Data Science Leaders

When Cindy Tu first stepped onto a conference stage, it wasn’t part of a long-term plan. It was a turning point. A single speaking invitation shifted her role from quietly reviewing AI systems to actively shaping how governance is practiced across financial services. With a background spanning IT, data, and audit, Cindy brought a rare systems-level view to the table.Now a rising voice in enterprise AI risk, she’s influencing how institutions think about oversight, how governance frameworks evolve, and why people are at the heart of successful implementation. Her perspective is informed not only by technical expertise, but by lived experience.Cindy brings:How to frame governance as an enabler and not a gatekeeperWhy third-party risk keeps rising as gen AI adoption acceleratesHow to flip governance from the “no” guys to the “yes” guys

When Shub Agarwal joined an early conversational AI startup, he was building products in uncharted territory with emerging technology few had heard of. By late 2019, Google and Meta were aggressively recruiting him. But a sudden personal loss made him rethink his priorities, leadership, and the impact he wanted to create.From fast-growth startups to a financial regulator, he's codified a nine-step AI product framework, teaches at USC, and developed practical approaches to trust, governance, and AI agents in organizations. His story reflects intentional leadership in a rapidly evolving field.You’ll hear:How leaders should think about AI agents as part of their teams and workflowsWhy stepping back at key moments makes you a better leaderHow to evaluate AI systems beyond performance metrics

Daniel Kraft, physician-scientist and founder of NextMed Health, joins Domino’s Chris McSpiritt to discuss how AI, data convergence, and systems thinking are accelerating healthcare’s shift from reactive treatment to proactive, personalized care. With a background spanning regenerative therapies, digital health, and aerospace medicine, Daniel offers a wide-lens view of the challenges and opportunities shaping the next era of clinical innovation. From the limitations of “sick care” to the promise of personalized poly-pills and AI-powered digital twins, this conversation explores how to unlock proactive, personalized, and accessible healthcare at scale.Join us as we discuss:How exponential tech is enabling predictive, preventative care and what’s slowing it downWhy “data-to-action” is the next frontier for AI in life sciencesWhat data scientists and tech teams can do now to drive clinical impactGet more of Daniel’s insights at DanielKraftMD.net.

As organizations race to become AI-driven, data has emerged not just as a resource, but as a strategic advantage. In this episode, Kjell Carlsson sits down with Doug Laney, author of Infonomics and Data Juice, to explore how generative AI is accelerating the journey toward data monetization and organizational autonomy. Doug shares why traditional approaches to data management no longer suffice, how digital twins and agentic AI are redefining business models, and why the future may belong to self-driving organizations.Tune in to hear:How AI is reshaping the economics of data and enabling new business modelsWhy organizations should embrace agentic AI and digital twins to stay competitiveWhat leaders must do today to build autonomous, AI-native enterprises

AI strategy isn't just about technology. It's about transformation.In this episode, CDAO expert, Dr. Shahram Ebadollahi, joins to discuss what it takes to lead AI in an enterprise and why that leadership must go far beyond just selecting the right tools or models. Drawing on his experience building AI teams, Shahram explains why the Chief AI Officer role is one of the most complex, and most critical, positions in modern organizations. Here we talk about the cultural, organizational, and strategic shifts needed to embed AI deeply and responsibly, and how to move from experimentation to value delivery at scale.Whether you're leading AI or supporting it, this conversation offers a clear-eyed view of what success really requires.Tune in to hear:Why the Chief AI Officer must balance tech depth with business visionHow to structure teams and incentives for lasting AI successHow to move from AI experimentation to real business impact

What does the global race for AI look like? How has the US Department of Defense been adopting AI by engaging the private sector? And how should we expect AI policy to shift under the new administration?  In this episode – the last in our series on the future of AI – guest host Joel Meyer, President of Public Sector at Domino, sits down with General Jack Shanahan (Ret.) former Director of the Joint Artificial Intelligence Center at the U.S. Department of Defense, and Lauren Bedula, Managing Director at Beacon Global Strategies.Join us as we explore:How the U.S. Department of Defense is accelerating AI adoptionThe global AI race: U.S., China, and alliesAI policy shifts under the new U.S. administrationPredictions for AI in the Public Sector for 2025 and beyondAlso, hear more of Lauren’s thoughts on national security and tech innovation on the Building the Base podcast.

What does the near term future of Enterprise AI look like? A scramble to get valuable, cost-effective, enterprise-grade AI solutions into production, with rigorous governance at scale. If that sounds like a tall order - that’s because it is! However, large advanced organizations (yes, even outside of the tech sector) are already seeing success deploying AI solutions and we can expect the pace to accelerate rapidly in the next 1-2 years.To find out how enterprises are gearing up for this challenge we speak with Richard Swakla, AI and ML Specialist at NetApp and get the inside scoop on the trends that are underway and the strategies organizations are putting in place to operationalize meaningful AI outcomes.  Join us as we discuss:The pressure to shift from experimentation to productionBreaking down the data, infrastructure and organizational silosBest practices for tackling governance, security and cost challengesExamples of AI efficiency gains in healthcare, pharma, and beyond

AI is transforming financial services and insurance, but rarely in the headline-grabbing ways you might expect. From intelligent process automation to fraud detection and risk management, AI is being embedded across operations, but as AI adoption grows, so do the challenges—cybercrime, rapid technological changes, and ever-evolving regulatory frameworks.In this episode we sit down with Adam Gale, Field CTO for AI & Cyber Security at NetApp, and Mike Upchurch, VP of Strategy for Financial Services and Insurance at Domino, to explore:The "silent revolution" of AI in financeUnlocking the potential of unstructured dataAI governance for innovation and regulatory complianceStrategies for driving AI adoptionTop predictions for AI in 2025 in FSI

AI is transforming drug discovery and clinical trials, making what was once cutting-edge the new standard. Pharma companies are evolving into tech companies, leveraging AI to revolutionize every phase of drug development.In this episode, Chris McSpiritt, VP of Life Sciences Strategy at Domino, discusses how AI is no longer a niche capability for select firms but an essential capability for all biopharma organizations. We explore AI-based digital organisms, the increasing reliance on AI in clinical trials, and the shift toward personalized medicine.Join us as we dive into:How AI will drive the majority of new drug discoveriesThe role of AI in clinical trialsThe growing need for pharma companies to adopt a tech company mindsetThe impact of AI-driven automation on regulatory compliance and reporting

What’s next for artificial intelligence in 2025? In this episode, Dr. Kjell Carlsson delivers his annual predictions about the immediate future of AI. From AI becoming the universal corporate scapegoat to the rebranding of generative AI as "agentic AI.” Discover why commercial AI solutions remain scarce, the pivotal role of workforce upskilling in achieving transformative AI outcomes, and how governance is shifting from regulation-focused to impact-driven.Want to see how Kjell fared in his 2024 predictions? Look no further than Data Science Leaders podcast Episode 62: AI in 2024: Predictions on the Future of the AI Revolution.

Innovation and AI governance aren’t at odds. Done properly, governance practices can be the key to accelerating implementation of even the latest GenAI use cases. In this interview from RevX London with Raj Mukherjee, Head of Data Science and AI at Direct Line Group, we find out how they embraced the principles of a lean startup, adopted a product mindset and became the first major insurance company to implement a GenAI solution to accelerate customer service. Come find out how they were able to overcome the challenges of legacy infrastructure, build trust and do the seemingly impossible — make compliance fun.

How can organizations drive transformative AI innovation while effectively managing its inherent risks? Is governance a bottleneck or can it become a catalyst for AI success?In this webinar, we explore the critical role of AI governance with Brandon Purcell, VP Principal Analyst at Forrester Research, and Theo Linnemann, Data Scientist at the Federal Reserve Bank of New York. Together, they share actionable insights into the principles, practices, and policies necessary for ensuring the safe use of AI that drives real-world impact.Join us as we discuss:The evolution of AI governance and why it has become a top priority for AI teamsFrameworks and practices for identifying and managing AI risk The growing importance of automation for effective governanceFairness, business, and other metrics for AI governanceHow to govern an Agentic AI future 

This episode is a collaboration with the Ethical Machines podcast featuring Reid Blackman — CEO of the AI ethical risk consultancy Virtue — and Nick Elprin, cofounder and CEO of Domino Data Lab. Join us as they discuss:The differences between AI ethics and AI governanceHow the AI governance challenges have changed (and where they haven’t)The people, process and technology solutions for genAI governance (and how they are all intertwined)Predictions about AI maturity, slip ups and regulation

Think you have a hard time driving AI adoption in your organization? Try driving AI transformation in the largest, most regulated organization on the planet – the Pentagon. In this episode, we sit down with Dr. Chris Kirchhoff – co-author of Unit X: How the Pentagon and Silicon Valley are Transforming the Future of War and former Director of Strategic Planning for the National Security Council – to uncover how the Department of Defense is accelerating the adoption of AI. Dr. Kirchhoff shares invaluable lessons on managing disruption and fostering innovation at scale.Join us as we discuss:How leadership and experimentation drove the success of the Defense Innovation UnitThe importance of developing new frameworks for procurement for AI adoptionInsights from modern warfare: drone technology and the shift to consumer-driven innovationBest practices for scaling technological transformation across any large organization

The EU AI Act, is it the first step towards comprehensive AI regulation that will make us all safer, or is it the scariest thing in AI today, or both?In this episode we speak with Adam Gale, Field CTO for AI and Regulatory Compliance at NetApp, to demystify the EU AI act and discuss future-proof strategies to ensure compliance.Join us as we discuss:The core requirements of the act and how they impact your use casesGray areas to look out for in the actCompliance with existing regulations such as DORA and GDPRCore governance capabilities that enable compliance for the EU AI act and future regulation

AI governance is no longer a hypothetical consideration—it's critical not only to meet regulatory requirements but also to build trust, drive adoption, and deliver tangible impact with AI. In this episode, we bring together experts Jared Vaudrey and Dr. Dylan Bobby Storey, who have spent years developing AI solutions in heavily regulated industries. They share their hard-won insights on the realities of implementing AI governance across a wide range of organizations and use cases.Join us as we discuss:The value of AI governance for driving adoption and innovationHow governance principles apply to traditional ML and genAIThe common challenges of governance Real-world examples and best practices from advanced teams integrating AI governanceHow automation helps scale governance and provide the flexibility to support future regulation

AI governance is more important than ever, but confusion reigns about basic questions such as: what it is, why it is important and what we should do about it. As an AI, data science, or business leader, you need to dispel these governance misconceptions in order to manage AI risk and ensure the safety and reliability necessary to drive adoption and impact. The ability of your organization to drive impact with AI, and potentially your career, depends on it.Join us as host Kjell Carlsson, gives an opinionated take on the basic questions of AI governance:Why you should care about AI governance (and why it goes beyond ethics and regulation)What AI governance is (and how it differs from ethical AI, responsible AI, and trustworthy AI)Why AI governance is difficult (and the gap between frameworks and practices)What AI governance looks like in practice (across the AI/ML lifecycle)What we need for AI governance (and the capabilities to do it at scale)   

Data privacy - why is it so important in the age of AI, why is it so difficult, and what should we be doing to improve it in our organizations?In this episode we speak with Dr. Rebecca Balebako, privacy engineer and Chief Privacy Officer at Lotic.ai, about why AI makes privacy more important than ever, the common misconceptions around data protection, who should own privacy, and the benefits and limitations of privacy enhancing technologies (PETs).Join us as we discuss:The value of privacy (~5% of annual profit)The many definitions of privacy.The top 3 misconceptions: no one cares, it can be fixed with a privacy policy, and privacy is a blocker.Who should own privacy in your organization.PETs vs. data hygieneFor more on Rebecca’s consulting on privacy strategy, coaching and how to build a privacy advocate team see https://www.privacyengineer.ch/ and for more on privacy methods check out the book “Practical Data Privacy” by Katharine Jarmul.

What capabilities do you need to take advantage of AI and what changes will you need to make to your IT architecture? Well, let’s look at the data.In this episode, Shawn Rogers, CEO and Fellow at BARC US, shares the results of their survey on how enterprises are optimizing their architecture for AI innovation. Shawn unpacks data on everything from the biggest obstacles to delivering AI impact to how companies are sourcing their AI capabilities. Join us as we discuss:The AI talent gap and the strategies that firms are using to close itThe myriad AI capabilities that high-readiness firms are implementingThe need to manage AI costs upfront to ensure deploymentThe perpetual question of build vs. buy (or both!)The crucial need for AI governance to ensure approval and adoptionDownload the full report here.

How do you drive digital strategy and transformation with AI? Do you need an AI strategy or a business strategy that intelligently leverages AI?In this episode, we delve into the challenge of driving transformation with AI in insurance with Fu'ad Butt, VP Head of Digital Strategy and Automation at OneAmerica. Fu’ad shares his best practices for identifying and executing AI projects. These range from how to identify the most promising use cases (hint: focus on augmented intelligence, but tie it to business value) to executing them successfully (build a test and learn process, and use multifunctional pods).  Join us as we discuss:The role of AI in digital strategy today.Overcoming the challenge of aligning AI to business value.Experimenting efficiently with digital twins and a contrarian in the loop.Breaking hierarchies with multifunctional pods for faster impact.

AI is disrupting marketing, but the biggest threat isn’t AI systems misbehaving, it is the unintended consequences of AI systems performing exactly what they were intended to do.In this interview with Dr. Daniel Hulme, Chief AI Officer at WPP and CEO of Satalia, we discuss the ways that AI is transforming marketing – from accelerating content creation and maximizing activation to exploring the creative landscape and creating “brains” that ensure it is responsible and legal. Also, tune in for fascinating discussions of AI consciousness and what it means to be a Chief AI Officer.    Join us as we discuss:The greatest GenAI opportunities in marketing and beyondHow to maximize AI impact with decision optimizationResponsible AI and the challenges of AI systems going very rightThe emerging field of AI consciousnessThe Chief AI Officer: why you need one and the prerequisites for success  For more information about the new research organization focused on AI consciousness co-founded by Daniel Hulme see conscium.com and his interview on the London Futurists Podcast.

How do you deliver impact with AI and ML and cut development time by weeks and even months? By understanding your customer, building trust, and managing risk. Done well, effective and responsible AI practices can be the secret to faster implementation, adoption, and performance at lower cost and risk.In this episode with Dr. Alex Manasson, Data Science Leader for the Americas at International Flavors and Fragrances (IFF), we uncover their best practices for managing risk and driving rapid AI development and adoption in the safety-focused world of manufacturing.. Dr. Manassof shares insights on balancing statistical process control with predictive modeling, the importance of adapting your data collection processes, and the pros and cons of digital twins. Discover practical tips and strategies for implementing AI and ML tools to boost efficiency and foster trust in high-stakes environments. 

How do you deliver value with responsible AI, who is responsible for it, how do you put it into practice, and could we use AI to make our organizations more ethical?  This episode comes to you from the RevX conference in London, where we asked these questions of Chris Wiggins, Chief Data Scientist at the New York Times. He is also Professor of Applied Mathematics at Columbia University and author of the books “How Data Happened: A History from the Age of Reason to the Age of Algorithms” and “Data Science in Context”.Join us as we discuss:What we can learn from the history of research ethics and data legislationThe need for clear principles and defined ownership to ensure ethical AIThe translation of ethical principles into checklists, standards, and product decisionsThe importance of benchmarking AI against human performance and addressing how human biases in data lead to biased AI outcomesTo see all of the sessions at the RevX conferences go to domino.ai/revx. 

AI is not all about the data, however, your ability to develop and deploy efficient data pipelines is absolutely critical for unlocking the power of AI at scale. But how do you manage modern data pipelines for AI and how do you deal with fragmented ecosystems and spiraling costs? In this episode, brought to you from the RevX Philadelphia conference,  Richard Swakla, AI/ML Specialist at NetApp, joins us to discuss the current trends and best practices in the life sciences around data and AI. Join us as we discuss:The role of AI in enhancing productivity in healthcare and the life sciences, particularly in drug discovery,  claims processing and fraud detection.The growing importance of hybrid cloud solutions to balance cost, efficiency, and infrastructure access.Challenges in transitioning AI projects from pilots to production due to high costs and rapidly evolving models.To see all of the sessions at the RevX conferences go to domino.ai/revx. 

How do you enable AI, data science and analytics on petabyte-scale data, with extremely stringent privacy and security requirements?This episode comes to you from the RevX-New York conference where we had a fireside chat with Ivan Black - Director in charge of ML, AI, and analytics platforms at the US financial services regulator FINRA. Join us as we discuss:The challenges of enabling AI on massive, rapidly growing financial datasetsTalent strategies to support the rapidly changing AI ecosystemThe importance of AI governance and reproducibilityManaging cloud costsTo see all of the sessions at the RevX conferences or to find information about attending upcoming ones go to domino.ai/revx.

How do you craft and implement a strategy to transform an organization with AI? Not just to build a growing portfolio of successful AI projects, but to fundamentally re-engineer the organization’s core processes, to radically increase productivity, to overhaul the company’s tech stack, and to prepare it for a future of AI-driven competition.In this episode, Akshaya Murthy, who leads the AI efforts for Operations at Zendesk joins us to discuss the mandate and toolkit of the AI transformation leader, the importance of strategy for AI impact, the AI transformation efforts at Zendesk, and their successes to date. Join us as we discuss:The AI transformation leader: mandate and skillset Strategy: the misunderstood and frequently forgotten key to AI impactGenAI: the transformation leader’s new best tool for rapid impact Process transformation: the goal of AI transformation

AI leaders. Why do we need them? How do you become one? And above all, how do you keep your job as one?In this episode, we are joined by guest speaker Mike Gualtieri, VP and Principal Analyst at Forrester, and we unpack the opportunities, pitfalls, and best practices of the AI leader role. He shares the pivotal role of AI leaders in catalyzing organizational transformation, their unique skill set that must encompass data science, business acumen, and software engineering, their importance in navigating the evolving regulatory landscape surrounding AI, and the need for platforms to facilitate rigorous auditing and compliance measures to foster trust and transparency.Join us as we discuss:The strategic imperative for AI leaders to curate a diversified portfolio of AI initiatives The multifaceted nature of AI risk management, spanning legal, ethical, and societal dimensionsThe formidable challenges inherent in navigating and enforcing AI-centric regulations amidst rapid technological advancement

GenAI is evolving at a breakneck pace, matched only by the startups that are looking to commercialize it. So what better way to understand the latest GenAI trends than to ask a venture capitalist specializing in AI? In this episode, we speak with James Cham, partner at Bloomberg Beta, about the state of GenAI – where it is delivering value today – and the challenges preventing firms from moving from incremental GenAI-driven productivity gains, to truly disruptive GenAI use cases. Along the way, we cover the problem of treating GenAI like software development, the rapidly changing economics of GenAI, and the key to success with all types of AI which is – as always – understanding people. Join us as we discuss:The cultural challenges preventing companies from unlocking the disruptive potential of GenAIWhy developers don’t get data-powered applications (and why data scientists need product thinking) How GenAI can change our engagement with technology (by killing the GUI)

A human being consists of billions of cells, each with the same genetic code but interacting in a myriad ways that can eventually translate into disease. Understanding and treating that disease is, in essence, a data problem. But how do you unlock that data and how do you change an organization to systematically use that data to improve decision-making and accelerate drug discovery? In this episode, we speak with Volodimir Olexiouk, Director of Scientific Engagement and Data Science Team Lead at BioLizard, about best practices for overcoming the data challenges for AI-driven drug discovery and combining scientific expertise with data science for augmented intelligence in the life sciences. Join us as we discuss:The challenges in discerning correlation from causation and integrating domain expertiseHow bridging expertise gaps and merging data silos in pharmaceutical companies radically improves drug-discovery processes The promise AI holds for swifter and more effective responses to future pandemics

Trip planning may well be the perfect AI use case. Too much information, too many combinations, and too little time —for humans, but not for Tripadvisor’s AI Trips. In this episode Rahul Todkar, VP Head of Data and AI, shares the secrets to building a trusted GenAI solution at internet scale and discusses the similarities and differences between data leadership roles at digitally native companies and more traditional enterprises. Join us as we discuss:How to use GenAI to unlock first party dataThe ideal GenAI development teamThe evolving role of data and AI leaders

Wouldn’t it be great if there was a commonly agreed-upon framework for executing all AI projects successfully? Well, there isn’t one. However, there is CRISP-DM, the antediluvian “Cross-Industry Standard Process for Data Mining”, but you need to expand, modernize and adapt this framework for success at your organization.In this episode from the archive, Dave Cole interviews David Von Dollen, former Head of AI at Volkswagen of America, about how they integrated CRISP-DM into an Agile process to drive more rapid iteration and, ultimately, more successful AI projects.

What’s just as important as the government keeping us safe from AI? Government leveraging AI to keep us safe!In this episode, we interview Joel Meyer – former head of strategy at the Department of Homeland Security (DHS) and the person who drove the creation of the DHS AI Task Force. Joel shares how they identified key areas where they could apply AI to improve national safety and security, such as combating fentanyl and child sexual exploitation and abuse, and the steps that the federal government is taking to build AI capabilities across the public sector.Join us as we discuss:Key areas where US government agencies are looking to leverage AI to improve mission effectivenessThe people, process, and technology steps that government agencies are implementing to scale AI and how they apply to the private sectorThe importance and value of Responsible AI in public sector use cases and beyond

There is no such thing as an AI drug, but AI and ML-models are driving the next wave of new treatments. In this episode, Brandon Allgood, Chief Data Officer at FogPharma and serial entrepreneur at the intersection of ML and Biopharma, shares his insights on how AI is disrupting the traditional process of drug discovery and development.Join us as we discuss: Why AI is so powerful for drug discoveryWhat data science needs to learn from engineeringHow drug discovery processes need to be rebuilt with AI models at their core

What’s the biggest problem in AI today? It’s that far too few projects make it to deployment. In this episode, Eric Siegel, founder of the long-running Machine Learning Week conference and creator of the first (and perhaps only) ML music video, tells us about his new book, The AI Playbook and the bizML framework for aligning stakeholders and maximizing the chance for deployment and impact.Join us as we discuss:Causes behind the high rates of AI project failureCritical project steps for ensuring deploymentHumor as a means to bridge the gap in AI understandingAnd check out:The AI Playbook: Mastering the Rare Art of Machine Learning DeploymentThe entire Predict This music video. You won’t regret it.Machine Learning Week June 4-7, 2024

The biggest challenges to driving impact with AI have little to do with AI and everything to do with humans. Nowhere is this greater than with GenAI where myths and misconceptions abound as to how organizations should be designing, developing and operationalizing GenAI-based applications. In this episode with Rowan Curran, industry analyst at Forrester Research, we debunk the most harmful myths and discuss how AI teams are shattering these myths and delivering transformative outcomes.Join us as we discuss:The role of data scientists and ML engineers in GenAI projectsSuccessful approaches to prompt engineeringThe linkages between MLOps and LLMOps

Imagine Generative AI handling tens of thousands of conversations with your customers daily. Science fiction? Not at Bolt where this has been in production since the summer of 2023. In this episode, Mikhail Korolev – head of the data science team at Bolt’s food delivery service – shares the challenges and hard earned best practices for operationalizing a GenAI application that dramatically lowers cost while also increasing customer satisfaction. Join us as we discuss:How to leverage GenAI to automate customer service conversationsHow to manage inconsistency and mitigate risk in GenAI appsHow to protect sensitive data and comply with regulatory requirements with GenAI

2023 has been an exciting year for AI, but it’s nothing in comparison to what we will see in 2024! Expect to see sensational successes amid the debris of projects that were set up for failure, a flowering of predictive AI, and the emergence of the scariest thing in AI to date (EU regulation). Tune in to this episode where Dr. Kjell Carlsson shares his top predictions for AI in 2024 and get ready for a year of scandal, fraud, plummeting processor prices, and ascendant AI leaders. Also, goodbye quantum computing! Happy holidays from all of us at Domino Data Lab and the Data Science Leaders podcast.

ChatGPT wasn’t the beginning of generative AI, but it did spark the GenAI revolution. Now, one year since it was launched, how much progress have we made, what impact is GenAI delivering, what are the real risks, and what developments are just around the corner? Join this session with the titan of the data science community, Anaconda CEO Peter Wang, and Dr. Kjell Carlsson, Head of AI Strategy at Domino Data Lab, where we will cover:The state of GenAI: where GenAI is delivering and missing expectationsThe challenges: the real risks and remaining barriers to impactThe future: what advances are underway and what can we expect over the next year

How do you achieve success as a Chief Data Officer? It is a role that is more important, yet more challenging, than it has ever been, with a rapidly expanding set of expectations from stakeholders in every part of the business.Here to help us understand the CDO role, its evolution, and the keys to success is Gary Barr, Global Chief Data Officer at Legal & General Investment Management (LGIM). Drawing from his wealth of experience, Gary speaks about the three incarnations of the CDO – from data governance champion to air traffic-controller of AI-driven transformation – as well as the dangers of dividing teams into “offense” and "defense”, the goal of the data mesh, and why AI regulation should be welcomed, not feared.Join us as we discuss:The rapid evolution of the CDO mandate and its responsibilitiesChanging the operating model for data and AI adoptionThe importance of qualitative and sentimental measures of ROI

Most experts agree that AI isn’t about replacing human intelligence, but about improving it. When it comes to education, we should take this literally. In this episode we discuss how to use AI to transform how we learn with Stephen Kosslyn, President of Active Learning Sciences and Founder and Chief Academic Officer of Foundry College. Stephen brings unparalleled expertise when it comes to using AI in education from his remarkable career spanning leadership roles at Harvard, Stanford, and Minerva University, but also thanks to his recent book “Active Learning with AI: A Practical Guide”.Join us as we discuss:How Generative AI can make learning more effective and scalableHow to design educational programs, create training experiences, and assess student understanding using Generative AIOvercoming the challenges of embracing AI in the education sectorFor more on the science of active learning and detailed, practical Generative AI examples, please check out Stephen’s new book, available now.

How do you implement an enterprise-grade GenAI application that serves millions of users a day? By focusing your application and building the capabilities for operationalizing it at scale.Join our upcoming fireside chat with Domino's SVP of Product, Chris Lauren, who will share lessons learned while operationalizing the world’s first enterprise-grade GenAI application to be used on a global scale, Github Copilot.Join us to learn:Success factors for GenAI use casesCommon challenges and how to avoid themKey capabilities for operationalizing GenAI models at scaleInferencing GenAI models cost-effectively

Every organization has an abundance of outlier detection use cases, but how do you turn them into repeatable, scalable AI products that drive a virtuous cycle of adoption and impact?To answer this question, Jan Zirnstein, Senior Data Science Director at Honeywell,. shares their best practices for successfully driving value using anomaly detection, how to build trust with stakeholders, and the importance of both product management and software development resources.Join us as we discuss:How to spark a virtuous cycle with anomaly detection use casesDriving continuous improvement by transitioning from unsupervised to supervised machine learningAligning the model development and software development lifecycles

AI has enormous potential for good, not least in helping us make more ethical, sustainable decisions as investors and consumers. In this week’s episode Ron Potok, Head of Data Science at Clarity AI, explains how AI helps us overcome the challenges of collecting, normalizing and assessing Environmental, Social and Governance (ESG) data and making that data useful and convenient to humans when making decisions. Indeed, he reveals how AI can bring transparency to human-only ESG ratings that can be more opaque and prone to bias than an AI model, and the benefits of leveraging humans and AI models in tandem.Join us as we discuss:Overcoming the ESG data quality challenges with AILeveraging AI to contextualize data and drive consistency How AI can provide greater transparency than human-only ratings

How do you trust black-box AI models with decisions that will make-or-break your business?This week we speak with Gregory Zuckerman -- special writer at the Wall Street Journal and author of The New York Times bestseller of The Man Who Solved the Market -- to find out how the pioneers in algorithmic trading learned to stop worrying and trust their AI systems. Join us as we discuss:How trust in AI relies on trust in people and processesThe limits of explainability and transparencyThe power of systems over stories

Who doesn’t have a data science talent gap? Anyone? Most organizations struggle to realize their AI ambitions because of a lack of data science skills, a disconnect between the technology and the business domain, and a lack of leadership experience with AI.Halliburton has been solving all three of these challenges with one of the earliest and largest corporate data science programs in the energy sector.In today’s episode of Data Science Leaders, we are extremely fortunate to be joined by Dr. Satyam Priyadarshy, Managing Director, Technology Fellow and Chief Data Scientist at Halliburton who shares their best practices for upskilling talent, bridging the data science - business divide, and ensuring executive engagement.Join us as we discuss:How to upskill existing domain experts on data science methodsHow to engage and drive alignment with corporate stakeholders through workshopsThe benefits of upskilling domain experts on code-based data science toolsThe importance of involving and upskilling leadership

It’s been said: “When everything is important, nothing is important.” So how do you succeed with AI-driven transformation where everything – across people, process, and technology – is important? It requires leadership, a deliberate strategy, and ongoing organizational change. Here to share insight on these transformational challenges and best-practices are Jen Stave and Catherine Feldman from the Digital, Data, and Design (D^3) Institute at Harvard. In this wide-ranging conversation, the duo draws upon seminal research from the Harvard Business School – such as professor Clay Christensen’s theory of Disruption – to explain how organizations must adapt their business and operating models, and make experimentation part of their organizational DNA.Join us as we discuss:Disruption and the reasons so many AI projects failThe need for a holistic approach and strong leadership for AI successApplying a jobs to be done” approach to generative AIAlso don’t miss HBS professor Karim Lakhani’s Rev 4 Keynote, “Competing in the Age of AI”.

Generative AI is here and, unless you’ve been cloistered in a cave, you already know it’s making waves in nearly every industry. But when it comes to this shiny new technology, separating fact from fiction can become quite a challenge.Luckily, in this episode, Rowan Curran, Analyst at Forrester, joins the show to demystify the latest leaps in AI tech, help you apply it to your business today, and give a glimpse of how it will affect the business landscape of tomorrow.Join us as we discuss:Separate generative AI facts and fictionTake a closer look at AI applications you can start using todayExamine the future of AI and its impacts on the workforce and the workplace

“It is on the shoulders of leaders that they build and maintain an ethical AI risk program.” That’s the message Reid Blackman – author of “Ethical Machines” and founder CEO at Virtue Consultants – shares in this episode. He discusses the real ethical AI concerns — blackbox models, bias, hallucinations, privacy violations and more — and explains the crucial need for leadership accountability, buy-in from the very top of the organization, and a multi-party effort in building and maintaining AI ethical risk programs.Join us as we discuss:Why AI poses greater ethical risks than other technologies (and humans)Leadership and the other key elements of a successful AI / digital ethics programThe importance of explainability

When it comes to driving business impact with AI, there are no silver bullets, but data science product management comes pretty close. It could well be the key to bridging the gap between business and technical teams, designing solutions to meet the business need, spurring ideas from experimentation to implementation, and driving continuous improvement. But how do you build a product management capability for data science?In this episode, Alek Liskov, Director of AI & Data Product Management at Verizon, shares their hard-won best practices in building data science product teams and their phenomenal successes in delivering AI-driven impact.Join us as we discuss:The emerging discipline of data science product managementHow to build durable product teams for data scienceWhere to find and how to develop data science product managers

What’s different about the AI wave today versus the 1980s and what do the latest advances reveal about our human intelligence? We’re behind the scenes at Rev4 with Steven Levy, best selling author and Editor at Large at WIRED. Steven shares insights he’s built over the past four decades writing about AI and the people (like Marvin Minskey) and companies (like Google and Facebook) that have brought us to where we are today. Join us as we discuss:A brief history of AI - from dashed hopes to triumphant transformersHow AI is helping us understand human intelligenceThe regulatory risks of anthropomorphizing AI