AI Radicals

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:

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

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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:

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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