DataFramed

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
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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
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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

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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

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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
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