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DataFramed

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Welcome to DataFramed, a weekly podcast exploring how artificial intelligence and data are changing the world around us. On this show, we invite data & AI leaders at the forefront of the data revolution to share their insights and experiences into how they lead the charge in this era of AI. Whether you're a beginner looking to gain insights into a career in data & AI, a practitioner needing to stay up-to-date on the latest tools and trends, or a leader looking to transform how your organization uses data & AI, there's something here for everyone.

Join host Richie Cotton as he delves into the stories and ideas that are shaping the future of data. Subscribe to the show and tune in to the latest episode on the feed below.

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Yesterday · 50 min
# 386 Sales and Marketing Don't Care About Agents | Duncan Lennox, CPTO at HubSpot
AI agents are being pitched for almost every part of sales, marketing, and customer support, from AI SDRs to support bots that resolve tickets without a person. But most businesses aren't excited about agents for their own sake. They want more leads, more closed deals, and happier customers. For go-to-market teams, that raises practical questions about where automation helps and where people still need to stay involved. How much of the sales process can an agent handle? How do you build enough trust to let one talk to your customers? And how do you know it's working? Duncan Lennox is the Chief Product and Technology Officer at HubSpot, where he oversees the product portfolio and leads Engineering, Product, UX, Next Bets, and IT and Security. Before HubSpot, he held roles at Google, most recently as general manager of the Applied AI team in Google Cloud, and at Amazon Web Services, where he was general manager of Elastic File System. He also co-founded two enterprise SaaS startups, serving as CEO and CTO. In the episode, Richie and Duncan explore automating sales with AI SDRs, how agents resolve over 70% of support tickets, building customer trust in agents, context engineering and evals, the humanity score for avoiding AI slop, model-agnostic agent harnesses, controlling AI costs, outcome-based pricing, the headless SaaS strategy, and much more. Links Mentioned in the Show: • HubSpot Customer Agent • HubSpot Prospecting Agent • Breeze Assistant • HubSpot Breeze agents overview • Lenny's Newsletter • Connect with Duncan • AI-Native Course: Intro to AI for Work • Related Episode: Don't Build on Jell-O: How to Make Agentic AI Reliable with Dan Klein, CTO at Scaled Cognition New 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
November 2 · 52 min
#385 Creating an Engine of Value from AI | Jim Rowan, Head of AI at Deloitte USA
Most large organizations have bought into AI, yet far fewer have turned it into measurable value. Surveys of thousands of business leaders show the same pattern: spending on technology is up, while investment in data foundations, process redesign, and people lags behind. For data and AI professionals, that gap shows up as pilots that never reach production, automated steps that only move the bottleneck, and teams unsure how to use the tools they've been given. How do you fix messy data before AI agents arrive? How do you redesign a process instead of speeding up a broken one? And how do you get people to actually use AI? Jim Rowan is a Principal and the US Head of AI at Deloitte Consulting LLP, where he works with technology organizations, clients, and Deloitte business leaders on their AI goals. He previously served as National Managing Partner for the US Strategy & Analytics practice and as US AI & Data Operations Offering Leader, and he was one of the authors of Deloitte's State of AI in the Enterprise report. In the episode, Richie and Jim explore why enterprise AI stalls, building the data foundation, writing business cases that hold up as models change, rethinking org charts and processes, leadership's role, build versus buy, controlling token costs, change management and continuous learning, and much more. Links Mentioned in the Show: • Deloitte: State of AI in the Enterprise • Deloitte Enterprise AI Navigator • Ethan Mollick: Co-Existence • The Rundown AI newsletter • Connect with Jim: LinkedIn • AI-Native Course: Intro to AI for Work • Related Episode: How to Thrive in a World of Continuous Transformation with Phil Le-Brun and Jana Werner, Executives in Residence at AWS New 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
October 26 · 53 min
#384 Is the Cloud Over? | Sergio Gago, CTO at Cloudera
AI agents are only as useful as the data they can reach, and most company data is scattered across clouds, data centers, and legacy systems. Giving agents access is easy to get wrong, especially when the data includes salaries, customer records, or financial results. That's why governance, permissions, and audit trails are becoming the main bottleneck for getting agents into production. How do you give an agent the right access without exposing sensitive information? Who owns that job? And do traditional ideas like data fabrics and data meshes still hold up? Sergio Gago is the CTO at Cloudera. He previously held senior roles at Moody's Analytics, most recently as managing director of AI/ML and quantum, and was CTO of Rakuten's Spanish operations and of digital publishing company Zinio. In the episode, Richie and Sergio explore hybrid infrastructure for AI, choosing between cloud, neo clouds and on-premise, data fabrics and governance for agents, why agents need data access, the changing role of data teams, ephemeral dashboards, skills for engineers, sovereign AI, and much more. Links Mentioned in the Show: • Cloudera: The Great AI Rearchitecture report • Thoughtworks: Core principles of data mesh • Cloudera Unified Data Fabric • Apache Ranger • OpenClaw explained • Trino • Connect with Sergio • AI-Native Course: Intro to AI for Work • Related Episode: AI Agents Are the New Shadow IT (And Your Governance Isn't Ready) with Stijn Christiaens, Chief Data Citizen at Collibra New 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
October 19 · 1 hr 5 min
#383 How to Coordinate Intelligence Across Teams & Agents | Vijoy Pandey, SVP of Outshift at Cisco
AI agents are getting easier to build and connect. Open standards now let them discover each other, pass messages and use shared tools. But connecting agents is not the same as getting them to work toward the same goal, and research on multi-agent systems shows that misalignment grows with every agent you add. For professionals deploying agents, this shows up as handoffs that fail and results that don’t add up, even when each agent is right on its own. How do agents agree on what a task means? What does a team need in place before adding more agents? And how do you know the combined result is better? Vijoy Pandey is SVP and GM of Outshift by Cisco, the company’s incubation engine for emerging technologies such as agentic AI and quantum networking. He has spent about three decades building distributed infrastructure at Google, IBM and startups, holds a PhD in Computer Science from UC Davis, and has filed more than 80 patents. In the episode, Richie and Vijoy explore the cognitive revolution and what it means for agents, the Internet of Cognition, coordination failures in multi-agent systems, agent protocols and guardrails, task-based access control, tiny teams with tokens (T3), organizational memory and context leaders, measuring whether agents are actually better, emergent collective intelligence, and much more. Links Mentioned in the Show: AGNTCY: open-source agent connectivity project Model Context Protocol (MCP) and Agent2Agent protocol (A2A) Outshift: Internet of Cognition Coherent Cognition, Vijoy’s Substack Michael Tomasello: A Natural History of Human Thinking Michael Levin: talks and interviews Connect with Vijoy AI-Native Course: Intro to AI for Work Related Episode: AI Agents Are the New Shadow IT (And Your Governance Isn’t Ready) with Stijn Christiaens, CEO at Collibra New 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
October 15 · 31 min
#382 What DataCamp's Learner Data Says About Learning | Pieter Moors, Lead Data Scientist at DataCamp
Career goals in data have shifted quickly. Data scientist was long the default target, but AI engineer has appeared from almost nowhere, and companies are moving training toward AI literacy, agents, and prompt engineering. Certifications are becoming a common way to prove these skills, while many people wonder whether learning to code still matters when AI can write it for them. What are people choosing to learn right now? Which fundamentals still hold up? How do you prepare for a certification exam? And is the fear of AI replacing technical roles showing up in what people actually study? Pieter Moors is the Lead Data Scientist at DataCamp, where he works with the company's learner data to understand how people learn and how that behavior changes over time. He is part of DataCamp's new Data Insights program, which shares findings from that data with the world every couple of weeks. In the episode, Richie and Pieter explore what DataCamp's own learner data reveals, when enterprise and individual learners study, how the World Cup disrupted study time, why short regular sessions and streaks build habits, how AI tutors relate to certification pass rates, the shift from data scientist to AI engineer, and much more. Links Mentioned in the Show: Connect with Pieter DataCamp AI Tutor DataCamp Certifications Introduction to Python course DataCamp AI Assistant DataCamp mobile app DataCamp subreddit AI-Native Course: Intro to AI for Work Related Episode: The Best Moments from RADAR 11x: AI, Jobs, Agents and Skills New 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
October 12 · 50 min
#381 Escaping the AI Hiring Doom Loop with Daniel Chait, CEO & Ophir Samson, Head of Voice AI at Greenhouse
Most hiring decisions still come down to gut feel, and gut feel is full of blind spots — a favorite school, a firm handshake, the type of coffee someone carries into the room. As AI reshapes recruiting, the industry is being forced to ask whether decisions can be made on evidence instead of instinct. That means defining what actually predicts success in a role, testing every candidate against the same criteria, and being able to show the reasoning behind a hiring call rather than just a hunch. For anyone building a team, the question isn't whether bias exists — it's whether your process is built to catch it before it shapes who gets hired. Daniel Chait is the co-founder and CEO of Greenhouse, the hiring software used by tens of thousands of companies to run structured, data-driven hiring processes. Ophir Samson is Greenhouse's Head of Voice AI, leading the team following Greenhouse's acquisition of Ezra AI Labs, the voice AI interviewing company he founded. In the episode, Richie, Daniel, and Ophir explore the flood of AI-generated resumes drowning both job seekers and recruiters, how Voice AI interviews let candidates showcase themselves beyond a resume, structured hiring and scorecards for fairer decisions, bias audits and anti-cheat safeguards, EU AI Act compliance, and measuring success through pass-through rates and time savings, and much more. Links Mentioned in the Show: • Zapier <> Greenhouse: Conversational Application Voice AI • Paradigm Strategy • Anthropic • Pipecat on GitHub • Daily • Are Emily and Greg More Employable Than Lakisha and Jamal? (AEA) • Connect with Daniel: LinkedIn • Connect with Ophir: LinkedIn • AI-Native Course: Intro to AI for Work • Related Episode: How to Have a Machine Learning Career in 2026 New 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
October 7 · 48 min
#380 The Best Moments from RADAR 11x: AI, Jobs, Agents and Skills
A job title can stay the same while the work underneath it changes completely. That idea ran through RADAR 11x, our online conference, and this recap keeps the best moments from four of the day's eight sessions. You will hear how to sort your tasks between you and AI, which durable skills hold their value, why pilots stall before production, and how software teams work with coding agents. Missed RADAR? Start here. Which tasks should AI take over? Which skills deserve your next hour of learning? Featured in this recap: Ben Zweig, CEO at Revelio Labs; Nicole Immorlica, Professor at Yale University and Senior Principal Researcher at Microsoft; Margaret Beier, Professor at Rice University; Matt Jones, EVP of Strategy at Cielo Talent; Krishnan Hariharan, CTO and VP of Engineering for Forge at Honeywell; Satesh Sonti, Principal Specialist Solutions Architect at AWS; Laurent Gil, President and co-founder at Cast AI; Joao Moura, CEO at CrewAI; Ali Arsanjani, Senior Director of Applied AI Engineering at Google; and DataCamp co-founders Jonathan Cornelissen (CEO) and Martijn Theuwissen (COO). In the episode, Richie rounds up the best moments from RADAR 11x, exploring whether AI will take your job, the durable skills worth building, why AI pilots stall, cutting token costs, how software teams work with agents, the future of the data analyst role, and much more. Links Mentioned in the Show: • We Are Not Machines (Sarah O'Connor) • Jevons paradox • AI-Driven Development Life Cycle (AI-DLC) (AWS) • The State of AI Careers 2026 (DataCamp) • Watch on demand: Humans On The Loop • Watch on demand: Don't Waste Your Time on AI Pilots • Watch on demand: The New Developer Workflow • Watch on demand: Closing and AMA • RADAR 11x event page • AI-Native Course: Intro to AI for Work • Related Episode: Data-Driven Workforce Analytics with Ben Zweig, CEO at Revelio Labs New 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
September 28 · 47 min
#379 The Secrets of Deploying AI in Production | Sumti Jairath, Chief Architect at SambaNova
AI agents are moving from demos into production, but many teams run into the same wall: responses that take too long and cost too much. As models grow bigger and reason through more steps before answering, every chained agent adds delay, and that adds up fast at scale. For data and AI professionals, this raises a practical question: how do you pick a model and an inference platform that keeps a multi-agent workflow fast without blowing the budget? And underneath that choice sits a bigger one — how much of the AI stack, from chips to software, should a company actually control? Sumti Jairath is Chief Architect at SambaNova Systems, where he's focused on machine learning, big data analytics, and software-defined hardware since 2017. Before that, he spent close to eight years as a Senior Hardware Architect at Oracle working on hardware acceleration for machine learning and data analytics, and earlier held processor and server design roles at Hewlett Packard and Sun Microsystems. He holds a B.Tech in Electronics and Communication Engineering from the National Institute of Technology, Kurukshetra. In the episode, Richie and Sumti explore why AI agents are slow and expensive, the architecture behind faster inference, choosing the right model and platform for a task, open-source models and sovereign AI, the full AI infrastructure stack, power efficiency, the AI data center debate, career paths in AI infrastructure, and much more. Links Mentioned in the Show: • NVIDIA's Groq acquisition, announced around GTC • Chris Olah, Anthropic • Anthropic and OpenAI's coding-agent-driven revenue growth • Ollama • Apple Mac mini • SambaNova careers • Connect with Sumti • AI-Native Course: Intro to AI for Work • Related Episode: From City Sewers to Sovereign AI with Russ Wilcox, CEO at ArtifexAI New 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
September 21 · 50 min
#378 The Data Engine for AI with Ledion Bitincka, CTO at Cribl & Nikhil Mungel, Head of AI R&D at Cribl
As agents take over more of the actual coding, the nature of technical work is shifting from producing software to judging it. Engineers increasingly spend their time specifying what should be built and then checking whether an agent's output actually solved the problem, rather than writing every line themselves. That changes what skills matter for a career in data and software: problem-solving and evaluation start to outweigh knowing today's specific tools or syntax. It also raises a harder question for teams — if agents can generate work this fast, how do you know which of it is actually worth shipping? Ledion Bitincka is Co-Founder and CTO of Cribl, where he leads the engineering organization with a first-principles approach to product delivery. Before Cribl, he was an Advanced Development Architect at Splunk, where he worked on Search-Time Schema, Hunk, and SmartStore, and before that founded Triangulus Communications. Nikhil Mungel is Head of AI R&D at Cribl, based in San Francisco, with over 15 years building distributed systems and AI teams at companies including Substack, Splunk, and ThoughtWorks — he now leads teams building LLM-powered systems for IT and security data. In the episode, Richie, Ledion, and Nikhil explore AI agent disasters and cost shocks, software telemetry fundamentals, using agents to analyze telemetry, AI-powered software factories, the shift from knowledge work to judgment work, skills for the agentic era, avoiding runaway AI spend, and measuring product value through growth metrics, and much more. Links Mentioned in the Show: • Jocko Willink's book, Leadership Strategy and Tactics: A Field Manual • Cribl • Cribl AI (Copilot) • Claude Code • AWS Graviton • NVIDIA • Connect with Ledion: LinkedIn • Connect with Nikhil: LinkedIn • AI-Native Course: Intro to AI for Work • Related Episode: AI Agents at Work: What Actually Breaks (and How to Fix It) New 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
September 14 · 38 min
#377 The Algorithm for Hypergrowth with Jon McNeill, CEO at DVx Ventures & Former President at Tesla
Hypergrowth companies rarely scale on strategy alone — they scale on how fast decisions get made and how much risk employees feel safe taking. A recurring idea in high-growth environments is treating decisions differently depending on whether they're reversible, and rewarding people for making an impact rather than simply avoiding mistakes. For managers and individual contributors alike, that shift changes what "good work" looks like day to day. Which of your team's decisions actually need a leader's sign-off, and which ones would move faster if people just tried something and adjusted? Jon McNeill is CEO and co-founder of DVx Ventures, a venture studio that has launched 12 companies. He previously served as President at Tesla, where revenue grew from $2B to $20B in 30 months, and as COO at Lyft through its IPO. A serial entrepreneur, he's founded and sold six companies, sits on the boards of Lululemon and Asurion, and wrote The Algorithm: The Hypergrowth Formula that Transformed Tesla, Lululemon, General Motors and SpaceX. In the episode, Richie and Jon explore the algorithm behind Tesla's 10X hypergrowth, why automation should always come last, how to find and delete unnecessary process steps, building a culture of curiosity and urgency, one-way vs. two-way door decisions, small-team organizational design, changing the currency of promotion, and running effective meetings, and much more. Links Mentioned in the Show: • The Algorithm: The Hypergrowth Formula that Transformed Tesla, Lululemon, General Motors and SpaceX by Jon McNeill • Incorruptible by Eric Ries • Unreasonable Hospitality by Will Guidara • Eleven Madison Park • Jensen Huang on LinkedIn • Karim Bousta on LinkedIn • Connect with Jon • AI-Native Course: Intro to AI for Work • Related Episode: How to Thrive in a World of Continuous Transformation New 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
September 7 · 42 min
#376 Rethinking the Data Stack in the age of AI with Tristan Handy, President of Fivetran + dbt Labs
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 Work Related Episodes: The Data Team's Agentic Future, with Ketan Karkhanis, CEO at ThoughtSpot Towards Self-Service Data Engineering with Taylor Brown, Co-Founder and COO at Fivetran New 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
August 31 · 47 min
#375 Is Math The Key to Better Coding AI? With Tudor Achim, CEO at Harmonic
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 app Empower your business with world-class data and AI skills with DataCamp for business
August 24 · 45 min
#374 How to Thrive in a World of Continuous Transformation | Phil Le Brun and Jana Werner, Executives in Residence at AWS
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 DataCamp New 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
August 17 · 52 min
#373 What Do Your Colleagues Do All Day? (The Value of Institutional Knowledge & AI for Process Reengineering) | Jennifer Smith, CEO at Scribe
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 & Company Aaron Levie, CEO of Box, followed by Jennifer on X Jaya Gupta, Partner at Foundation Capital Connect with Jennifer: 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 Crop, EVP at WNS New 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.
August 10 · 48 min
#372 Bulletproof Large Scale Data Science with Srini Raghavan, Chief Product Officer at Freshworks
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 Replit New 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.
August 3 · 39 min
#371 The Real Reason Your Product Team Needs a Feedback Loop with Todd Olson, CEO at Pendo
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 letter HubSpot’s 2024 terms-of-service backlash Ramp Stripe Fin (Intercom’s AI agent), recently announced to be acquired by Salesforce Anthropic / Claude Code Connect with Todd AI-Native Course: Intro to AI for Work Related Episode: The Data Team’s Agentic Future with Ketan Karkhanis, CEO at ThoughtSpot New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business
July 27 · 44 min
#370 Failure is Data (and Other Career Advice) | Todd Dewett, Leadership Author & Speaker
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 Wyss New to DataCamp? Learn on the go using the DataCamp mobile app
July 20 · 49 min
#369 How to Become a Top Business Intelligence Analyst | Helen Wall, Founder at Helen Data Design & Microsoft Influencer
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 ThoughtSpot New 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
July 13 · 50 min
#368 AI Agents Are Now Your Database's Main User | Reynold Xin, Co-Founder at Databricks
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-databases New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business
July 6 · 51 min
#367 Don't Build on Jell-O: How to Make Agentic AI Reliable with Dan Klein, CTO at Scaled Cognition
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-trust New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

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