AI Radicals

Why "America's data steward" is more than a job title, and what it takes to keep fraud intelligence trustworthy in the age of AI agents.

In this episode of AI Radicals, guest host Susan Wilson, Sales Leader at Alation, talks with Cara Tice, Chief Data Officer at Early Warning (the company behind Zelle), about what data stewardship really means when trillions of dollars in real-time payments, and the fraud intelligence protecting them, run on your data.

Cara and Susan dig into why data products have replaced the "one data platform to rule them all" mentality, why integration and change management matter more than any single tool, and why AI doesn't create data problems, it just exposes the ones that were already there. 

"Your AI governance is reliant on your data management program. Make sure you've got data governance up and running, because AI is going to rely on your data management controls and the context you create."

Listen to this episode to learn:

• Why "data products" — not centralized platforms — are the fastest path to trustworthy, reusable data
• Why AI governance is really just an extension of data governance, and why the two leaders need to be joined at the hip
• What a new CDO should fix (and what to leave alone) in their first 90 days on the job

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“As a CDO or as a data steward, you need to be obsessed with the customer outcome. Don't lose sight of that. It's easy to lose sight of it when you are focused on plumbing and fixing this issue and that issue. You always have to remind yourself of what's the bigger picture, what's the north star?  With data products, you become relentless about the customer outcome because you're delivering to a specific business need, you're creating more capability that is business centric around data. When I think about the features that you would need for data science models, it's the same. Whether your consumer is a data scientist or is a commercial consumer, that data is used to make a decision. So you need to make sure that it's accurate.” – Cara Tice

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Time Stamps

[03:04] Stewardship vs. governance — and what "America's data steward" actually means

[06:49] What governed data means when the cost of being wrong is fraud

[09:41] Speed and precision in fraud modeling, and why data is fragmented everywhere

[10:55] Data products: tables, joins, and eating the vegetables without knowing it

[13:54] Progress vs. perfection — and where perfection is non-negotiable

[16:30] The monolith is gone. Now it's Lego blocks (but not too many)

[17:56] The thing that gets you every time: change management

[20:50] Vertical slices, quick value, and the CFO's "where's my return?"

[22:35] What Cara rebuilds in every CDO role — and what she leaves alone

[29:17] The grocery store analogy: connecting the catalog to business outcomes

[31:29] Agentic AI, data risk, and the RPA lesson nobody wants to revisit

[36:13] AI governance as an extension of data governance

[38:34] Rapid fire: the first-six-months mistake and the year-one investment

[41:39] Data leaders and AI leaders: joined at the hip

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Sponsor

This podcast is presented by Alation.

Learn more:

* Subscribe to the newsletter: https://caspianstudios71626.activehosted.com/f/73

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* Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/

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Links

Connect with Cara Tice on LinkedIn: https://www.linkedin.com/in/cara-dailey/

Learn more about Early Warning: https://www.earlywarning.com/

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Why AI agents fail at scale, and the systems-thinking fix nobody's building yet.

In this episode of AI Radicals, host Satyen Sangani talks with Eugene Wu, Columbia professor and co-founder of the Data Agents and Processes (DAPLab), about why building reliable AI agents is fundamentally a systems problem, not a model problem.

Eugene explains "semantic coupling," the idea that every layer of an agent's stack, from data retrieval to tool calls to reasoning, is interdependent, so a small failure anywhere can quietly corrupt the final output. He and Satyen draw a parallel to early relational databases absorbing the complexity that applications used to handle themselves, arguing that today's computing infrastructure needs to do the same for agents: managing data flows, enforcing rules deterministically instead of hoping a prompt is followed, and giving agents safe room to explore and fail without real-world consequences. Eugene shares research from his lab on search at massive scale, why even top models struggle to find the right evidence in huge datasets, and a "branchable" computing environment that lets agents try, fail, and roll back cheaply.

"You need somewhere to ground reliability and quality. If it's not probabilistic, and you can rely on it and it's guaranteed, then the system doesn't need to think about it at all."

Listen to this episode to learn:

• Why "semantic coupling" makes traditional layered software thinking break down for AI agents
• Why pushing rules into the system (not the prompt) turns probabilistic behavior into guaranteed safety
• Why compute-level innovations like branchable environments could redefine what we expect agents to do on our behalf

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“ So many people in the data community and companies are working on data search, but how do you evaluate the end-to-end quality? And how much search is even the critical bottleneck in this kind of end-to-end question? Because what does the agent need to do? It needs to take your question, it needs to figure out how to decompose it into a series of sub-questions. Such as, “Schools near Clinton Hill.” Then it needs to figure out, “Okay, I need to find information about Clinton Hill, and I need to find locations of schools.” And it needs to then search over 40 million documents and datasets that we've collected, and find the right ones. At any given step, if it didn't find the right data, then the whole thing falls apart and you can't answer the question.” – Eugene Wu

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Time Stamps

*(01:10): What the Data Agents and Processes Lab is and why it spans multiple research areas

*(04:29): Semantic coupling explained

*(19:31): How agents find the right data in a massive data lake

*(29:18): What trust means when AI can persuade as well as answer

*(42:59): The biggest unlocks for agents over the next 12 months 

*(56:12): What we'll be talking about in 12 to 18 months

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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/

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Links

Connect with Eugene Wu on LinkedIn: https://www.linkedin.com/in/eugene-wu-b23417290/

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Why AI transformations fail and the 90-day plan that actually works.

In this episode of AI Radicals, host Satyen Sangani sits down with Charlene Li, bestselling author and strategist, to unpack her new book on how organizations can create real value with AI, not just deploy it.

Charlene explains why the biggest mistake leaders make is treating AI as a separate strategy instead of a tool that serves their existing business strategy, and lays out the 90-day framework she and co-author Katia Welch built to help executives move from confusion to a clear AI roadmap. They dig into why "Goldilocks governance" beats both reckless and overly restrictive AI policies, why ownership of AI shouldn't default to IT, and why the obsession with pilots is really a symptom of leaders abdicating strategic responsibility. Charlene also shares real examples from a call center that grew headcount instead of cutting it, to a bank that reskilled instead of laying off, showing what an abundance mindset toward AI looks like in practice.

"Good governance doesn't slow you down. It actually speeds you up, because you know what you are able to do and what you shouldn't be doing."

Listen to this episode to learn:

• Why AI should never be treated as its own strategy and how to anchor every AI decision in existing business goals
• Why "moving from pilot to production" is really a leadership gap, not a technology gap
• Why an abundance mindset is what separates organizations that win with AI from those that stall out

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“ They're green-lighting these pilots because they feel they need to be doing something, but they won't green-light into production because they don't have an overarching AI roadmap that supports their strategy. They haven't put the time and attention to it. They've given it to IT. They have not really thought about what are we trying to do with this? The business leaders have abdicated their responsibility for AI because they themselves don't understand it, and they're not prepared, they don't know, they're not equipped to be able to have a conversation, a strategic conversation with it, because they don't even understand that it is a strategic issue. It's a leadership gap that we have right now.” – Charlene Li

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Time Stamps

*(01:20): Charlene’s motivations for writing Winning with AI

*(07:40): The 90-day blueprint explained

*(12:11): Goldilocks governance & the AI Trust Pyramid

*(18:22): Three ways to create value: engagement, efficiency, reinvention 

*(28:04): Why pilots fail to reach production

*(40:15): Satyen’s takeaways

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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/

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Links

Connect with Charlene Li on LinkedIn: https://www.linkedin.com/in/charleneli/

Winning with AI: The 90-Day Blueprint for Success: https://www.amazon.com/Winning-AI-90-Day-Blueprint-Success-ebook/dp/B0GQM9PD3P

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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 separately
• Why agentic, discovery-driven workloads may reduce the need to centralize all your data, but raise the stakes on metadata
• Why forking products into many tailored variants, instead of one shared code path, could reshape how software gets built

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“ 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

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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

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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/

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Links

Connect with Anant Jhingran on LinkedIn: https://www.linkedin.com/in/anantjhingran/

Learn more about IBM: https://www.ibm.com/us-en

Anant’s Podcast Context Window: https://www.youtube.com/playlist?list=PLm-EPIkBI3YqXTgboKALGzNmGELWp_oTT

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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:

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“ 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

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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

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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/

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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/

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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:

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“ 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

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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

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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/

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Links

Connect with Nathalie Berdat on LinkedIn: https://www.linkedin.com/in/nathalie-berdat-b716b56/

Learn more about BBC: https://www.bbc.com/

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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:

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“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

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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

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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/

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Links

Connect with Francois Ajenstat on LinkedIn: https://www.linkedin.com/in/francoisajenstat/

Learn more about Golden Analytics: https://goldenanalytics.com/

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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:

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“ 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

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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

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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/

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Links

Connect with Erin McIntosh on LinkedIn

Learn more about CNA Insurance

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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:

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“ 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

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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

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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/

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Links

Connect with Mark Nelson on LinkedIn

Learn more about Madrona

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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.

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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.