The Case for Federation in the Age of Agents with Anant Jhingran, CTO of IBM Software

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.

Why mode one thinking keeps most companies stuck—and what it takes to build a company that can keep reinventing itself with AI. In this episode of AI Radicals, host Satyen Sangani talks with ServiceNow’s Brian Solis and Dave Wright and authors of Infinite, about why so many enterprises get stuck chasing ROI on isolated AI use cases instead of using AI to become something genuinely new. Brian and Dave unpack their "mode one, mode two" framework: deciding what existing work deserves to scale with AI (mode one) versus using AI to unlock entirely new value the business couldn't create before (mode two). Using stories like Ford's costly rehiring of quality engineers after over-automating, and IKEA's Billie bot freeing thousands of agents to launch a billion-euro design business, they explain how the real ROI conversation starts with strategy, not use cases. They also dig into why most companies are still stuck optimizing yesterday's workflows, why trust and psychological safety are prerequisites for innovation, and why AI governance has to evolve from a checkbox exercise into managing AI as a true enterprise asset. "AI is not the strategy. If it does become the strategy, it very much limits the impact it's going to have on the organization." Listen to this episode to learn: Why leading with use cases limits AI's impact, and how IKEA turned 8,200 agents into a billion-euro business Why most companies stay stuck optimizing yesterday's workflows instead of reinventing them Why governing AI as an asset is key as agentic AI scales -------- “ You'll see a common set of challenges, like, for example, what's the ROI of AI? That seems to be a popular conversation that has all kinds of different schools of thought around it.  AI is not the strategy. If it does become the strategy, it very much limits the impact it's going to have on the organization and how you can measure its success. Where we have the more successful ROI conversations is if we take a step back and look at, well, what are some of the things that we couldn't do without it? Does this workflow deserve to exist? Does this question help you compete more effectively for 2030? We want to bring the strategy back to the beginning of the conversation.” – Brian Solis -------- Time Stamps *(01:16): Why Brian and Dave wrote a book on AI reinvention  *(05:05): Why "What's the ROI of AI?" is the wrong question *(18:20): Mode one vs. mode two: optimizing yesterday vs. building tomorrow *(27:19): AI maturity: where enterprises really stand today *(31:33): Governing AI as an asset, not an employee *(49:50): 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 Brian Solis on LinkedIn: https://www.linkedin.com/in/briansolis/ Connect with Dave Wright on LinkedIn: https://www.linkedin.com/in/davewright2/ Infinite: How Visionary Leaders Transform Today's Businesses into AI-Forward Companies: https://www.amazon.com/Infinite-Blueprint-Leading-Age-AI/dp/1394439024 Read ServiceNow’s AI Enterprise Maturity Index 2026: https://www.servicenow.com/content/dam/servicenow-assets/public/en-us/doc-type/resource-center/white-paper/wp-enterprise-ai-maturity-index-2026.pdf Learn more about ServiceNow: https://www.servicenow.com/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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.

Analytics tools are getting a total rewrite for the AI era. What does it actually take to build a "Cursor for data"? In this episode of AI Radicals, host Satyen Sangani is joined by Francois Ajenstat, founder and CEO of Golden Analytics, to discuss how AI is reshaping data analysis workflows. A three-decade veteran of the analytics space — from Cognos to Microsoft to a decade as Chief Product Officer at Tableau — Francois explores why context and metadata still matter more than ever, and why the next generation of data tools needs to be built with a "slider of autonomy." "What we generate is we know how data is being used for different use cases and how people traversed our tool to get to that answer... every step that somebody does in Golden is essentially recorded in a time machine." Listen to this episode to learn: Why visualization was never the hard part of BI — and what actually is How Golden built a per-user pricing model to align incentives with customers Why context and metadata need to be built through the job itself, not managed as an end unto itself -------- “As you go through the journey, not every model is great at every part of the analytical flow. Do you use Sonnet for everything or Opus or Fable? When is it appropriate to use different things? There's a factor of cost, there's a factor of latency, accuracy. All those things have to be really considered as you come through it, and how do you make this work also when you've never seen the data in the first hand?” – Francois Ajenstat -------- Time Stamps *(03:12): From Cognos to Microsoft to Tableau: building the BI industry *(08:32): Is BI dead? Why visualization was never the hard part *(12:21): Building Golden: two-click dashboards and a constellation of LLMs *(19:19): Why data isn't software: the unique challenges of AI + data *(31:41): The blurring boundaries between metadata, context, and BI *(48:05): 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 Francois Ajenstat on LinkedIn: https://www.linkedin.com/in/francoisajenstat/ Learn more about Golden Analytics: https://goldenanalytics.com/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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.

AI Radicals is back for Season 4 — and trust in AI has never been more contested. This season, host Satyen Sangani, CEO and co-founder of Alation, sits down with leaders, builders, and operators working at the edge of AI transformation to ask the question everyone's dancing around: can we actually trust the systems we're building? New conversations dig into data quality, governance, and the feedback loops that separate AI that works from AI that just demos well. If you care about making AI matter inside your company, your team, or your own career — Season 4 starts July 29, 2026. -------- 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/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The future of business intelligence is being rewritten. Have you ever wondered how AI will unlock the power of unstructured data?In this episode of Data Radicals, host Satyen Sangani is joined by Chris Aberger, newly-minted VP at Alation to discuss building AI-powered data workflows.A startup pioneer, Chris explores the importance of metadata in enhancing AI applications within organizations, the significance of quick iterations, and the evolving role of AI engineers.“ That two-step realization is what's causing a lot of this activity that we're seeing in the market, which is, I know I need to plug into databases. I'm now coming to terms with the fact that this is actually a really tough problem to get right.”Listen to this episode to learn:Why metadata curation and feedback loops are crucial for making AI effectiveThe necessity of a fast-paced, iterative approach in developing AI solutionsHow to enable end-users to become builders through AI and metadata toolsListen now: alation.com/podcast/episodes/ai-builders-metadata-chris-aberger*Satyen’s narration was created using AI--------“People have realized that, okay, like structured data is actually like the hard problem to get right. And all these organizations' really valuable data is inside their databases in the structured formats. We have to figure out how to make this ready for the AI era. And then the kind of second level problem that people are discovering is how do I make this structured data actually work? Oh, it's metadata. And I think that realization that that kind of two-step realization is what's causing a lot of this activity that we're seeing in the market, which is, I know I need to plug into databases. I'm now coming to terms with the fact that this is actually a really tough problem to get right. In order to get it right, I need to effectively go build a data catalog or metadata provider, and therefore we're seeing a lot of activity in this space.” – Chris Aberger--------Time Stamps*(02:04): From the Stanford AI Lab to founding Numbers Station*(12:10): From chat with your data to act with your data: From data users to business builders*(19:23): The value of metadata to production-ready AI*(28:46): What are precision agentic workflows?*(35:35): Empowering enterprise data users to build with AI*(45:50): Satyen’s takeaways--------SponsorThis 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/--------LinksConnect with Chris on LinkedIn Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In the luxury world where artistry is key, how is AI enabling personalization, optimization, and speed?In this episode of Data Radicals, host Satyen Sangani is joined by Julie De Moyer, Chief Data Officer of LVMH Beauty to break down the role of data and AI in business transformation.A seasoned strategist and leader of innovation across 15 beauty brands, Julie shares practical examples of AI application in various aspects of LVMH's operations, from product development to supply chain management.“ The AI is often the cherry on the cake. We're moving towards those new technologies that are helping us dream even bigger.”Listen to this episode to learn:The importance of collaboration, change management, and consumer-centric approaches.How to work closely with CEOs to drive meaningful data-driven decisions.How to balance AI and human creativity within the luxury beauty industry.Listen now: https://www.alation.com/podcast/episodes/lvmh-data-ai-strategy-julie-de-moyer*Satyen’s narration was created using AI**LVMH is vendor-neutral and this does not constitute an endorsement**All views and opinions expressed by the speakers are their own--------“ If you look at the making of perfumes or the way we actually make the wines, in other industries, we would use the AI in order to help those, I would say, those scientists to go faster, to optimize their trials. It will never replace the final scent or the final product that is decided on, but it can help with the substitutions of products that might need to go out, as a result of regulatory changes. It might also help with making sure that the quality of the products last as long as possible. We really help those researcher scientists do their job better and easier.” – Julie De Moyer--------Time Stamps*(01:34): Julie’s background: From economics student to technology leader*(07:55): AI in action: How stakeholders collaborate*(14:42): The role of data in luxury today (and 5 ways to apply AI in retail)*(22:09): Leading data in a multi-brand environment*(28:18): How to become a trusted AI leader: Key tips*(33:40): Satyen’s takeaways--------SponsorThis 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/--------LinksConnect with Julie on LinkedIn Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this special compilation episode, we're bringing together the most insightful conversations from our latest season exploring the rise of AI. You'll hear from thought leaders like Fortune's AI editor Jeremy Kahn on rebuilding the middle class, marketing executive Michael Olaye on creative acceleration, and Tom Davenport on what real AI transformation looks like. Plus insights from numerous other AI experts including CDOs, academics, and tech entrepreneurs who are shaping our AI future today.We'll explore practical AI use cases, examine how data leaders can leverage these technologies, and discuss how the CDO role is evolving in response to AI's momentum.Listen to this episode to learn:How AI copilots are fundamentally changing the way we work and uplifting workers across industries.Why it's important to have data leaders who can translate between technical possibilities and business realities.How solid data infrastructure, clear business objectives, and thoughtful human oversight leads to successful AI transformation. Whether you're a data professional, business leader, or simply curious about AI's impact, this episode offers perspectives from those on the frontlines of the AI revolution.--------“I think this is gonna be a tremendously transformative technology, and I think there's some really big positive effects, particularly I think we are gonna see a huge uplift in labor productivity, and I don't think we're gonna see sort of mass joblessness from this technology. I think actually this is a technology that could enable people to sort of be lifted back up into the middle class.” – Jeremy Kahn“You can do stuff that would take weeks before in days. You can collaborate with people who have no technical knowledge on technical things. We have tools now that you can code by like verbally speaking natural language. We have tools that you can do design without having any design skills. So, I think it's opened up a whole new site for agencies, consultancies, companies, but it's also opened a whole new site for a new like economy of like content creators. When you build anything with AI, having a human in that loop where we are today, having humans in that loop, checking that also, it's good.” – Michael Olaye“It's not rocket science. It's having senior people who are interested in analytical, decision making, hiring people who can do the work. The day-to-day work of analytics, both the data management and the data analysis. And ultimately having some sort of unique data that is proprietary to you, that will really differentiate you. Because ultimately data is a fuel of analytics and AI. And if you don't have something distinctive, you're gonna have the same models that everybody else has.” – Tom Davenport--------Timestamps*(01:18): AI as copilot*(04:30): AI: It’s still early days*(06:13): AI Use Cases*(11:26): How data leaders can leverage AI*(14:07): Data leadership and systems thinking*(18:17): The future (and limits) of the CDO*(29:15): Predictions*(35:16): Career advice--------SponsorThis 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/* David’s LinkedIn Profile: https://www.linkedin.com/in/davidwchao/  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.