Data teams spend enormous energy building pipelines, platforms, and governance frameworks but often skip the most fundamental step: truly understanding what people are actually asking for. In this episode, Juan and Tim sit down with data librarians Jenna Jordan and Amalia Child to explore why library science may be the missing lens for data work.

At the heart of the conversation is the reference interview, a structured technique librarians use to uncover a user's "true information need," which almost never matches the first question they ask. From establishing trust and listening without judgment, to asking open-ended questions and verifying whether the need was actually met, the reference interview offers a rigorous, repeatable framework for anyone serving data users.

If you've ever wondered why data projects deliver less value than expected, this episode will reframe the problem entirely and give you a practical toolkit to start closing the gap.

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Data teams obsess over pipelines and platforms but often skip the most fundamental step: truly understanding what people are actually asking for. We chat with data librarians Jenna Jordan and Amalia Child who share a framework for exactly that; it's called the reference interview, and it might be the most practical toolkit data teams have never used.

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AI governance at scale — what it means, how to do it, and what regulations you need to know now. Host Bobby Brill brings together five ServiceNow experts across two conversations for a complete 20-minute briefing on governing AI in the enterprise.

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WHAT WE COVER
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RAVI KRISHNAMURTHY — VP, AI Platform, ServiceNow
Why hidden AI is one of the biggest unmanaged risks in the enterprise — and why governance is an accelerator, not a brake.

PETER WEIGT — Responsible AI, ServiceNow
The innovation paradox: how AI Control Tower makes governance a team sport and breaks down the silos that slow AI deployment down.

SAMPADA CHAVAN — AI Control Tower, ServiceNow
How AI Control Tower was built, what the discovery problem really looks like, and why compliance must be baked into the AI lifecycle — not bolted on at the end.

ANDREA LAFOUNTAIN — AI Legal, ServiceNow
The three regulatory frameworks every enterprise needs to know: EU AI Act, Colorado AI Act, and NIST. Plus: the compliance strategy that scales across all of them.

NAVDEEP GILL — Responsible AI, ServiceNow
The math on enterprise AI compliance — why it's exponential — and how AI Control Tower's automated discovery keeps you ahead of it.

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CHAPTERS
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0:00 Introduction
1:23 The Hidden AI Problem — Ravi Krishnamurthy & Sampada Chavan
5:33 AI Control Tower in Practice — Peter Weigt & Sampada Chavan
7:37 The Regulatory Landscape — Andrea LaFountain & Navdeep Gill
14:38 Compliance in Action & Key Deadlines
17:05 Wrap-Up

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KEY DATES TO KNOW
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EU AI Act enforcement: August 2026
Colorado AI Act enforcement: June 2026
NIST AI RMF: Voluntary framework, increasingly referenced by regulators

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LEARN MORE
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ServiceNow AI Control Tower: https://www.servicenow.com
NIST AI Risk Management Framework: https://www.nist.gov/artificial-intelligence

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ABOUT THIS PODCAST
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Hosted by Bobby Brill. A ServiceNow podcast exploring the people, technology, and ideas shaping the future of work.

#AIGovernance #ServiceNow #AIControlTower #ResponsibleAI #EUAIAct #EnterpriseAI #AICompliance #FutureOfWork #NowAssist #Podcast

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There's a gap at the heart of most organisations and data hasn't filled it. Pete Williams, an experienced data leader, has spent years watching companies build sophisticated data capabilities, only to see insight stall before it reaches action. In this episode, we diagnose why: organisations still make decisions along traditional vertical lines, while data sits in a horizontal layer that was never given real authority. AI doesn't solve this misalignment, it exposes it! Pete makes a compelling case for why fixing this structural void is the defining challenge for data leadership today.

See omnystudio.com/listener for privacy information.

This is the takeaway episode with Pete Williams, an experienced data leader, who argues that organisations still make decisions along traditional vertical lines, while data sits in a horizontal layer that was never given real authority. Pete makes a compelling case for why fixing this structural void is the defining challenge for data leadership today.

See omnystudio.com/listener for privacy information.

Juan and Tim have a rant session with a special guest in person, Jesús Barrasa, well known in the knowledge graph space. We covered AI trends, the balance between operational vs. strategic work, knowledge graphs, context graphs. The most valuable part of our rant was our pragmatic discussion about ontologies stripping them down to what they actually are and why they matter.

Starting to work with ontologies? Think about these two dimensions: WHAT (Formal, Explicit and Shared Meaning) and WHY (Interoperability, Automation).

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Most organisations know they're not getting value from their data and AI investments, but few are willing to name the real culprit: the boardroom. In this episode, Kyle Winterbottom joins Juan and Tim to argue that the mandates handed to CDAOs are fundamentally broken because the people hiring them don't understand what good looks like. Boards continue to build capability without starting with the end in mind, creating a culture where data leaders are rewarded for building things rather than delivering outcomes. The result? An entire industry that has learned to play the wrong game.

See omnystudio.com/listener for privacy information.

This is the takeaway episode with Kyle Winterbottom where he argues that the mandates handed to CDAOs from the board are fundamentally broken because the people hiring them don't understand what good looks like. The result? An entire industry that has learned to play the wrong game.

See omnystudio.com/listener for privacy information.