part, they sell it to another manufacturer, they get the same contract every year. They're very good at what they do. They're so specialized, they don't have competition. They have a simple world. They don't need to have a huge massive IT department to simulate their competitive landscape. But in the real world, you have many products. You often sell to many different types of consumers, and every one of your products may have a hundred different competitors. Dan: That's a complex world. That's not like a simple jellyfish that floats in the open ocean. That's a flatworm company, a company that has to start to take an understanding of the world around them and build internal maps. And by the way, there are companies that do have IT systems. You've got an IT system that runs your website, it has a log of who's coming to your website. There's a search field you can store who's searching for what on your website. You have your customer relationship management system, you have your sales system, you have your commission system, you have your product management system, you have your inventory system. You got all these systems, and they're all little silos. And what we've learned is that if you want to have intelligent agents that help knowledge workers make decisions, putting a hundred data silos in the cloud is not going to help you build intelligent agents that are helping your knowledge workers get their jobs done. Dan: What we need is to be like the flatworm where we centralize knowledge in a brain, our central nervous system as it were. And the manifestation of that is a knowledge graph where you can model the complexity of the real world in all of its detail to make good decisions and make good predictions about, hey, if I introduce this product, this is going to be a change in revenue. If I see this change happening in these products, here's my prediction of why it's happening. All of those things, if you have all your data spread across these silos, you're not going to be able to have intelligent agents. So the way I say this is that if you want to be a flatworm company, you need to centralize your knowledge in a knowledge graph and you need to use all of the power of modeling the outside world to precisely predict your customer's behavior. Does that make sense? Larry: That makes perfect sense. And what I love about one thing,

Update 11 November 2023: I've talked with a lot of people, both interviews for the podcast and informational chats, over the past few weeks and have made some interesting discoveries. So, in addition to helping us all understand AI and how to use it in our work, I'm adding to this podcast's scope coverage of people working in content roles on AI products. Like any other software, AI products need content strategy, content design, UX writing, technical documentation, etc., and we'll hear from those folks soon. Here's the video version of this episode: https://www.youtube.com/watch?v=5qAfH0_0h5I Episode transcript Welcome to the Content and AI podcast. This is episode number 0, an introduction to the show. This episode is just me talking about my intention and plans. Going forward, it will be conversations with experts on both AI and content practice. My intent with this new podcast is twofold: one, to demystify the family of technologies and practices known as artificial intelligence and, two, to democratize the use of AI across the span of content use cases, everything from research and discovery, to content creation and authoring, to content design, content engineering, and content operations. All the stuff we do. I'll talk to folks in the AI field of course - and at first that will largely be a bunch of old white guys, which in itself points to some of data sampling and bias problems that AI practitioners face. But I'll also talk to a diverse range of content practitioners working in product content, support documentation, conversational design, website content, marketing content, content-marketing content - anyone who's adding AI to their digital content workflows - which is pretty much all of us at this point. We've already seen the applications of AI all over the place: auto correct and auto fill in forms digital assistants like Alexa, Cortona, Bixby, and Siri search engines social media feeds personalized content in advertising and on websites and digitial products recommendations from ecommerce merchants robots on assembly lines fraud prevention drug discovery medical diagnosis generative AI, the computer-generated text, and image, and videos that are flooding your in box and social media feeds We'll go under the hood (or as they say in England, the bonnet, I'm recording this in London) - we'll go under the hood, behind the scenes top look at the scope of AI. Not all agree on the precise scope - but we'll look at topics like: NLP, natural language processing, and its applications in areas like conversation design machine learning - statistical modeling of data - embeddings and vectorization and predicting which words come next knowledge representation - bringing real-world facts to the table, which we're already seeing with practices like retrieval augmented generation (RAG) neural networks - machine-based augmented decision making expert systems - rules-based ways to augment human decision making since the 70s computer vision robotics AI ethics and Silicon Valley hype To that last point, we'll pay attention to folks like Timnit Gebru and her collaborator Emily M. Bender. Timnit Gebru is the AI researcher who was fired from Google for pointing out the shortcomings in their approach. She and Bender coauthored the now-famous "stochastic parrots" academic paper. And one of my early guests, one of those old white guys, a delightful and remarkably accomplished human named Paco Nathan - will help us see the current state of AI through lens of an industry veteran with deep deep deep experience in the technical foundations of AI and a ton of experience in the tech startup world. So we'll try to balance the tech hype coming out of Silicon Valley. But we can't and won't ignore that hype - regardless of its merits, they've got the attention of executives and decision makers and the media, so we'll definitely keep an eye on the the big players in the AI space like OpenAI, Anthropic, and Google's Deepmind, and show you how to best use their products. Finally, we'll keep on the radar screen the concept of art general intelligence (AGI). But my main intent is to democratize AI technology to help content practitioners understand and use AI as expertly and efficiently as possible (edit: and to help content practitioners working on AI products). We've already seen many ways that AI can help content folks: content creation - relieving the terror of the blank page, tedious outline tasks, research, etc authoring, enterprise UX, auto- and assisted tagging, voice, tone, and style governance, creating content variants, repurposing existing content, etc. strategy formulation content design content engineering content operations AI's going to be able to help us across the span of content practice. No matter what kind of content work you do, you'll soon be using AI in any number of ways (edit: and you'll likely be helping to design the next generation of AI tools). Anyhow, welcome to the Content and AI podcast. I and my guests are here to help you navigate this dynamic new landscape and to use AI effectively in your content work. If you're doing interesting work with AI - and your boss will let you talk about it - DM me on LinkedIn - I'm always happy to chat about how we might get you on the show, or just chat about AI.