AI Radicals

Data foundations are the real bottleneck for agentic AI, and most teams are governing the wrong layer.In this episode of AI Radicals, host Satyen Sangani talks with Sanjeev Mohan, industry analyst and author of Designing the AI-Driven Data Foundations, about why AI's biggest promises still rest on unglamorous data fundamentals, and why agents are breaking the old rules of governance.Sanjeev walks through his layered model of metadata, semantics, context, ontology, and knowledge graphs, and the two dig into where data products fit in that stack. They debate whether governance belongs to the agent or the data itself, why deterministic rules must live with the data rather than be re-explained to every model or prompt, and how agentic systems generate entirely new categories of data that nobody has a framework for governing yet. "There's a concept 100% related to the whole idea of context and data products, and that is governance. Now we are in the realm of AI, which is a very different beast to govern."Listen to this episode to learn:Why semantics, not storage or compute, is becoming the real moat in the AI stackWhy agentic governance has to cover input, output, and runtime, not just clean data in and clean data outWhy data teams are entering a golden era, shifting from backup-and-restore work to business strategy and reusable data products--------“ One definition is that data product is a philosophy. It's not a thing. And what data product says is that when you build your artifact, you build it with the product management best practices. For example, there's a data product owner, it has a version number, and it's backward compatible. It's packaged with SLAs and contracts. So if you are packaging your semantic layer and you're building it using the product management best practices, then that's a data product, very much so.” – Sanjeev Mohan--------Time Stamps[01:37] Sanjeev’s book Designing the AI-Driven Data Foundations[03:47] Defining metadata, semantics & context[07:28] Taxonomy, ontology & knowledge graphs[10:01] What is a data product?[20:24] From data governance to agentic governance[27:30] The context layer debate[31:32] The future of data people[38:15] Predictions for the next 90-180 days[42:50] Closing & key takeaways--------SponsorThis podcast is presented by Alation.Learn more:* Subscribe to the newsletter: https://caspianstudios71626.activehosted.com/f/73* Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/* Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/--------LinksConnect with Sanjeev Mohan on LinkedIn: https://www.linkedin.com/in/sanjmo/Designing the AI-Driven Data Foundations: Architecture, Principles, and Practice: https://www.wiley.com/en-us/shop/general-introductory-computer-science/designing-the-ai-driven-data-foundations-architecture-principles-and-practice-p-9781394396665#aboutauthors-section Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Cara Tice, Chief Data Officer at Early Warning Services, brings deep experience building data strategies across financial services. She explores why reusable data products are replacing monolithic platforms, how trusted data powers real-time fraud intelligence, and why AI governance depends on strong data governance. She also shares practical advice for new CDOs, safer AI experimentation, and preparing for autonomous agents.

Eugene Wu, a Columbia University associate professor and co-director of the Data Agents and Processes Lab, explores why reliable AI agents demand systems-level thinking. Topics include semantic coupling across agent stacks, searching massive data lakes, deterministic safety beyond prompts, and branchable environments that let agents experiment, fail, and roll back safely.

Charlene Li, bestselling author and leadership strategist, shares a practical 90-day roadmap for turning AI ambition into business value. They explore why AI belongs in business strategy, not just IT; why pilots stall before production; how balanced governance accelerates progress; and how an abundance mindset can transform call centers, banking, and the future of work.

Why the same old data infrastructure playbook won't survive contact with agentic AI, and what actually has to change underneath.In this episode of AI Radicals, host Satyen Sangani talks with IBM Software CTO Anant Jhingran about why enterprise AI's biggest bottleneck isn't the models, it's the decades-old data and integration infrastructure sitting underneath them.Anant and Satyen dig into why data fundamentals (provenance, metadata, systems of record) haven't changed, even as humans and fixed workflows give way to agents reasoning on the fly. They contrast "AI for data" with "data for AI," why AI's tolerance for messiness still doesn't excuse bad data, and why centralization may matter less as agents run quick, discovery-driven queries instead of big fixed reports. Anant also shares a new focus at IBM: rethinking whether one "golden" code path per product still makes sense when AI makes forking and personalizing variants easy."If you think that agents are just going to do the same thing that you're doing except machines instead of people, it's kind of boring, and I don't think that's going to happen. The real change is they're doing something different that we haven’t done before."Listen to this episode to learn:Why "AI for data" and "data for AI" are two distinct problems enterprises need to solve separatelyWhy agentic, discovery-driven workloads may reduce the need to centralize all your data, but raise the stakes on metadataWhy forking products into many tailored variants, instead of one shared code path, could reshape how software gets built--------“ Something that I wouldn't have thought of three months back or six months back, which is how do we actually build products. And the reason is very simple, is that if you just say that AI is going to help us build products faster, then it doesn't actually create a competitive differentiation because everybody else is creating products faster with AI. So you have to both do things differently and perhaps do different things.” – Anant Jhingran--------Time Stamps*(02:43): Is this AI moment different from past tech shifts?*(08:38): Data quality as a forcing function: "data for AI" vs "AI for data"*(14:23): How advanced is the industry in applying LLMs to old data problems?*(21:14): Federation's comeback & metadata vs. centralization*(32:16): IBM's three strategic pillars & building products differently in the AI era*(51:16): Takeaways--------SponsorThis podcast is presented by Alation.Learn more:* Subscribe to the newsletter: https://caspianstudios71626.activehosted.com/f/73* Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/* Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/--------LinksConnect with Anant Jhingran on LinkedIn: https://www.linkedin.com/in/anantjhingran/Learn more about IBM: https://www.ibm.com/us-enAnant’s Podcast Context Window: https://www.youtube.com/playlist?list=PLm-EPIkBI3YqXTgboKALGzNmGELWp_oTT Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Dave Wright, ServiceNow’s chief innovation officer, and Brian Solis, its futurist and author, explore why AI must go beyond automating old workflows. They contrast Ford’s costly automation reversal with IKEA’s bold redeployment of 8,200 agents, examine “mode one” optimization versus “mode two” reinvention, and discuss psychological safety, AI governance, accountable ownership, and the leadership needed to build continually relevant companies.

How data trust breaks—and how to rebuild it before AI makes it worse. In this episode of AI Radicals, host Satyen Sangani sits down with Nathalie Berdat, Data Director of Product at the BBC, to explore how one of the world's most trusted media institutions is rebuilding its data foundations for the AI era. Nathalie shares how she diagnosed a quiet trust crisis inside the BBC—teams producing conflicting numbers for the same metrics—and led a multi-year effort to fix it: identifying the handful of metrics that actually mattered, building certified "data products" as single sources of truth, and modernizing a legacy platform to support them at scale. She also unpacks why AI governance at a public institution carries different stakes than at a commercial company, how the BBC decides where genAI is (and isn't) allowed to touch editorial content, and what has to be true before agentic AI can responsibly run across an organization like the BBC. "The governance isn't a compliance checkbox, it's closer to editorial standards. It has to be defensible to a journalist." Listen to this episode to learn: Why low trust in data often shows up as two teams presenting two different numbers for the same metric and how to fix it Why the BBC treats AI governance as an editorial issue, especially when it comes to recommendations and content curation Why agentic AI requires clear data ownership, documented lineage, and machine-readable governance before it can be deployed responsibly -------- “ Building a data product that gives you a very trusted source of truth when it comes to who works and where and what cost center allows you to then expose this product and build on top something like return on investment for our content or program, because then you'll know who has worked, how much it cost us to build and develop a program.  You need to know your return on investment for something you'll be commissioning. You'll be investing a lot of effort and time and people on it.” – Nathalie Berdat -------- Time Stamps *(01:56): How the BBC differs from a commercial enterprise in AI governance *(06:51): Rebuilding trust in data at the BBC *(18:47): Building certified data products and driving adoption *(26:00): AI, context, and the data product as a foundation *(29:53): Editorial complexity: AI, personalization, and audience trust *(44:32): Satyen’s takeaways -------- Sponsor This podcast is presented by Alation. Learn more: * Subscribe to the newsletter: https://caspianstudios71626.activehosted.com/f/73 * Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/ * Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/ -------- Links Connect with Nathalie Berdat on LinkedIn: https://www.linkedin.com/in/nathalie-berdat-b716b56/ Learn more about BBC: https://www.bbc.com/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Francois Ajenstat, founder and CEO of Golden Analytics and former Tableau product chief, reflects on 30 years reshaping analytics. He explains why visualization was never the hard part. He demos Golden’s two-click dashboards, a “slider of autonomy,” and using multiple LLMs plus SQL. He also tackles context, metadata, model routing, latency, and pricing that align incentives with users.

Data quality problems don't just create bad reports; they create mistrust. And once trust is gone, people stop using your systems and start building their own workarounds. In this episode of AI Radicals, host Satyen Sangani sits down with Erin McIntosh, Vice President of Global Data Operations at CNA Insurance, to talk about what it actually takes to modernize data governance at a global commercial insurer in the age of agentic AI. Erin shares how CNA is rethinking decades-old governance playbooks, why "build vs. buy" decisions have been upended by new AI tooling, and how her team is shifting from automating decisions to actually improving them. Erin also opens up about the hardest part of leading transformation at speed: getting an organization to trust new systems, rebuild processes from the outcome backward instead of the process forward, and move from slow, bureaucratic governance to agentically-led governance that can actually scale. "Good data governance is actually effective. Bad data governance is actually slow and burdensome." Listen to this episode to learn: Why the shift from automating decisions to improving decisions is where real AI ROI comes from Why agentic governance—not more process—is the path to finally scaling stewardship, compliance, and data quality Why seeking perfection instead of progress is the biggest waste of time and money in AI deployments today -------- “ Each person had to learn which version that they wanted to trust and which one they wanted to use based on their own experience. That became the system of finding the right pieces of information that helped their story. That's really when it clicked for me that this isn't just a data problem, and it wasn't just a reporting problem, and it wasn't just a technology problem. It was a trust problem. Once trust is gone, people don't stop working. They really just build their own version of reality.” – Erin McIntosh -------- Time Stamps *(04:00): A year of rapid transformation—modernizing BI and third-party data at CNA *(13:52): Automating a decision vs. improving a decision—and why that distinction matters *(20:41): Why AI's fidelity comes down to governed context *(28:48): Building an agentically-led governance organization *(35:02): Quick hits: AI's biggest misconceptions, wasted effort, and governance myths *(36:26): Satyen’s takeaways -------- Sponsor This podcast is presented by Alation. Learn more: * Subscribe to the newsletter: https://www.alation.com/podcast/ * Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/ * Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/ -------- Links Connect with Erin McIntosh on LinkedIn Learn more about CNA Insurance Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

AI can write code faster than ever. But what if code is no longer the hard part? In the premiere episode of AI Radicals, host Satyen Sangani is joined by Mark Nelson, Venture Partner at Madrona and former CEO of Tableau, to explore what AI is actually changing—and what remains fundamentally the same about building great software and great businesses. Having led companies through the rise of databases, cloud computing, SaaS, and self-service analytics, Mark offers a rare perspective on today's AI boom. He explains why judgment and customer understanding are becoming the new competitive advantage, why enterprise buyers are shifting from AI experimentation to demanding measurable ROI, and why today's token-based pricing models may be rewarding the wrong behavior. "Code is easy to generate. Great software isn't. The bottleneck has shifted to understanding what to build." Listen to this episode to learn: Why generating code is no longer the bottleneck – but building great software still is Why enterprise AI is entering an ROI-driven phase where customers expect measurable business value Why the next generation of AI companies will win by understanding customers, not just building better models -------- “ We all come with towering strengths and our own weaknesses. Not just being a product person, not just being an engineer, not just being a salesperson, all of those skill sets. One thing I'll always say about any founder that is true is like, Do you understand your customer? Do you understand what you're solving and why? Do you really kind of first personally feel that pain? Understanding who they're building for and what problem they're solving for.” – Mark Nelson -------- Time Stamps *(02:21): Why AI is different from every technology wave before it *(07:48): AI won't replace judgment—and that's what matters most *(12:17): What venture investors are really looking for in AI founders *(20:27): AI makes code cheap—but great software is still hard to build *(30:18): Enterprise AI moves from experimentation to ROI *(35:15): Why token-based AI pricing is due for a reckoning *(45:19): The future of enterprise software and the next AI winners *(54:06): Satyen’s takeaways -------- Sponsor This podcast is presented by Alation. Learn more: * Subscribe to the newsletter: https://caspianstudios71626.activehosted.com/f/73 * Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/ * Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/ -------- Links Connect with Mark Nelson on LinkedIn Learn more about Madrona Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.