DataFramed

Across the data and AI industry, infrastructure that once served dashboards and human analysts is being rebuilt to serve autonomous agents instead. That shift changes what "AI-ready data" actually means, pushing teams to rethink documentation, governance, and the semantic layer so agents pull consistent, trusted definitions rather than guessing. Day to day, this shows up as pressure to clean up gold-layer tables, eliminate duplicate metrics, and formalize business logic that used to live only in someone's head. It raises real questions: how clean does data need to be before agents can safely act on it, and who ends up owning that definition?Tristan Handy is President and Co-Founder of Fivetran + dbt Labs, the company formed by the June 2026 merger of Fivetran and dbt Labs. He founded dbt Labs in 2016 (originally as Fishtown Analytics) and spent a decade as its CEO before leading the company through the merger, and has worked in data for 23 years.In the episode, Richie and Tristan explore the dbt and Fivetran merger, building an open and modular data stack, using data to power trustworthy AI agents, the growing importance of semantic layers, how data team structures are evolving, career advice for data practitioners, context engineering for AI-driven research, and much more.Links Mentioned in the Show:• Simon Willison's blog• dbt MCP server• Apache Iceberg• Apache Polaris• LookML / Looker's semantic layer• The Vaccine Education Center (CHOP)• Connect with Tristan• AI-Native Course: Intro to AI for WorkRelated Episodes:The Data Team's Agentic Future, with Ketan Karkhanis, CEO at ThoughtSpotTowards Self-Service Data Engineering with Taylor Brown, Co-Founder and COO at FivetranNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

AI capability in mathematics jumped before most people noticed, tackling Olympiad-level problems and unsolved research questions that had resisted attack for years. But the pattern of where AI succeeds and where it stalls is uneven and worth understanding. It's much better at grinding through cases to disprove something than at constructing an elegant, original proof. Anyone working with AI in a technical field runs into this same asymmetry. Where exactly is the boundary between tasks AI can already do reliably and ones that still need human judgment and creativity?Tudor Achim is the co-founder and CEO of Harmonic, an AI company building toward mathematical superintelligence. He previously led the machine learning team at Quora and co-founded and served as CTO of the autonomous driving company Helm.ai. Under Tudor, Harmonic's Aristotle system achieved gold-medal performance at the 2025 International Math Olympiad alongside systems from OpenAI and Google DeepMind — with every proof formally verified.In the episode, Richie and Tudor explore why AI is starting to outperform humans at advanced mathematics, the shift toward formally verified proofs using the Lean language, where AI already beats humans (finding counterexamples) versus where it still falls short (building elegant proofs), how human mathematicians' roles will change, why math capability gains spill over into better AI reasoning generally, and much more.Links Mentioned in the Show:• Tudor's TED Talk: "The Path to Mathematical Superintelligence"• Aristotle, Harmonic's reasoning system• Harmonic• The Erdős Problems• American Institute of Mathematics• Rich Sutton, "The Bitter Lesson"• Connect with Tudor: LinkedIn• AI-Native Course: Intro to AI for Work• Related Episode: Why AI Agents Haven't Taken Over Knowledge Work Yet, with Jennifer Smith, CEO of Scribe (exact URL pending — episode published Aug 17, 2026, too recent to be indexed yet; confirm link on datacamp.com/podcast before publishing)New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Technology is now moving faster than the organizations trying to adopt it. A model can be tested in an afternoon, but the approval to test it can take half a year, and that gap is where most transformation budgets quietly disappear. The structures that made companies safe and predictable — layers of sign-off, centralized control, standardized processes — were built for a world where getting things wrong was expensive. That world is gone. So what actually has to change inside a company for AI to deliver value? Which habits are holding things up? And where do you start when everything needs fixing at once?Phil Le Brun is an Executive in Residence at AWS and previously spent over 25 years at McDonald's Corporation, where he was VP of Global Technology Development and International CIO. Jana Werner is an Executive in Residence at AWS, where she leads the Financial Services Practice in EMEA and advises Fortune 500 executive teams on transformation, having previously scaled a tech start-up to acquisition by HP, led digital transformation at Tesco Bank, and advised DHL on global change. Together they are the authors of The Octopus Organization: A Guide to Thriving in a World of Continuous Transformation (Harvard Business Review Press).In the episode, Richie, Phil and Jana explore why AI transformations stall, the Tin Man organization and its anti-patterns, the octopus as a model for adaptive companies, why AI adoption metrics mislead, being data informed rather than data driven, making fast reversible decisions, hiring and onboarding, embedding learning into daily work, and much more.Links Mentioned in the Show:• The Octopus Organization (book)• Through the Looking-Glass — Lewis Carroll (the Red Queen)• Goodhart's law• Annie Duke on "resulting"• Linda Hill, Harvard Business School• A Seat at the Table — Mark Schwartz• Connect with Phil• Connect with Jana• AI-Native Course: Intro to AI for Work• Related Episode: Your 90 Day Blueprint for AI Success with Charlene Li• Explore AI-Native Learning on DataCampNew to DataCamp?• Learn on the go using the DataCamp mobile app• Empower your business with world-class data and AI skills with DataCamp for business

Four years into the AI boom, headlines still promise agents that will run entire departments, yet most companies can't point to the transformation they were sold. The gap isn't intelligence — today's models are remarkably capable — it's context: no model arrives knowing how your company actually gets things done. For anyone tasked with deploying AI at work, this raises pressing questions. What does it take to turn generic intelligence into something that understands your specific operations? And why do so many well-funded AI initiatives stall before they ever reach production?Jennifer Smith is Co-Founder and CEO of Scribe, the Workflow AI platform used by more than 6 million people and 94% of the Fortune 500. Under her leadership, Scribe has surpassed $100M in ARR and raised $75M at a $1.3B valuation. Before founding Scribe, Jennifer spent three years at Greylock Partners interviewing 1,200 C-suite executives about the problems they were trying to solve, and previously worked at Coatue Management and McKinsey & Company. She holds an MBA from Harvard and a BA from Princeton.In the episode, Richie and Jennifer explore why AI agents haven't taken over knowledge work yet, harnessing institutional knowledge as "specialized intelligence," mapping enterprise workflows with LLMs, building the business case and ROI for AI transformation, balancing top-down and bottom-up change management, and the agency-driven skills that matter most in an AI-native workplace, and much more.Links Mentioned in the Show:Scribe (Jennifer's company)McKinsey & CompanyAaron Levie, CEO of Box, followed by Jennifer on XJaya Gupta, Partner at Foundation CapitalConnect with Jennifer: LinkedInAI-Native Course: Intro to AI for WorkRelated Episode: AI Agents at Work: What Actually Breaks (and How to Fix It) with Danielle Crop, EVP at WNSNew to DataCamp? Learn on the go using the DataCamp mobile app.Empower your business with world-class data and AI skills with DataCamp for business.

Software buying decisions used to be made once, by someone far removed from the people actually using the tool. That model is breaking down. Teams now expect software to work out of the box, without weeks of setup, integration, and configuration before anyone sees value. At the same time, a growing share of "users" aren't people at all — they're AI agents calling the same systems through APIs and chat interfaces. That raises a set of questions worth sitting with: what happens to user experience when the user isn't human? Can personalization and simplicity coexist, or is one always traded for the other? And as building software gets cheaper, what actually separates a good product from a cluttered one?Srini Raghavan is Chief Product Officer at Freshworks, where he leads product strategy for the company's AI-powered customer and employee experience software. He previously served as Chief Product Officer at RingCentral and SVP of Product at Five9, and holds an MBA from the University of Chicago Booth School of Business.In the episode, Richie and Srini explore the SaaS consolidation trend and why the "SaaSpocalypse" prediction missed the point, building software that works for both humans and AI agents, how MCP and modular architecture are reshaping product design, the rise of the "product builder" role replacing specialized titles, customer feedback loops and cohort-based A/B testing, judgment as the most important AI-era career skill, and much more.Links Mentioned in the Show:• Fresh Service — https://www.freshworks.com/freshservice/• Jaya Gupta's Webinar at RADAR - https://app.datacamp.com/learn/webinars/whats-next-rethinking-analytics-for-the-ai-human-era• Freddy AI Agent Studio — https://www.freshworks.com/freshservice/ai-agent-studio/• Figma Make — https://www.figma.com/make/• Cursor — https://cursor.com• NotebookLM — https://notebooklm.google/• Marc Andreessen's "Mexican standoff" comment — https://officechai.com/ai/programmer-product-manager-and-designer-roles-are-merging-into-a-single-builder-role-marc-andreessen/• Connect with Srini: https://www.linkedin.com/in/srinivasan28/• AI Tutor Course: Intro to AI for Work — https://www.datacamp.com/courses/introduction-to-ai-for-work• Related Episode: Vibe Coding and the Rise of the Non-Developer Builder with Matt Palmer, Developer Relations at ReplitNew to DataCamp? Learn on the go using the DataCamp mobile app.Empower your business with world-class data and AI skills with DataCamp for business.

Software teams are shipping faster than ever, but speed hasn't solved the oldest problem in the industry: most software still isn't very good. AI coding tools have lowered the barrier to building something, yet they haven't lowered the barrier to building something worth using. As more people who aren't trained software creators start shipping products, a new question is forming across product, design, and engineering teams: if AI can build almost anything, how do you make sure it builds the right thing, and builds it well?Todd Olson is co-founder and CEO of Pendo, the product experience platform he started in 2013. Before that, he held product and engineering roles at Rally Software, Red Hat, Cisco, and Google. He's the author of The Product-Led Organization and has led Pendo through raising over $356M in venture funding while growing to 2,300+ customers.In the episode, Richie and Todd explore why bad software still gets built, how much context AI coding agents need before they can be trusted, using behavioral data and "rage prompts" to catch what's actually frustrating users, the shift toward headless and agentic software design, how product, design, and engineering roles are splitting apart, managing one-way-door risk during AI transformation, and much more.Links Mentioned in the Show:Jeff Bezos’s one-way door / two-way door decision framework: 2015 Amazon shareholder letterHubSpot’s 2024 terms-of-service backlashRampStripeFin (Intercom’s AI agent), recently announced to be acquired by SalesforceAnthropic / Claude CodeConnect with ToddAI-Native Course: Intro to AI for WorkRelated Episode: The Data Team’s Agentic Future with Ketan Karkhanis, CEO at ThoughtSpotNew to DataCamp?Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobileEmpower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

As AI takes over more technical and routine work, the skills that set data and AI professionals apart are shifting. Raw technical ability and a high IQ still matter, but they are becoming table stakes as tools get more capable and teams get smarter. What increasingly separates people is harder to automate: communication, self-awareness, authenticity, and the ability to keep learning through failure. For anyone building a career in this space, that raises real questions. Which skills are actually worth investing in now? What holds up as AI advances? And how do you keep growing once you have already had some success?Dr. Todd Dewett is one of the world's most-watched leadership voices — an authenticity expert, bestselling author, and top LinkedIn Learning instructor whose courses have reached more than 25 million people across 100+ countries. After beginning his career at Andersen Consulting and Ernst & Young, he earned a PhD in organizational behavior at Texas A&M and spent a decade as an award-winning professor before going solo. He is a five-time TEDx speaker and the author of Show Your Ink.In the episode, Richie and Todd explore why fear quietly limits careers, treating failure as data rather than a verdict, the people skills that outlast raw IQ, learnable self-awareness, authenticity at work, using AI without losing your voice, getting better at speaking and writing, building habits, escaping the success trap, and much more.Links Mentioned in the Show:• Todd's LinkedIn newsletter (writing + his "Creswall" comic) — https://www.linkedin.com/in/drdewett/• Todd Dewett on LinkedIn Learning — https://www.linkedin.com/learning/instructors/todd-dewett• Free LinkedIn Learning access via your public library — https://www.linkedin.com/learning• Gemma Leigh Roberts, chartered psychologist — https://www.linkedin.com/in/gemmaleighroberts/• Erin Shrimpton, chartered organisational psychologist — https://ie.linkedin.com/in/erinshrimpton• Connect with Todd: https://www.linkedin.com/in/drdewett/• AI-Native Course: Intro to AI for Work• Related Episode: How to Have a Machine Learning Career in 2026 with Marina WyssNew to DataCamp?Learn on the go using the DataCamp mobile app

Business intelligence has never been only about building charts and writing queries. Most of the work that makes a report trustworthy happens below the surface — in the data models, documentation, and stakeholder conversations that users never see. For analysts, technical skill is just the starting point; understanding what the business actually needs, and why a number exists, matters just as much. So what really separates a competent analyst from a great one? How do you build something that answers the right question, not just any question? And which skills are worth investing in first?Helen Wall is the founder of Helen Data Design and a Microsoft-recognized business intelligence expert and LinkedIn Learning instructor. A former actuary, she has worked across financial reporting, weather data, and consulting projects, and has maintained a running list of monthly Power BI updates for close to five years. She studied math and economics at the University of Washington, and focuses on where data analytics meets design.In the episode, Richie and Helen explore what separates a great business intelligence analyst from an average one, the iceberg model of analytics work, building and using semantic layers, taking over messy legacy projects, documenting for both humans and AI agents, how Power BI has changed over five years, keeping AI outputs consistent and cost-effective, accountability in the age of agents, and much more.Links Mentioned in the Show:• Connect with Helen• Microsoft AI for Good Lab• Power BI monthly feature updates• SQL Server Analysis Services• Power BI Q&A visual• DAX (Data Analysis Expressions)• AI-Native Course: Intro to AI for Work• Related Episode: The Data Team's Agentic Future with Ketan Karkhanis, CEO at ThoughtSpotNew to DataCamp?• Learn on the go using the DataCamp mobile app• Empower your business with world-class data and AI skills with DataCamp for business

For forty years, the rule held that transactional and analytical databases had to be separate systems, connected by fragile pipelines that move data from one to the other. That assumption is now being questioned. As AI agents start generating the majority of database activity, the old architecture is being redesigned around speed, scale, and a single copy of governed data. For anyone who works with data day to day, this raises practical questions. Do you still need separate systems for live and historical data? What happens to the pipelines you maintain? And how does your stack change when agents, not people, write most of the queries?Reynold Xin is co-founder and Chief Architect of Databricks. He is one of the original creators of Apache Spark, where he led the design of GraphX, Project Tungsten, and Structured Streaming, co-designed DataFrames, and served as release manager for Spark 2.0. He holds a PhD in Computer Science from UC Berkeley's AMPLab and a degree in Engineering Science from the University of Toronto.In the episode, Richie and Reynold explore self-service analytics with Genie, the ontology layer that grounds AI in enterprise data, handling hallucinations, governance and permissions for AI agents, merging transactional and analytical databases with Lakebase and LTAP, real-time analytics, controlling cost through autoscaling, the future of Spark and classic machine learning, and much more.Links Mentioned in the Show:• Connect with Reynold: https://www.linkedin.com/in/rxin• Genie (Databricks data agent): https://www.databricks.com/product/genie• Genie Ontology / Genie One: https://www.databricks.com/blog/introducing-genie-one-genie-ontology-and-genie-agents• LTAP (Lake Transactional/Analytical Processing): https://www.databricks.com/company/newsroom/press-releases/databricks-launches-ltap-first-lake-transactionalanalytical• Lakehouse//RT: https://www.databricks.com/blog/introducing-lakehousert-real-time-performance-unified-lakehouse• Lakebase: https://www.databricks.com/product/lakebase• Apache Spark: https://spark.apache.org• AI-Native Course: Intro to AI for Work - https://www.datacamp.com/courses/introduction-to-ai-for-work• Related Episode: AI's Impact on Databases - https://www.datacamp.com/podcast/ais-impact-on-databasesNew to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobileEmpower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

Across the AI industry, capability has exploded while trustworthiness has lagged badly behind. The same technology that writes fluent prose can invent a refund policy that was never real, and most of those errors are subtle enough that no one notices. As more teams hand high-stakes work to AI — in banking, healthcare, customer service — the cost of confident mistakes adds up fast. So how common are hallucinations, really? Can chaining models together or adding humans to the loop fix it? And is reliability something you can design into a system from the start?Dan Klein is the CTO and co-founder of Scaled Cognition and a professor of computer science at UC Berkeley, where he leads the Berkeley NLP Group within the Berkeley AI Research (BAIR) Lab. He previously co-founded Semantic Machines, a conversational AI company acquired by Microsoft in 2018. At Scaled Cognition he built APT (Agentic Pretrained Transformer), a frontier model designed from the ground up for reliable, policy-adherent agentic AI.In the episode, Richie and Dan explore why AI reliability has lagged behind capability, how hallucinations hide in plain sight, the limits of humans-in-the-loop and LLM-as-judge, building reliability into model architecture, agentic systems and verifiable actions, test-driven agent development, the skills that stay valuable, digital literacy, and much more.Links Mentioned in the Show:• Connect with Dan: https://www.linkedin.com/in/dan-klein/• Scaled Cognition: https://www.scaledcognition.com/• Berkeley NLP Group: https://nlp.cs.berkeley.edu/• Code smells (Martin Fowler): https://martinfowler.com/bliki/CodeSmell.html• Refactoring, by Martin Fowler: https://martinfowler.com/books/refactoring.html• "Now you have two problems" (Jamie Zawinski quote): https://regex.info/blog/2006-09-15/247• Lean theorem prover: https://lean-lang.org/• AI-Native Course: Intro to AI for Work - https://www.datacamp.com/courses/introduction-to-ai-for-work• Related Episode: How to Build AI Your Users Can Trust with David Colwell, VP of AI & ML at Tricentis - https://www.datacamp.com/podcast/how-to-build-ai-your-users-can-trustNew to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobileEmpower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

AI agents are no longer limited to automating routine tasks like customer support or report generation. Research labs and pharmaceutical companies are beginning to deploy teams of specialist AI agents capable of designing experiments, analyzing data, and proposing new hypotheses — in some cases producing results that outperform human experts. For data scientists and researchers, this raises urgent questions: Where do AI agents excel in scientific workflows today, and where do they fall short? How do you build an agent that can genuinely innovate rather than just replicate what's already been done? And what does it take to scale a single model into a fully functioning virtual research team?James Zou is an Associate Professor of Biomedical Data Science, and by courtesy of Computer Science and Electrical Engineering, at Stanford University. He leads the Stanford AI for Science Lab and is affiliated with Together AI. His research focuses on building AI agents for scientific discovery and data science, making AI more reliable and statistically rigorous. He has received a Sloan Fellowship, NSF CAREER Award, two Chan-Zuckerberg Investigator Awards, and faculty awards from Google, Amazon, and Adobe.In the episode, Richie and James explore how AI scientist agents are already outperforming human experts in scientific discovery, the Virtual Lab framework for building teams of specialist AI agents that conduct real research, teaching models to innovate not just imitate through a new training paradigm called "learning to discover," DS Gym for self-improving data science agents, scaling agentic systems from a single model to a Virtual Biotech with tens of thousands of agents, Einstein Arena as the first competition platform built exclusively for AI agents, converting scientific papers into agent-native MCPs through Paper to Agent, and much more.Links Mentioned in the Show:• Virtual Lab (Nature paper)• Einstein Arena• DS Gym• Paper2Agent• Together AI• AlphaFold 2 / Nobel Prize 2024• Connect with James• AI-Native Course: Intro to AI for Work• Related Episode: #358 How AI Agents Will Work While You Sleep | Ruslan SalakhutdinovNew to DataCamp?• Learn on the go using the DataCamp mobile app• Empower your business with world-class data and AI skills with DataCamp for business

Most organizations know AI matters, but few have turned that conviction into a written plan. Ambition and hope are everywhere; a clear roadmap tied to business strategy is rare. For teams on the ground, this gap shows up as scattered initiatives, tools nobody fully uses, and a lot of activity that never adds up to real value. So where do you actually start? How do you move from a long list of use cases to a focused plan you can execute? And who in the organization should own the job of turning AI into business results?Charlene Li is a New York Times bestselling author and strategic advisor who has spent more than two decades helping leaders navigate disruptive change. She founded Altimeter Group, has advised 49 of the Fortune 100, and is the co-author of Winning with AI: The 90-Day Blueprint for Success (with Dr. Katia Walsh).In the episode, Richie and Charlene explore how to get your organization AI-ready in 90 days, why you don't need a separate AI strategy, appointing an AI value owner, creating value beyond efficiency, building AI fluency, Goldilocks governance, why you should kill your AI pilots, and much more.Links Mentioned in the Show:Winning with AI: The 90-Day Blueprint for SuccessDr. Katia Walsh (co-author)ModernaKonectaIKEAAndrej Karpathy's LLM WikiConnect with Charlene: LinkedInAI-Native Course: Intro to AI for WorkRelated Episode: Our Data Trends & Predictions for 2026 with Jonathan Cornelissen & Martijn TheuwissenNew to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobileEmpower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

AI agents are starting to handle parts of the shopping journey that used to require human judgment — discovery, comparison, checkout. But behind every agent recommendation is a massive, invisible layer of data infrastructure. Product catalogs need to be structured, inventory synced in real time, pricing accurate, and quality signals clear. For data engineers and teams building at companies like Shopify, this shift means rethinking how data flows through systems and what "good enough" quality actually means. How do you ensure data is ready for AI? And how is this reshaping what data teams actually do?Nell Thomas is the VP of Data at Shopify, where she leads a team of approximately 400–500 people across data infrastructure, ML platforms, data engineering, and data science. Her career spans multiple industries including social media (Facebook), e-commerce (Etsy), politics (Hillary for America, Democratic National Committee), and now commerce. She holds an A.B. in Psychology from Harvard University and an M.A. in History & Sociology of Science from the University of Pennsylvania.In the episode, Richie and Nell explore agentic commerce and how AI agents are transforming shopping, the role of data in enabling AI-driven commerce, Shopify's Catalog and Universal Commerce Protocol, data quality requirements for agentic systems, how the data team function is evolving at Shopify, changing skill requirements for data professionals, and Nell's unconventional career path from politics to tech.Links Mentioned in the Show:- Agentic Commerce on Shopify- Universal Commerce Protocol (UCP) vs Agentic Commerce Protocol (ACP)- Shopify Catalog Documentation- Agentic Storefronts — Shopify Sales Channel- ChatGPT — OpenAI's Conversational AI- How Shopify Built Data Infrastructure at ScaleRelated Scaling Data Quality in the Age of Generative AINew to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobileEmpower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

Technical skills are being commoditized faster than ever. As AI takes on more of the work that used to define a junior knowledge worker, the things that once made someone valuable are becoming table stakes. What compounds in this environment is reputation — what colleagues, clients, and decision-makers think about you when your name comes up.That puts new pressure on visibility. People doing great work in silence are increasingly the ones getting passed over for promotions and external opportunities. So how do you build a reputation without becoming an influencer? What does AI-era credibility actually look like? And how do you start small?Dorie Clark teaches Executive Education at Columbia Business School and is the Wall Street Journal and USA Today bestselling author of The Long Game, Entrepreneurial You, Reinventing You, and Stand Out. She has been named four times as one of the Top 50 business thinkers in the world by Thinkers50, recognized as the #1 Communication Coach in the world by the Marshall Goldsmith Leading Global Coaches Awards, and is a frequent contributor to the Harvard Business Review.In the episode, Richie and Dorie explore why AI fluency is the new Excel skill, tinkering with AI's jagged frontier, the security risks of agentic AI, what personal branding really means in an AI-disrupted job market, the recognized expert formula, the ladder strategy for credibility, networking with "no asks for a year," running better meetings, and much more.Links Mentioned in the Show:• The Jagged Frontier (HBS Working Paper)• Agentic Misalignment: How LLMs could be insider threats (Anthropic)• AI-powered coding tool wiped out a software company's database (Fortune)• Reinventing You by Dorie Clark• The Long Game by Dorie Clark• Superteams by Ron Friedman• Connect with Dorie on LinkedIn• AI-Native Course: Intro to AI for Work• Related Episode: #341 Our Data Trends & Predictions for 2026New to DataCamp?Learn on the go using the DataCamp mobile app.Empower your business with world-class data and AI skills with DataCamp for business.

The role of the machine learning engineer is being rewritten in real time. AI coding assistants are absorbing parts of the day-to-day, planning and evaluation are eating up more of the week, and the lines between machine learning engineer, AI engineer, and data scientist are blurrier than ever. For anyone working in data and AI — or trying to break in — this shift changes what skills are worth investing in, what employers actually screen for, and how interviews are run. What's still worth learning? What does a competitive portfolio look like? And how do you stand out when a thousand applicants are using bots to apply?Marina Wyss is a Senior Applied Scientist at Twitch (an Amazon company), where she builds production AI and machine learning systems across content understanding, recommendations, and forecasting. She came into the field from a non-traditional background — a political science undergrad and a Master's in social data science in Berlin — and has held machine learning roles at Coursera and a Berlin-based statistical consultancy along the way. Outside her day job, Marina runs a popular AI/ML YouTube channel and weekly newsletter, and coaches people transitioning into machine learning from non-traditional careers.In this episode, Richie and Marina explore how AI is reshaping the machine learning engineer role, the shifting balance between coding and planning, why evaluation matters more than ever, the differences between ML engineer, AI engineer, and data scientist roles, how to break into the field from a non-technical background, what makes a strong portfolio project, the hiring process at big tech, how to prepare for technical interviews, networking strategies that actually work, what success looks like in your first few months on the job, and much more.Links Mentioned in the Show• Chip Huyen — AI Engineering (book)• Andrew Codesmith on YouTube• Phillip Choi on YouTube• A Life Engineered on YouTube• Keras• LeetCode• Connect with Marina: LinkedIn• AI-Native Course: Intro to AI for Work• Related Episode: How to Have a Career in Data Science in 2025 with Dawn ChooNew to DataCamp?Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobileEmpower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

Every conversation about AI in data eventually arrives at the same question: which roles survive, and which ones get automated away? Generative AI can already draft SQL, build dashboards, and run exploratory analysis — but it still can't sit with a business stakeholder and untangle what "customer" actually means across five teams. For data professionals, that shifts the day-to-day from production work toward translation, modeling, and judgment. So which skills are worth doubling down on? Which roles are becoming central, and which are quietly disappearing? And what should anyone hiring — or being hired — be paying attention to right now?Veronika Durgin is the VP of Data at Saks Global, where she leads data strategy across the luxury retail group. A full-stack data executive with more than two decades of experience spanning database administration, data engineering, platform architecture, data modeling, and analytics, Veronika is a Snowflake Data Superhero and a member of CDO Magazine's Global Editorial Board. She writes about data modeling, data culture, and data leadership on her Substack and Medium.In the episode, Richie and Veronika explore the future of data careers under AI, why analytics engineering becomes the catch-all role, the skills and hiring shifts data leaders are making, centralized data with decentralized analytics, keeping enterprise data teams agile, conceptual data modeling as the unglamorous prerequisite to AI, semantic layers, agentic commerce, and much more.Links Mentioned in the Show:Connect with Veronika: LinkedInVeronika's Substack: Think. Solve. Repeat.dbt — referenced as the origin of "analytics engineering"Open Data Science Conference (ODSC) — Veronika's recent talk on data and company politicsAmazon "two-way door" decisions — Bezos shareholder letterJessica Talisman — Veronika's recommendation for knowledge graphs and ontologiesJuan Sequeda — referenced on semantic layers and knowledge graphsCatalog & Cocktails podcast (hosted by Juan Sequeda)AI-Native Course: Intro to AI for WorkRelated Episode: Creating an AI-First Data Team with Bilal Zia, Head of Data Science & Analytics at DuolingoNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Most AI ethics conversations sound the same: be fair, be transparent, be accountable. The values are right, but in practice they don't get teams out of bed in the morning. Executives nod along, employees take the compliance training, and meanwhile real risks like hallucinations, cascading failures, and autonomous agents acting at scale slip through. So what shifts when teams stop chasing an ethical ideal and start naming the specific disasters they want to avoid? Who needs to be in the room to spot them? And what kind of training actually changes how people use AI day to day?Reid Blackman is the founder and CEO of Virtue, an AI ethical risk consultancy, and the author of The Ethical Nightmare Challenge: How to Avoid the Worst of AI (2026) and Ethical Machines (HBR Press, 2022). A former philosophy professor at Colgate with a PhD from the University of Texas at Austin, he has designed responsible AI programs for organizations including Amazon, Etsy, Kraft Heinz, Merck, US Bank, and Nationwide, and has advised the FBI, NASA, the World Economic Forum, and the Canadian government on federal AI regulations. He also hosts the Ethical Machines podcast.In the episode, Richie and Reid explore why responsible AI fails to motivate organizations, the biggest AI ethical nightmares facing companies today, the unique risks of agentic AI including cascading failures and emergent risks, the Ethical Nightmare Challenge framework, cross-functional ENC teams, training employees in plain language, scaling AI governance, measuring success by what you avoid, and much more.Links Mentioned in the Show:• The Ethical Nightmare Challenge by Reid Blackman• Ethical Machines by Reid Blackman• Ethical Machines podcast• Claude Code• Connect with Reid: LinkedIn• AI-Native Course: Intro to AI for Work• Related Episode: #350 How to Make Hard Choices in AI with Atay KozlovskiNew to DataCamp? Learn on the go using the DataCamp mobile app.Empower your business with world-class data and AI skills with DataCamp for business.

Valerie Tiberius is the Paul W. Frenzel Chair in Liberal Arts and Professor of Philosophy at the University of Minnesota. She is an expert in ethics, moral psychology, and well-being, and the author of five books including What Do You Want Out of Life? and the forthcoming Artificially Yours: Real Friendship in a World of Chatbots (Princeton University Press, May 2026). She previously served as President of the Central Division of the American Philosophical Association.In the episode, Richie and Valerie explore the purpose of friendship and whether AI can replicate it, the benefits and risks of chatbot companions for loneliness, how sycophantic AI responses distort advice and self-perception, the dangers of companion chatbots for children's social development, designing ethical AI companions that promote human flourishing, the zone of proximal development as a framework for better AI tools, and much more.Links Mentioned in the Show:Artificial Intimacy by Sherry Turkle Being You: A New Science of Consciousness by Anil SethLiberation Day: Stories by George SaundersHard Fork podcast (NYT)Connect with ValerieAI-Native Course: Intro to AI for WorkRelated Episode: #342 — "The Secrets to High AI Adoption" with Stefano Puntoni, Professor at WhartonNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Almost every AI agent demo lands in roughly the same place: it works most of the time, looks remarkable, and then fails in a way no one anticipated. Self-driving cars hit this wall a decade ago, and agents are running into it now. For data and AI teams, the question is no longer whether agents can complete a task — it's whether they can complete it reliably enough to remove the human reviewer. Which categories of work tolerate a 90% success rate? Which absolutely don't? And where should the next layer of guardrails sit?Ruslan Salakhutdinov is a UPMC Professor of Computer Science at Carnegie Mellon University and one of Geoffrey Hinton's former PhD students. He has previously served as Director of AI Research at Apple and VP of Research in Generative AI at Meta. His research focuses on deep learning, reasoning, and AI agents.In the episode, Richie and Russ explore the most exciting use cases of AI agents today, long horizon tasks, the credit assignment problem, multi-agent systems, designing reliable human-in-the-loop workflows, agent safety and guardrails, embodied and physical AI, lessons from self-driving cars, the difference between academia and industry, and much more.Links Mentioned in the Show:• Claude Code (Anthropic)• Yutori• Waymo• Apple Project Titan• DeepSeek-V3 Technical Report• Kimi K2 Technical Report• Connect with Ruslan: LinkedIn• AI-Native Course: Intro to AI for Work• Related Episode: AI Agents at Work: What Actually Breaks (and How to Fix It) with Danielle CropNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Time series data is everywhere — from inventory systems and energy grids to financial planning and product demand. As data volumes grow, the old ways of building individual forecasting models simply don't scale. How do you forecast hundreds of thousands of products without spending months on manual modeling? How do you know when to trust automation and when to step in? And what does it actually take to produce forecasts that business stakeholders will act on?Rami Krispin is Senior Director of Data Science and Engineering at Apple Finance, where he leads teams working at the intersection of statistical modeling, machine learning, and production forecasting. He is the author of Hands-On Time Series Analysis with R, an open-source contributor, Docker Captain, and instructor. He holds an MA in Applied Economics and an MS in Actuarial Mathematics from the University of Michigan, where he began his journey learning time series on DataCamp — before going on to build his own course there.In the episode, Richie and Rami explore time series foundation models and the case for scaling, traditional versus modern forecasting approaches, feature engineering in the business world, backtesting and model selection, risk management in automated forecasting, communicating forecast uncertainty to stakeholders, the evolving role of data scientists as architects, and much more.Links Mentioned in the Show:Forecasting: Principles and Practice (Rob Hyndman)NixtlaskforecastProphetConnect with RamiAI-Native Course: Intro to AI for WorkRelated Episode: Developing Better Predictive Models with Graph TransformersNew to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Cloud data platforms now offer hundreds of services, plus a growing menu of SQL, NoSQL, and open source options. Unified environments promise a simpler path, but the hard trade-offs—consistency versus scale, single-writer versus sharded, RPO/RTO targets—still matter. In daily work, you may be deciding between SQL Server, Postgres, and a globally distributed JSON store, while also asking AI tools to draft queries and spot issues. Should you still learn SQL if an agent can write it? How do you validate the intent, performance, and security of generated queries? And can monitoring agents actually reduce on-call pain without taking away needed control?Shireesh is the CVP of Databases at Microsoft. He leads product management, engineering, and cloud operations for Azure Databases as well as App Development for Microsoft Fabric. The products in his team’s portfolio include Azure SQL Database (on-prem, Hybrid and Cloud), Azure Cosmos DB, Azure PostgreSQL, and Azure MySQL.\\n\\nPreviously, as the Senior Vice President at SingleStore, Shireesh was responsible for end-to-end engineering and product vision of the company. Before moving to SingleStore, Shireesh was a founding member of Cosmos DB, where he architected, designed, and directly contributed to multiple key pieces of the services.\\n\\nShireesh has 20+ years of experience on large scale, big data, scale-out, relational and schema agnostic distributed systems across SQL, Azure Cosmos DB and PostgreSQL/Citus.In the episode, Richie and Shireesh explore how AI agents are reshaping data stacks, why unified platforms like Fabric matter, how semantic models and ontologies reduce confusion in metrics, SQL and NoSQL choices on Azure, Postgres to Cosmos DB with guidance for builders, and much more.Links Mentioned in the Show:Microsoft FabricAzure Cosmos DBWhat is Azure SQL Database?Connect with ShireeshAI-Native Course: Intro to AI for WorkRelated Episode: Six Skills Data Professionals Need To Succeed with Abhijit Bhaduri, Brand Evangelist & Former General Manager of Global L&D at MicrosoftExplore AI-Native Learning on DataCampNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Decision intelligence is showing up across data and AI teams as companies move beyond dashboards to decisions made with context. Graphs, entity resolution, and better data products are becoming core tools as messy, siloed data meets stricter risk and compliance needs. In day-to-day work, this means linking “James,” “Jim,” and “Jamie” across systems, enriching records with third‑party sources, and pushing models where the data already lives in your lakehouse. How do you trust your customer counts? Which links in a graph matter, and which are noise? Can graph-based context reduce LLM hallucinations enough for regulated decisions with humans still in-loop.Jamie Hutton is the Co-founder and Chief Technology Officer of Quantexa, where he leads the company’s global research and development organization in advancing its market-leading Decision Intelligence Platform. With over two decades of experience pioneering data-driven technologies, Jamie has been at the forefront of innovations that connect and unify data at scale to solve complex real-world challenges. He is the creator of dynamic Entity Resolution, a pioneering capability that has redefined how the world’s leading organizations transform raw data into trusted, decision-ready intelligence. This innovation enables enterprises to prepare their data for AI, uncover new revenue streams, and expose hidden connections in even the most sophisticated criminal networks. By providing the foundation for accurate, explainable, and actionable insights, Jamie’s work has empowered governments, financial institutions, and global enterprises to make faster, smarter, and more confident decisions.Prior to co-founding Quantexa, Jamie held senior technology and analytics leadership roles at SAS and Detica, where he delivered mission-critical solutions for organizations operating in some of the most complex and high-stakes environments in the world. Jamie holds a First-Class master’s degree in computer engineering and is recognized as a leading authority in contextual analytics, data integration, and applied AI for mission-critical decision-making.In the episode, Richie and Jamie explore decision intelligence beyond BI, entity resolution across siloed data, building context graphs for fraud, AML, credit risk, and growth, how graph analytics separates meaningful links from noise, graph-RAG for LLMs to cut hallucinations, human-in-the-loop workflows, and ways to start today, and much more.Links Mentioned in the Show:QuantexaDun & Bradstreet Data EnrichmentConnect with JamieAI-Native Course: Intro to AI for WorkRelated Episode: How Optimization Powers Decision Intelligence with Duke Perrucci & Ed Klotz, CEO and Senior Mathematical Optimization Specialist at Gurobi OptimizationExplore AI-Native Learning on DataCampNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Data and AI platforms are racing toward agentic and even autonomous analytics. But the bottleneck is rarely the model—it’s data readiness: governed metrics, clear metadata, and a semantic layer machines can read. For data engineers and analysts, this shifts work from hand-built SQL and dashboard tweaks to designing meaning and trust. If an agent can draft column descriptions, propose a model for a new business question, and build the first dashboard layout, where do you add the most value? What do you measure to prove ROI in 30 days? How do you prevent “shiny demos” from driving strategy too early.Ketan Karkhanis is the CEO of ThoughtSpot. Prior to joining the company in September 2024, Ketan was the Executive Vice President and General Manager of Sales Cloud at Salesforce. He returned to Salesforce in March 2022 after his time as the COO of Turvo, an emerging supply-chain collaboration platform. Before that, Ketan spent nearly a decade at Salesforce, where he led product areas in Sales, Service Cloud, Lightning Platform, and finally Analytics, wherein as the Senior Vice President & GM of Einstein Analytics, he pioneered incredible innovation, customer success, and business acceleration from launch to over $300M and a 30,000 strong user community. Prior to Salesforce, Ketan was at Cisco Systems where he led various technology initiatives and initiatives spanning Customer Advocacy, Cisco Certifications & eLearning.In the episode, Richie and Ketan explore AI agents for analytics, why “self‑service BI” often fails, using agents to answer questions, build dashboards, and automate data modeling, how analyst and engineer roles shift toward governance and agent design, how transparency, culture, and ROI drive safe adoption, and much more.Links Mentioned in the Show:ThoughtspotThoughspot’s Spotter AgentsConnect with KetanAI-Native Course: Intro to AI for WorkRelated Episode: AI Agents at Work: What Actually Breaks (and How to Fix It) with Danielle Crop, EVP Digital Strategy & Alliances at WNSExplore AI-Native Learning on DataCampNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

AI agents are spreading across the data and AI industry, promising to automate everything from research to outreach. At the same time, teams are learning that these tools can hallucinate, leak data, or act in surprising ways. In day-to-day work, the challenge is deciding which tasks to hand off, what data to share, and how to keep the output trustworthy. Do your agents actually add value, or just add noise? Are they running in a secured, ring-fenced environment? How do you balance playful experimentation with critical checking when an agent confidently gets a key fact wrong?Danielle leads go-to-market strategy at WNS, Capgemini's AI transformation services arm. Previously, Danielle was Chief Data Officer at American Express and Albertsons. She also write The Remix substack on technology trends, and is an Editorial Board Member for CDO Magazine.In the episode, Richie and Danielle explore AI agents at work, experimentation with guardrails, data privacy, access, tone controls, OpenClaw automation wins and failures, token costs, tying AI plans to P&L strategy, shifts in careers and hiring, how data teams handle unstructured data governance, and much more.Links Mentioned in the Show:WNSConnect with DanielleAI-Native Course: Intro to AI for WorkCatch Danielle speaking at RADAR—April 1Related Episode: AI Agents Are the New Shadow IT (And Your Governance Isn’t Ready) with Stijn Christiaens, CEO at CollibraExplore AI-Native Learning on DataCampNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

World models are emerging as the next step after large language models, pushing AI from book knowledge toward systems that can simulate the physical and social world. Instead of just generating text or short videos, the goal is steerable simulation with long-horizon consistency and planning. For practitioners, this raises practical choices: what data and representations do you need, and when do you mix symbolic reasoning with generative models? How do you test whether a model can follow actions over minutes, not seconds? And where do you start—robotics, driving safety, or synthetic data generation?Professor Eric Xing is President of Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) and a world-leading computer scientist whose work spans statistical machine learning, distributed systems, computational biology, and healthcare AI. A fellow of AAAI, IEEE, and the American Statistical Association, he has authored over 400 research papers cited more than 44,000 times.Before MBZUAI, Eric was a Professor of Computer Science at Carnegie Mellon University, where he also founded the Center for Machine Learning and Health. He is the founder and chief scientist of Petuum Inc., recognized as a World Economic Forum Technology Pioneer, and has held visiting roles at Stanford and Facebook. He holds PhDs in both Molecular Biology and Computer Science.In the episode, Richie and Eric explore world models as simulators for action, the jump from book intelligence to physical and social skills, why long-horizon planning is still hard, architectures, robots, data generation, open K2 Think LLMs, virtual-cell biology, and much more.Links Mentioned in the Show:MBZUAIPan World ModelConnect with EricAI-Native Course: Intro to AI for WorkRelated Episode: Developing Better Predictive Models with Graph Transformers with Jure Leskovec, Pioneer of Graph Transformers, Professor at StanfordExplore AI-Native Learning on DataCampNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Across the AI industry, high-stakes tools are being deployed in places where errors can harm people: sepsis alerts in hospitals, identity checks, welfare fraud detection, immigration enforcement, and recommendation systems that shape life outcomes. The pattern is familiar: scale and speed go up, while human review becomes rushed, shallow, or punished for disagreeing. In daily work, that can look like a nurse forced to act on false alarms, or a team using an LLM summary in ways the designers never planned. When should you slow down deployment? How do you detect new “wild” use cases early? And what does responsible tracking and oversight look like under real pressure?Atay Kozlovski is a Postdoctoral Researcher at the University of Zurich’s Center for Ethics. He holds a PhD in Philosophy from the University of Zurich, an MA in PPE from the University of Bern, and a BA from Tel Aviv University. His current research focuses on normative ethics, hard choices, and the ethics of AI.In the episode, Richie and Atay explore why AI failures keep happening, from automation bias to opaque targeting and hiring models. They unpack “meaningful human control,” accountability, and design in healthcare, government, and warfare. You’ll also hear about deepfakes, consent, digital twins, and AI-driven civic engagement, and much more.Links Mentioned in the Show:“Lavender” IDF recommendation systemAmnesty International reports on AI/automation in welfare systems“Meaningful Human Control” (MHC) frameworkConnect with AtayAI-Native Course: Intro to AI for WorkRelated Episode: Harnessing AI to Help Humanity with Sandy Pentland, HAI Fellow at StanfordExplore AI-Native Learning on DataCampNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Data governance has been around long enough to develop playbooks, but AI governance is evolving in real time. Industry trends like LLMs, agents, and emerging “swarms” are changing what oversight even means, from data lineage to agent-to-agent provenance.For working teams, the questions are immediate: who leads—legal, security, IT, data, or a new AI role? How do you set standards so engineers aren’t using a different tool for every task? What maturity framework should you measure against, and how often should you reassess as technology shifts? How do you help teams move fast without breaking trust?Stijn is a data governance veteran and one of the leading thinkers in the space. He runs data strategy, data infrastructure, and product evangelism at the data and AI governance company Collibra. Since founding Collibra 18 years ago, Stijn has held several executive positions, including COO and CTO.In the episode, Richie and Stijn explore AI governance failures and wins, risks from agents that can act on systems, creating visibility with an agent registry, how AI governance differs from data governance, ownership across legal, security, IT, and data teams, EU AI Act risk tiers, and much more.Links Mentioned in the Show:CollibraConnect with StijnAI-Native Course: Intro to AI for WorkRelated Episode: The New Paradigm for Enterprise AI Governance with Blake Brannon, Chief Innovation Officer at OneTrustExplore AI-Native Learning on DataCampNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Automation is moving from APIs to full “computer use,” where agents click through screens like a human. That power is transforming evidence collection, access reviews, and repetitive security tasks, but it also raises new risk. In everyday workflows, the safest gains often start with read-only actions, sandboxes, and clear opt-in for anything that writes changes. Do your tools know when an access request is an anomaly? Can you keep humans in the loop with fast review-and-approve steps? And if an agent can browse your systems, how do you stop data from walking out the door before customers or attackers notice?Jeremy Epling is Chief Product Officer at Vanta, where he leads product strategy and execution for the company’s trust management platform. He focuses on helping organizations automate security and compliance, enabling them to build and scale with confidence.Previously, he was VP of Product at GitHub, overseeing Actions, Codespaces, npm, and Packages—core components of the modern developer workflow used by millions worldwide. Before GitHub, Jeremy spent more than 16 years at Microsoft, leading product teams across Azure DevOps Pipelines and Repos, OneDrive, Outlook, Windows, and Internet Explorer. His work has centered on developer platforms, cloud infrastructure, and productivity tools at global scale.In the episode, Richie and Jeremy Epling explore AI-driven security risks, vendor data use and trade-secret leakage, governance and access controls, compliance beyond audits, how agents automate security questionnaires and vendor reviews, how to ship faster safely, human-in-the-loop design, and “computer use” automation, and much more.Links Mentioned in the Show:VantaVanta State of Trust ReportConnect with JeremyAI-Native Course: Intro to AI for WorkRelated Episode: Governing Pandora's Box: Managing AI Risks with Andrea Bonime-Blanc, CEO at GEC Risk AdvisoryExplore AI-Native Learning on DataCampNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Physical AI is showing up across the industry as sensors, connected devices, and foundation models move from the cloud into the real world. After years of IoT wiring everything to the internet, the big shift is turning raw measurements and video into meaning, not just dashboards. For day-to-day teams, that changes how you monitor equipment, detect failures, and decide what to do next. When thousands of sensor streams hit storage, who turns them into insights and recommendations fast enough to matter? Can one model generalize across different sensors and conditions? And what must run on the asset versus the cloud?Dr. Ivan Poupyrev is CEO and Founder of Archetype AI, where he is building a multimodal AI foundation model that combines real-time sensor data and natural language to help people and organizations better understand and act on the physical world. The company is developing a developer platform to unlock new applications of Physical AI across industries.Previously, he was Director of Engineering at Google’s Advanced Technology and Projects (ATAP) division, where he founded and led large cross-functional teams to create Soli, a radar-based sensing platform, and Jacquard, a connected apparel platform powered by smart textiles and embedded ML. These technologies shipped in more than 15 products across 33 countries, including collaborations with Levi’s, YSL, Adidas, and Samsonite, and were integrated into flagship devices such as Pixel 4 and Nest products. His work has been widely published, recognized with major international awards, and featured in global media.In the episode, Richie and Ivan explore physical AI beyond robotics, turning IoT sensor streams into insights, recommendations, and automation, why physical foundation models differ from LLMs, sensor-fusion wins like wind-turbine failure alerts, edge deployment and privacy, how to pick a first project in practice, and much more.Links Mentioned in the Show:Archetype AIAttention Is All You Need (Original Transformer Architecture Paper)A Mathematical Theory of Communication (Shannon, 1948)Connect with IvanAI-Native Course: Intro to AI for WorkRelated Episode: Enterprise AI Agents with Jun Qian, VP of Generative AI Services at OracleExplore AI-Native Learning on DataCampNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Quantum computing is advancing fast, but it comes with a core industry challenge: noise. The big promise—better simulations, faster optimization, and maybe new kinds of AI—depends on quantum error correction and scaling from physical qubits to reliable logical qubits. For working professionals, that translates into system design questions, not just theory. How do you budget for the overhead of error correction? What does a hybrid quantum‑classical workflow look like when classical processors must process error data in real time? If a quantum approach shows “advantage” today, how do you know a better classical heuristic won’t catch up next month? Where should you focus first: hardware readiness or use cases?Dr. Yonatan Cohen is a physicist, entrepreneur, and co-founder of Quantum Machines, where he serves as Chief Technology Officer. He earned his Ph.D. at the Weizmann Institute of Science in Israel, focusing on quantum electronics, superconducting–semiconducting devices, and microfabrication. He is also a co-founder and former managing director of the Weizmann Institute’s entrepreneurship program and has published extensively in peer-reviewed journals, with recognized contributions to quantum computing. As CTO, Dr. Cohen has played a key role in developing the Quantum Orchestration Platform, a first-of-its-kind control and operating system for quantum computers that accelerates the path to practical, useful quantum systems.In the episode, Richie and Yonatan explore near-term quantum simulation, encryption risks, the open question of quantum AI, noisy qubits and error correction, physical vs logical scaling, the need for algorithms and use cases, how to try quantum coding via Amazon Braket, and much more.Links Mentioned in the Show:Quantum MachinesAmazon BraketIBM QiskitNVIDIA Cuda QuantumGoogle CirqConnect with YonatanAI-Native Course: Intro to AI for WorkRelated Episode: Developing Better Predictive Models with Graph Transformers with Jure Leskovec, Pioneer of Graph Transformers, Professor at StanfordExplore AI-Native Learning on DataCampNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

AI moves fast, and the news cycle can feel like a fire hose. New tools like agents and digital twins promise to help, but they also add more choices and noise. In day-to-day work, the challenge is less about knowing every breakthrough and more about deciding what matters, then making time to act. How do you cut meetings down, say no without friction, and still ship real work? How do you open your mind to new ideas while avoiding hype? And when you do spot a signal, how do you turn it into action across teams, stakeholders, and shifting priorities.As the Head of Global Innovation at ServiceNow, Brian Solis drives vision and strategy for future-focused innovation. He has three decades of experience as a technology leader, and Forbes called him "one of the more creative and brilliant business minds of our time". Previously, Brian was VP of Global Innovation at Salesforce. He has written nine books, including the best selling "Mindshift". Brian is an author of the ServiceNow Enterprise AI Maturity Index 2025 Report.In the episode, Richie and Brian explore the challenges of staying updated with AI advancements, the importance of mindset shifts for innovation, the role of storytelling in driving change, and practical strategies for managing information overload, fostering organizational transformation, and much more.Links Mentioned in the Show:Brian’s Book: MindshiftServiceNowConnect with BrianAI-Native Course: Intro to AI for WorkRelated Episode: The New Paradigm for Enterprise AI Governance with Blake Brannon, Chief Innovation Officer at OneTrustExplore AI-Native Learning on DataCampNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

AI leaders talk about innovation, but the wider reality is messy: fast change, uneven guardrails, and threats that span cyber, reputation, and customer harm. Industry-wide, organizations are shifting from one-off compliance to lifecycle governance—from inception to decommissioning—supported by boards, CEOs, and frontline teams. For professionals, that shows up as coordination work: shared metrics, incentives for responsible delivery, embedded ethics partners, and rapid-response groups when a new risk appears. How do you decide who is accountable for model behavior? What signals should trigger escalation? And what sources can you trust to stay informed without getting overwhelmed?Andrea Bonime-Blanc, JD/PhD, is founder and CEO of GEC Risk Advisory, a board member, strategic advisor, and award-winning author. She specializes in the governance of change, advising companies, NGOs, and governments on global strategic risk, leadership trust, geopolitics, sustainability, cyber resilience, and exponential technologies. A former C-suite executive at four global companies, including Bertelsmann and PSEG, she has held roles spanning legal, risk, ethics, sustainability, and cybersecurity, and currently serves on multiple boards and advisory boards.Andrea is a Senior Fellow at The Conference Board, NYU’s Center for Global Affairs, and an AI Ethics Strategy Fellow at the American College for Financial Services. She is a sought-after keynote speaker and media commentator, appearing in outlets such as Bloomberg, the Financial Times, and The New York Times. She is the author of several books, including Gloom to Boom and most recently, Governing Pandora: Leading in the Age of Generative AI and Exponential Technology.In the episode, Richie and Andrea explore the rapid advancements in AI, the balance between innovation and risk, the importance of adaptive governance, the role of leadership in tech governance, and the integration of ethics in AI development, and much more.Links Mentioned in the Show:Andrea’s Book—Governing Pandora: Leading in the Age of Generative AI and Exponential TechnologyMIT AI Risk RepositoryConnect with AndreaAI-Native Course: Intro to AI for WorkRelated Episode: Rebuilding Trust in the Digital Age with Jimmy Wales, Founder at WikipediaExplore AI-Native Learning on DataCampNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Data and AI teams are drowning in tools, but the big trend is consolidation and speed. AI-driven building is making dashboards, internal apps, and even data workflows feel more like products than reports. Custom interfaces, interactive presentations, and ad hoc apps are becoming easier to create than traditional BI artifacts.For working professionals, this raises practical questions: should you build a bespoke reporting site instead of another spreadsheet? Can you connect secure data views and prevent leaks by design? What does quality control look like when an agent writes the code—separate chats, clear plans, and tests? And what’s the real cost of going from idea to deployed app: a few dollars, or hundreds?Matt Palmer works at the intersection of developer experience, product marketing, and AI education. Leading Developer Relations at Replit, he helped grow Replit's revenue from $5M to $100M+. He creates content on vibe-coding, data transformation, AI, and more—blending technical depth with accessibility to empower developers and make complex tools approachable.In the episode, Richie and Matt explore the power of vibe coding, how non-developers are building impactful tools, the potential of AI in app development, the role of Replit in simplifying coding, and the future of personalized applications in data teams, and much more.Links Mentioned in the Show:ReplitCourse: Vibe Coding with ReplitYour Guide to ReplitConnect with MattAI-Native Course: Intro to AI for WorkRelated Episode: Building & Managing Human+Agent Hybrid Teams with Karen Ng, Head of Product at HubSpotExplore AI-Native Learning on DataCampNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

AI tools are becoming part of daily work for more professionals than ever before, yet adoption rates vary significantly across functions and company sizes. What separates organizations that successfully integrate AI from those that struggle? How do psychological factors like identity and autonomy shape how workers respond to AI implementation? And what role does corporate culture play in determining whether AI becomes a source of innovation or a point of resistance?Stefano Puntoni is the Sebastian S. Kresge Professor of Marketing at The Wharton School. Prior to joining Penn, Stefano was a professor of marketing and head of department at the Rotterdam School of Management, Erasmus University, in the Netherlands. He holds a PhD in marketing from London Business School and a degree in Statistics and Economics from the University of Padova, in his native Italy. His research has appeared in several leading journals, including Journal of Consumer Research, Journal of Marketing Research, Journal of Marketing, Nature Human Behavior, and Management Science. He also writes regularly for managerial outlets such as Harvard Business Review and MIT Sloan Management Review. Most of his ongoing research investigates how new technology is changing consumption and society, including how humans are adopting and evolving with AI.He is a former MSI Young Scholar and MSI Scholar, and the winner of several grants and awards. He is currently an Associate Editor at the Journal of Consumer Research and at the Journal of Marketing. Stefano teaches in the areas of marketing strategy, new technologies, brand management, and decision making.In the episode, Richie and Stefano explore the challenges of AI adoption in businesses, the psychological impacts on workers, the balance between human expertise and AI, the potential mental health effects of AI chatbots, and much more.Links Mentioned in the Show:Wharton SchoolConnect with StefanoMIT Report—The GenAI Divide: State of AI in Business 2025Wharton Report—Gen AI Fast-Tracks Into the EnterpriseAI-Native Course: Intro to AI for WorkRelated Episode: How to Build AI Your Users Can Trust with David Colwell, VP of AI & ML at TricentisExplore AI-Native Learning on DataCampNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

2026 is shaping up to be a pivotal year for data, AI, and how we work. From step-change improvements in foundation models to AI-native workflows reshaping careers, commerce, and education, the pace of change shows no signs of slowing. After revisiting and scoring their previous predictions, Richie, Jo, and Martijn turn their focus to what’s coming next in 2026.Building on last year’s discussion, we explore how AI will transform hiring and career progression, why personal AI tutors could become the default learning experience, how AI agents may begin executing real economic activity, and whether we’re on the brink of another “GPT-3 moment” driven by new hardware and scaling.Links Mentioned in the Show:Blog: The Junior Hiring CrisisBlog: The agentic commerce opportunity: How AI agents are ushering in a new era for consumers and merchantsAlex Banks on the ChatGPT era endingSpec & Evals Driven Agent Development (SEDAD) TemplateAI-Native Course: Intro to AI for WorkRelated Episode: Reviewing Our Data Trends & Predictions of 2025 with DataCamp's CEO & COO, Jonathan Cornelissen & Martijn TheuwissenExplore AI-Native Learning on DataCampNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

2025 was another huge year for data and AI. Generative AI continued to reshape how we work and interact with technology, with organizations moving beyond experimentation and pushing AI firmly into production. We saw major progress in foundation models, the rise of long-running AI agents, production-ready generative video, and wider adoption of synthetic data. At the same time, AI literacy, adoption, and ROI became central concerns for boards and executives, not just technical teams.This time last year, DataCamp Co-Founders Jonathan and Martijn made a series of predictions about data and AI for 2025. Today, they join Richie to reflect on how those predictions played out—and to share their vision for where data and AI are headed next.In the episode, Richie, Jonathan, and Martijn review the real-world adoption of generative AI, the shift from hype to production, the growing importance of AI literacy and usage at the executive level, the rise of longer-running AI agents, the near-mainstreaming of generative video, Europe’s position in the global AI race, why educators may be among the biggest AI adopters, and why AI hype continues to thrive—plus what they got right, what they got wrong, and what comes next.Links Mentioned in the Show:The DataCamp Data & AI Literacy Report 2025AI-Native Course: Intro to AI for WorkRelated Episode: Data Trends & Predictions 2025 with DataCamp's CEO & COO, Jonathan Cornelissen & Martijn TheuwissenExplore AI-Native Learning on DataCampNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Self-service analytics has been a goal for data teams for years, but recent advances in AI are accelerating progress in unexpected ways. The combination of natural language interfaces and spreadsheet-like tools is lowering barriers to data access across organizations. But how do you balance the freedom of self-service with the need for governance and accuracy? What skills do analysts need to work effectively with AI systems that don't always produce the same results twice? And when AI-generated answers might be slightly off, how do you know when to trust them?Mike Palmer is Chief Executive Officer of Sigma , where he leads the company’s strategy and growth as a cloud-native analytics and business intelligence platform. Since joining Sigma in 2020, he has focused on expanding access to cloud data by enabling business users to analyze data warehouses through familiar, spreadsheet-based workflows. Prior to Sigma, Mike served as Chief Product Officer at Druva, where he was part of the executive team scaling the company’s cloud data management platform and supporting rapid revenue growth. Before that, he was EVP and Chief Product Officer at Veritas Technologies, leading the transformation and modernization of a large enterprise data protection portfolio following its separation from Symantec. Earlier in his career, he held senior general management and executive roles at Seagate Technology and Verizon Enterprise Solutions, overseeing large-scale cloud, security, and enterprise infrastructure businesses. Mike is based in San Francisco and has spent his career building and operating enterprise data and analytics platforms at scale.In the episode, Richie and Mike explore the journey towards self-service analytics, the role of AI in democratizing data access, the challenges of stochastic processes, the evolution of analytics applications, how businesses can leverage AI for personalized insights, the future of enterprise software, and much more.Links Mentioned in the Show:SigmaConnect with MikeCourse: Introduction to SigmaAI-Native Course: Intro to AI for WorkRelated Episode: Self-Service Generative AI Product Development at Credit Karma with Madelaine Daianu, Head of Data & AI at Credit KarmaExplore AI-Native Learning on DataCampNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

AI governance is becoming critical as organizations deploy more intelligent systems across their operations. With predictions of over a billion AI agents entering the workforce in the coming years, traditional governance approaches simply cannot keep pace. How do you ensure your AI systems are using data responsibly without slowing down innovation? What happens when an AI agent makes decisions that were never explicitly programmed? And how do you build governance processes that scale alongside rapidly expanding AI adoption while maintaining trust with customers and regulators?Blake Brannon is Chief Innovation Officer at OneTrust, where he leads product vision and strategic direction for the company’s AI-ready governance platform. He has been with OneTrust since 2017, previously serving as Chief Technology Officer, and has played a key role in scaling the platform to support privacy, data governance, risk, and responsible AI initiatives for large enterprises. Blake is based in Atlanta and holds an academic background from the Georgia Institute of Technology, with early research experience in network systems and wireless communications.In the episode, Richie and Blake explore AI governance disasters, the importance of consent and data use, the rise of AI agents, the challenges of scaling governance processes, the need for continuous observability, the role of governance committees, strategies for effective AI governance in organizations, and much more.Links Mentioned in the Show:OneTrustConnect with BlakeAI-Native Course: Intro to AI for WorkRelated Episode: From City Sewers to Sovereign AI with Russ Wilcox, CEO at ArtifexAIExplore AI-Native Learning on DataCampNew to DataCamp?Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business

2025 was the year AI stopped being a curiosity and started reshaping real work. From data analysts speeding up entire workflows in minutes, to managers learning how to lead hybrid teams of humans and agents, the pace of change has been relentless. Across DataFramed this year, one theme kept surfacing: AI isn’t replacing data professionals—it’s raising the bar on what good looks like. Skills are shifting, careers are becoming more fluid, and organizations are being forced to rethink how they build teams, make decisions, and govern technology that now reasons, plans, and acts on our behalf. This Best of 2025 episode pulls together the most important ideas, voices, and debates from a year that fundamentally changed how data and AI show up in practice.In this special year-end roundup, Richie revisits the standout moments from DataFramed in 2025, spanning careers, business intelligence, data literacy, AI agents, industry use cases, and responsible AI foundations. You’ll hear why the data analyst role is evolving rather than disappearing, how hybrid human–AI teams are becoming the norm, and why communication remains the most underrated skill in data careers, the state of BI and data storytelling, the shift from training to behavior change in data and AI literacy, the rapid rise of agentic systems powered by reasoning at inference time. We also dive into real-world applications across healthcare, finance, and enterprise operations, alongside hard truths about data quality, governance, and model lineage. Finally, we spotlight advances in data science, NLP, and synthetic data—rounding out a year defined by faster cycles, higher expectations, and a renewed focus on getting the fundamentals right as AI scales.Episodes Featured in this Recap:#326 Is the Data Analyst Role Dying Out? with Mo Chen, Data & Analytics Manager at NatWest Group#319 Building & Managing Human+Agent Hybrid Teams with Karen Ng, Head of Product at HubSpot#295 How To Get Hired As A Data Or AI Engineer with Deepak Goyal, CEO & Founder at Azurelib Academy#294 Six Skills Data Professionals Need To Succeed with Abhijit Bhaduri, Brand Evangelist & Former General Manager of Global L&D at Microsoft#333 Creating an AI-First Data Team with Bilal Zia, Head of Data Science & Analytics at DuoLingo#310 The State of BI in 2025 with Howard Dresner, Godfather of BI#306 The Next Generation of Business Intelligence with Colin Zima, CEO at Omni#298 Data Storytelling Skills to Increase Your Impact with Kat Greenbrook, Author of The Data Storyteller's Handbook#323 The Evolution of Data Literacy & AI Literacy with Jordan Morrow, Godfather of Data Literacy#305 RAG 2.0 and The New Era of RAG Agents with Douwe Kiela, CEO at Contextual AI, Adjunct Professor at Stanford University, Inventor of RAG#316 Enterprise AI Agents with Jun Qian, VP of Generative AI Services at Oracle#328 The Challenges of Enterprise Agentic AI with Manasi Vartak, Chief AI Architect at Cloudera#308 A Framework for GenAI App and Agent Development with Jerry Liu, CEO at LlamaIndex#312 Can we Create an AI Doctor? with Aldo Faisal, Professor in AI & Neuroscience at Imperial College#288 How Generative AI is Transforming Finance with Andrew Reiskind, CDO at Mastercard#311 The Human Element of AI-Driven Transformation with Steve Lucas, CEO at Boomi#303 Increasing Your Organization's AI Maturity with Iwo Szapar & Eryn Peters, Founders at AI Maturity Index#286 Data Science Trends from 2 Kaggle Grandmasters with Jean-Francois Puget, Distinguished Engineer at NVIDIA & Chris Deotte, Senior Data Scientist at NVIDIA#275 Did Gen AI Kill NLP? with Meri Nova, Technical Founder at Break into Data#307 Human Guardrails in Generative AI with Wendy Gonzalez & Duncan Curtis, CEO & SVP of Gen AI at SamaNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

The concept of sovereign AI is becoming increasingly critical in our interconnected world. Nations and organizations are grappling with who controls the data, infrastructure, and technology that power artificial intelligence systems. But what does this mean for your work in data science and AI implementation? How do you navigate the complex landscape of data ownership when building AI solutions? As geopolitical tensions influence technology development, understanding the nuances of AI sovereignty isn't just for governments—it's essential for anyone working with data and AI systems to ensure resilience and compliance in an uncertain future.Russ Wilcox is the CEO of ArtifexAI, advising organizations on technology strategy, AI governance, and policy analysis. With 16 years in machine learning and AI, he focuses on translating complex policy and emerging tech trends into actionable strategy. His work spans government, infrastructure, and enterprise, with a focus on connecting technical capabilities to real-world implementation. A two-time World Economic Forum speaker and TEDx presenter, Wilcox has advised government agencies and Fortune 500 companies on AI strategy, urban intelligence, and technology policy. He also serves on AI ethics boards, lectures at UCLA and Boston University, and develops NLP systems for public- and private-sector use. Russ provides strategic consulting and speaking on AI governance, technology competition, and sustainable infrastructure.In the episode, Richie and Russ explore the US-China AI race, the philosophical differences in AI approaches, the concept of sovereign AI, the role of data sovereignty, and the potential for AI to transform infrastructure and governance, and much more.Links Mentioned in the Show:ArtifexAIRuss’ WebsiteConnect with RussAI-Native Course: Intro to AI for WorkRelated Episode: Harnessing AI to Help Humanity with Sandy Pentland, HAI Fellow at StanfordRewatch RADAR AI New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

The internet has transformed how we access information, but it's also created unprecedented challenges around trust and reliability. How do we build digital spaces where collaboration thrives and quality information prevails? What separates toxic online environments from productive ones? The principles of neutrality, transparency, and assuming good faith have proven essential in creating sustainable knowledge communities. But these same principles extend far beyond the digital realm—they're fundamental to effective leadership, successful business relationships, and even political discourse. When trust breaks down, everything becomes more difficult. So what practical steps can we take to foster trust in our organizations and communities?Jimmy Wales is an American-British internet entrepreneur best known as the founder of Wikipedia and co-founder of Fandom. Trained in finance at Auburn University and the University of Alabama, he began his career in quantitative finance before moving into early web ventures, including Bomis and the free encyclopedia project Nupedia. In 2001, he launched Wikipedia, which quickly became one of the most visited websites in the world. To support its growth, he established the Wikimedia Foundation in 2003, where he continues to serve on the Board of Trustees and act as a public spokesperson. He later co-founded Fandom in 2004, expanding the wiki model to entertainment, gaming, and niche communities. Wales has also pursued experiments in collaborative journalism, including WikiTribune and its successor WT Social. His work in open knowledge has earned recognition from organizations such as the World Economic Forum, Time magazine, UNESCO, and the Electronic Frontier Foundation. He has held fellowships and board roles at institutions including Harvard’s Berkman Center and Creative Commons.In the episode, Richie and Jimmy explore the early challenges of Wikipedia, the importance of trust and neutrality, the role of AI in content creation, and much more.Links Mentioned in the Show:WikipediaJimmy’s New Book: The Seven Rules of TrustTrust CaféConnect with JimmyBlog: The Trust Triangle of LeadershipAI-Native Course: Intro to AI for WorkRelated Episode: How to Build AI Your Users Can Trust with David Colwell, VP of AI & ML at TricentisExplore AI-Native Learning on DataCampNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

The AI landscape is evolving at breakneck speed, with new capabilities emerging quarterly that redefine what's possible. For professionals across industries, this creates a constant need to reassess workflows and skills. How do you stay relevant when the technology keeps leapfrogging itself? What happens to traditional roles when AI can increasingly handle complex tasks that once required specialized expertise? With product-market fit becoming a moving target and new positions like forward-deployed engineers emerging, understanding how to navigate this shifting terrain is crucial. The winners won't just be those who adopt AI—but those who can continuously adapt as it evolves.Tomasz Tunguz is a General Partner at Theory Ventures, a $235m early-stage venture capital firm. He blogs at tomtunguz.com & co-authored Winning with Data. He has worked or works with Looker, Kustomer, Monte Carlo, Dremio, Omni, Hex, Spot, Arbitrum, Sui & many others. He was previously the product manager for Google's social media monetization team, including the Google-MySpace partnership, and managed the launches of AdSense into six new markets in Europe and Asia. Before Google, Tunguz developed systems for the Department of Homeland Security at Appian Corporation.In the episode, Richie and Tom explore the rapid investment in AI, the evolution of AI models like Gemini 3, the role of AI agents in productivity, the shifting job market, the impact of AI on customer success and product management, and much more.Links Mentioned in the Show:Theory VenturesConnect with TomTom’s BlogGavin Baker on MediumAI-Native Course: Intro to AI for WorkRelated Episode: Data & AI Trends in 2024, with Tom Tunguz, General Partner at Theory VenturesRewatch RADAR AI New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Data science leadership is about more than just technical expertise—it’s about building trust, embracing AI, and delivering real business impact. As organizations evolve toward AI-first strategies, data teams have an unprecedented opportunity to lead that transformation. But how do you turn a traditional analytics function into an AI-driven powerhouse that drives decision-making across the business? What’s the right structure to balance deep technical specialization with seamless business integration? From building credibility through high-impact forecasting to creating psychological safety around AI adoption, effective data leadership today requires both technical rigor and visionary communication. The landscape is shifting fast, but with the right approach, data science can stand as a true pillar of innovation alongside engineering, product, and design.Bilal Zia is currently the Head of Data Science & Analytics at Duolingo, an EdTech company whose mission is to develop the best education in the world and make it universally available. Previously, he spent two years helping to build and lead an interdisciplinary Central Science team at Amazon, comprising economists, data and applied scientists, survey specialists, user researchers, and engineers. Before that, he spent fifteen years in the Research Department of the World Bank in Washington, D.C., pursuing an applied academic career. He holds a Ph.D. in Economics from the Massachusetts Institute of Technology, and his interests span economics, data science, machine learning/AI, psychology, and user research.In the episode, Richie and Bilal explore rebuilding an underperforming data team, fostering trust with leadership, embedding data scientists within product teams, leveraging AI for productivity, the future of synthetic A/B testing, and much more.Links Mentioned in the Show:DuolingoDuolingo Blog: How machine learning supercharged our revenue by millions of dollarsConnect with BilalAI-Native Course: Intro to AI for WorkRelated Episode: The Future of Data & AI Education Just Arrived with Jonathan Cornelissen & Yusuf SaberRewatch RADAR AI New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

The relationship between data governance and AI quality is more critical than ever. As organizations rush to implement AI solutions, many are discovering that without proper data hygiene and testing protocols, they're building on shaky foundations. How do you ensure your AI systems are making decisions based on accurate, appropriate information? What benchmarking strategies can help you measure real improvement rather than just increased output? With AI now touching everything from code generation to legal documents, the consequences of poor quality control extend far beyond simple errors—they can damage reputation, violate regulations, or even put licenses at risk.David Colwell is the Vice President of Artificial Intelligence and Machine Learning at Tricentis, a global leader in continuous testing and quality engineering. He founded the company’s AI division in 2018 with a mission to make quality assurance more effective and engaging through applied AI innovation. With over 15 years of experience in AI, software testing, and automation, David has played a key role in shaping Tricentis’ intelligent testing strategy. His team developed Vision AI, a patented computer vision–based automation capability within Tosca, and continues to pioneer work in large language model agents and AI-driven quality engineering. Before joining Tricentis, David led testing and innovation initiatives at DX Solutions and OnePath, building automation frameworks and leading teams to deliver scalable, AI-enabled testing solutions. Based in Sydney, he remains focused on advancing practical, trustworthy applications of AI in enterprise software development.In the episode, Richie and David explore AI disasters in legal settings, the balance between AI productivity and quality, the evolving role of data scientists, and the importance of benchmarks and data governance in AI development, and much more.Links Mentioned in the Show:Tricentis 2025 Quality Transformation ReportConnect with DavidCourse: Artificial Intelligence (AI) LeadershipRelated Episode: Building & Managing Human+Agent Hybrid Teams with Karen Ng, Head of Product at HubSpotRewatch RADAR AI New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

The future of education is being reshaped by AI-powered personalization. Traditional online learning platforms offer static content that doesn't adapt to individual needs, but new technologies are creating truly interactive experiences that respond to each learner's context, pace, and goals. How can personalized AI tutoring bridge the gap between mass education and the gold standard of one-on-one human tutoring? What if every professional could have a private tutor that understands their industry, role, and specific challenges? As organizations invest in upskilling their workforce, the question becomes: how can we leverage AI to make learning more engaging, effective, and accessible for everyone?As the Co-Founder & CEO of DataCamp, Jonathan Cornelissen has helped grow DataCamp to upskill over 10M+ learners and 2800+ teams and enterprise clients. He is interested in everything related to data science, education, and entrepreneurship. He holds a Ph.D. in financial econometrics and was the original author of an R package for quantitative finance.Yusuf Saber is a technology leader and entrepreneur with extensive experience building and scaling data-driven organizations across the Middle East. He is the Founder of Optima and a Venture Partner at COTU Ventures, with previous leadership roles at talabat, including VP of Data and Senior Director of Data Science and Engineering. Earlier in his career, he co-founded BulkWhiz and Trustious, and led data science initiatives at Careem. Yusuf holds research experience from ETH Zurich and began his career as an engineering intern at Mentor Graphics.In the episode, Richie, Jo and Yusuf explore the innovative AI-driven learning platform Optima, its unique approach to personalized education, the potential for AI to enhance learning experiences, the future of AI in education, the challenges and opportunities in creating dynamic, context-aware learning environments, and much more.Links Mentioned in the Show:Read more about the announcementTry the AI-Native Courses:Intro to SQLIntro to AI for WorkNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for busines

Data storytelling isn't just about presenting numbers—it's about creating shared wisdom that drives better decision-making. In our increasingly polarized world, we often miss that most people actually have reasonable views hidden behind the loudest voices. But how can technology help us cut through the noise and build genuine understanding? What if AI could help us share stories across different communities and contexts, making our collective knowledge more accessible? From reducing unnecessary meetings to enabling more effective collaboration, the way we exchange information is evolving rapidly. Are you prepared for a future where AI helps us communicate more effectively rather than replacing human judgment?Professor Alex “Sandy” Pentland is a leading computational scientist, co-founder of the MIT Media Lab and Media Lab Asia, and a HAI Fellow at Stanford. Recognized by Forbes as one of the world’s most powerful data scientists, he played a key role in shaping the GDPR through the World Economic Forum and contributed to the UN’s Sustainable Development Goals as one of the Secretary General’s “Data Revolutionaries.” His accolades include MIT’s Toshiba Chair, election to the U.S. National Academy of Engineering, the Harvard Business Review McKinsey Award, and the DARPA 40th Anniversary of the Internet Award. Pentland has served on advisory boards for organizations such as the UN Secretary General, UN Foundation, Consumers Union, and formerly for the OECD, Google, AT&T, and Nissan. Companies originating from his lab have driven major innovations, including India’s Aadhaar digital identity system, Alibaba’s news and advertising arm, and the world’s largest rural health service network.His more recent ventures span mental health (Ginger.io), AI interaction management (Cogito), delivery optimization (Wise Systems), financial privacy (Akoya), and fairness in social services (Prosperia). A mentor to over 80 PhD students—many now leading in academia, research, or entrepreneurship—Pentland helped pioneer fields such as computational social science, wearable computing, and modern biometrics. His books include Social Physics, Honest Signals, Building the New Economy, and Trusted Data.In the episode, Richie and Sandy explore the role of storytelling in data and AI, how technology reshapes our narratives, the impact of AI on decision-making, the importance of shared wisdom in communities, and much more.Links Mentioned in the Show:MIT Media LabSandy’s Booksdeliberation.ioConnect with SandySkill Track: Artificial Intelligence (AI) LeadershipRelated Episode: The Human Element of AI-Driven Transformation with Steve Lucas, CEO at BoomiRewatch RADAR AI New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Data quality and AI reliability are two sides of the same coin in today's technology landscape. Organizations rushing to implement AI solutions often discover that their underlying data infrastructure isn't prepared for these new demands. But what specific data quality controls are needed to support successful AI implementations? How do you monitor unstructured data that feeds into your AI systems? When hallucinations occur, is it really the model at fault, or is your data the true culprit? Understanding the relationship between data quality and AI performance is becoming essential knowledge for professionals looking to build trustworthy AI systems.Shane Murray is a seasoned data and analytics executive with extensive experience leading digital transformation and data strategy across global media and technology organizations. He currently serves as Senior Vice President of Digital Platform Analytics at Versant Media, where he oversees the development and optimization of analytics capabilities that drive audience engagement and business growth. In addition to his corporate leadership role, he is a founding member of InvestInData, an angel investor collective of data leaders supporting early-stage startups advancing innovation in data and AI. Prior to joining Versant Media, Shane spent over three years at Monte Carlo, where he helped shape AI product strategy and customer success initiatives as Field CTO.Earlier, he spent nearly a decade at The New York Times, culminating as SVP of Data & Insights, where he was instrumental in scaling the company’s data platforms and analytics functions during its digital transformation. His earlier career includes senior analytics roles at Accenture Interactive, Memetrics, and Woolcott Research. Based in New York, Shane continues to be an active voice in the data community, blending strategic vision with deep technical expertise to advance the role of data in modern business.In the episode, Richie and Shane explore AI disasters and success stories, the concept of being AI-ready, essential roles and skills for AI projects, data quality's impact on AI, and much more.Links Mentioned in the Show:Versant MediaConnect with ShaneCourse: Responsible AI PracticesRelated Episode: Scaling Data Quality in the Age of Generative AI with Barr Moses, CEO of Monte Carlo Data, Prukalpa Sankar, Cofounder at Atlan, and George Fraser, CEO at FivetranRewatch RADAR AI New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

The promise of AI in enterprise settings is enormous, but so are the privacy and security challenges. How do you harness AI's capabilities while keeping sensitive data protected within your organization's boundaries? Private AI—using your own models, data, and infrastructure—offers a solution, but implementation isn't straightforward. What governance frameworks need to be in place? How do you evaluate non-deterministic AI systems? When should you build in-house versus leveraging cloud services? As data and software teams evolve in this new landscape, understanding the technical requirements and workflow changes is essential for organizations looking to maintain control over their AI destiny.Manasi Vartak is Chief AI Architect and VP of Product Management (AI Platform) at Cloudera. She is a product and AI leader with more than a decade of experience at the intersection of AI infrastructure, enterprise software, and go-to-market strategy. At Cloudera, she leads product and engineering teams building low-code and high-code generative AI platforms, driving the company’s enterprise AI strategy and enabling trusted AI adoption across global organizations. Before joining Cloudera through its acquisition of Verta, Manasi was the founder and CEO of Verta, where she transformed her MIT research into enterprise-ready ML infrastructure. She scaled the company to multi-million ARR, serving Fortune 500 clients in finance, insurance, and capital markets, and led the launch of enterprise MLOps and GenAI products used in mission-critical workloads. Manasi earned her PhD in Computer Science from MIT, where she pioneered model management systems such as ModelDB — foundational work that influenced the development of tools like MLflow. Earlier in her career, she held research and engineering roles at Twitter, Facebook, Google, and Microsoft.In the episode, Richie and Manasi explore AI's role in financial services, the challenges of AI adoption in enterprises, the importance of data governance, the evolving skills needed for AI development, the future of AI agents, and much more.Links Mentioned in the Show:ClouderaCloudera Evolve ConferenceCloudera Agent StudioConnect with ManasiCourse: Introduction to AI AgentsRelated Episode: RAG 2.0 and The New Era of RAG Agents with Douwe Kiela, CEO at Contextual AI & Adjunct Professor at Stanford UniversityRewatch RADAR AI New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

The journey from startup to billion-dollar enterprise requires more than just a great product—it demands strategic alignment between sales and marketing. How do you identify your ideal customer profile when you're just starting out? What data signals help you find the twins of your successful early adopters? With AI now automating everything from competitive analysis to content creation, the traditional boundaries between departments are blurring. But what personality traits should you look for when building teams that can scale with your growth? And how do you ensure your data strategy supports rather than hinders your AI ambitions in this rapidly evolving landscape?Denise Persson is CMO at Snowflake and has 20 years of technology marketing experience at high-growth companies. Prior to joining Snowflake, she served as CMO for Apigee, an API platform company that went public in 2015 and Google acquired in 2016. She began her career at collaboration software company Genesys, where she built and led a global marketing organization. Denise also helped lead Genesys through its expansion to become a successful IPO and acquired company. Denise holds a BA in Business Administration and Economics from Stockholm University, and holds an MBA from Georgetown University.Chris Degnan is the former CRO at Snowflake and has over 15 years of enterprise technology sales experience. Before working at Snowflake, Chris served as the AVP of the West at EMC, and prior to that as VP Western Region at Aveksa, where he helped grow the business 250% year-over-year. Before Aveksa, Chris spent eight years at EMC and managed a team responsible for 175 select accounts. Prior to EMC, Chris worked in enterprise sales at Informatica and Covalent Technologies (acquired by VMware). He holds a BA from the University of Delaware.In the episode, Richie, Denise, and Chris explore the journey to a billion-dollar ARR, the importance of customer obsession, aligning sales and marketing, leveraging data for decision-making, and the role of AI in scaling operations, and much more.Links Mentioned in the Show:SnowflakeSnowflake BUILDConnect with Denise and ChrisSnowflake is FREE on DataCamp this weekRelated Episode: Adding AI to the Data Warehouse with Sridhar Ramaswamy, CEO at SnowflakeRewatch RADAR AI New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

The role of data analysts is evolving, not disappearing. With generative AI transforming the industry, many wonder if their analytical skills will soon become obsolete. But how is the relationship between human expertise and AI tools really changing? While AI excels at coding, debugging, and automating repetitive tasks, it struggles with understanding complex business problems and domain-specific challenges. What skills should today's data professionals focus on to remain relevant? How can you leverage AI as a partner rather than viewing it as a replacement? The balance between technical expertise and business acumen has never been more critical in navigating this changing landscape.Mo Chen is a Data & Analytics Manager with over seven years of experience in financial and banking data. Currently at NatWest Group, Mo leads initiatives that enhance data management, automate reporting, and improve decision-making across the organization. After earning an MSc in Finance & Economics from the University of St Andrews, Mo launched a career in risk and credit portfolio management before transitioning into analytics. Blending economics, finance, and data engineering, Mo is skilled at turning large-scale financial data into actionable insight that supports efficiency and strategic planning. Beyond corporate life, Mo has become a passionate educator and community-builder. On YouTube, Mo hosts a fast-growing channel (185K+ subscribers, with millions of views) where he breaks down complex analytics concepts into bite-sized, actionable lessons.In the episode, Richie and Mo explore the evolving role of data analysts, the impact of AI on coding and debugging, the importance of domain knowledge for career switchers, effective communication strategies in data analysis, and much more.Links Mentioned in the Show:Mo’s Website - Build a Data Portfolio WebsiteMo’s YouTube ChannelConnect with MoGet Certified as a Data AnalystRelated Episode: Career Skills for Data Professionals with Wes Kao, Co-Founder of MavenRewatch RADAR AI New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business