AI Radicals

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

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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:

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

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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:

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

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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:

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

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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.