ServiceNow Podcasts

ServiceNow Community Podcasts
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Mar 5, 2026 • 15min

Pre Gartner Data & Analytics Rants with Juan and Tim

Juan and Tim rant about whats on their mind going into Gartner Data and Analytics conference: "Context" is going to be the word of the week, The Execution Gap, Decision intelligence might be the bridge and the thing nobody's talking about yet: context lock-in. See omnystudio.com/listener for privacy information.
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Mar 4, 2026 • 29min

The Human in the Loop | Ethical AI with Di Le

The Human in the Loop | Ethical AI with Di Le ServicveNow Insights Podcast - hosted By Bobby Brill What does it actually mean to build AI responsibly? Not the buzzword version. The real version. In our latest episode, I sat down with Di Le — AI Ethicist and Human-Centered AI Strategist at ServiceNow — and she broke it down in a way I hadn't heard before. Most people use Ethical AI, Responsible AI, and Human-Centered AI interchangeably, and Di breaks down exactly where each one lives and how they apply to building AI that aligns with our societal values. Fairness. Transparency. Bias. Beyond evaluation and technical talking points, these are also design decisions with real consequences for real people — and operationalizing them is harder than most organizations want to admit. One line from Di that stopped me: "People have crossed oceans and built monuments in honor of our capability to think. And I just want people to preserve that and not surrender it so freely." That's the episode in one sentence. To learn more about Ethical AI and reseatch from Di Le and more - https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1020&context=sighci2025 https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1025&context=sighci2024  https://www.youtube.com/watch?v=QhVY-85A-Wk&t=5s ServiceNow Insights Podcast 🎧 The Human in the Loop — now available wherever you listen to podcasts. 0:00 – Introduction & Di Le's Background 1:35 – Is Ethical AI Just About Preventing Skynet? 2:22 – Responsible AI vs. Ethical AI vs. Human-Centered AI 6:34 – Why All Three Are Deeply Interconnected 8:39 – When "Amazing" Tech Goes Wrong: The Adobe Example 10:07 – Fairness, Transparency & Accountability Explained 12:06 – The Mug Factory: Understanding Bias in AI 14:52 – Transparency & Interpretability in AI Design 16:48 – Keeping Humans Genuinely in the Loop 18:06 – Accountability & Human Oversight 20:39 – Why Bias Isn't a Bug You Can Just Fix 22:30 – How ServiceNow Approaches Responsible AI 23:11 – Why Di Chose ServiceNow (The Origin Story) 27:09 – One Thing Everyone Should Do With AI 28:38 – ClosingSee omnystudio.com/listener for privacy information.
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Feb 26, 2026 • 56min

Your data should speak your language with Säde Haveri

Säde Haveri, entrepreneur and metadata specialist who organizes Helsinki Data Week, argues metadata is the bridge between systems and people. She discusses why data must match organizational language. She covers defining shared meanings, prioritizing critical datasets, embedding definitions early, and using ontologies and knowledge graphs to make semantics machine readable.
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Feb 26, 2026 • 6min

TAKEAWAY - Your data should speak your language with Säde Haveri

This is the takeaway episode with Säde Haveri where she makes the case that metadata is the bridge between systems and people. Most data and AI initiatives don't fail on technology — they fail due to the lack of meaning. If your data doesn't speak your language, it simply won't work for you.See omnystudio.com/listener for privacy information.
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Feb 19, 2026 • 44min

UTG Unlocked: AI Careers, Partnering with AI, and Understanding Global Cloud Services at ServiceNow

AI is transforming how businesses operate and how early-career talent grows. In this episode, UTG Unlocked, Mark Stockford (GVP, Global Cloud Operations) and Alyssa Gerhart (former intern, now full-time employee) share how AI is reshaping work at ServiceNow—from strategic impact to day-to-day execution. Our guest hosts Jorden Shelton and Cynthia Mathenge guide the conversation and explore real AI use cases like Unity, RAG-based duplicate detection, and intent detection, while emphasizing the importance of critical thinking and strong fundamentals.  In this episode, designed not just for recent interns, you’ll learn how AI is expanding career paths, how teams like Global Cloud Services power innovation behind the scenes, and what interns and early-career professionals can do now to grow: stay curious, use AI intentionally, seek mentors, and don’t just consume—contribute.  UTG is the engine behind the scenes here at ServiceNow — enabling innovation, maintaining production environments, supporting internal teams, and driving operational excellence. It connects strategy to execution by combining engineering, cloud operations, and technology operations to deliver stable, high-performing systems that allow the business and customers to succeed.  For more information about the Early Careers program visit - https://careers.servicenow.com/early-careers/  00:00 Welcome & What ‘UTG Unlocked’ Is All About  02:50 Meet the Panel: Mark, Alyssa, Jorden & Cynthia  04:29 Segment 1: How AI Is Impacting the Business (Customers vs. Employees)  06:26 Skills That Matter in an AI-Powered Workplace  09:52 Real AI Use Cases: Unity, Agents, and Faster Ops  13:57 AI and Career Growth: New Roles, New Paths, Partnering with AI  18:56 Advice for Early-Career Talent: Stay Curious, Build, Contribute  20:41 Segment 2 Kickoff: Rapid-Fire Fun28:50 Pulling Back the Curtain: What is GCS 30:37 GCS as a Superhero: Operating in the Shadows Like Batman  31:35 The Hidden Work: Solving Customer-Created Problems & Root-Cause Hunting  33:37 Alyssa’s Journey: Intern to FTE, Mentorship, and Scaling Developer Productivity  35:38 What’s Next: Emerging Tech on the Radar (AI to Quantum Computing)  37:15 Closing Takeaways: Keep Learning, Use AI Wisely, Ask Questions, and Give Back  40:12 Final Words & Where to Learn More See omnystudio.com/listener for privacy information.
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Feb 19, 2026 • 1h 2min

Shift Left Everything: How Ontology, Events, and Culture Unlocked Enterprise AI with Nachiket Mehta

Nachiket Mehta, VP of AI and data engineering who builds ontologies and event-driven data foundations. He recounts mapping real-world operations to system events. He tells the $35M lost-trailer tale and why tiger teams and warehouse visits mattered. He champions shifting data teams left, embedding engineers in domains, and tying work to OKRs to turn messy data into actionable systems.
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Feb 19, 2026 • 6min

TAKEAWAY - Shift Left Everything: How Ontology, Events, and Culture Unlocked Enterprise AI with Nachiket Mehta

This is the takeaway episode with Nachiket Mehta, an experienced data leader who has lived and breathed the “shift left”. In this episode we will unpack how ontology, events and culture unlock enterprise AI. We discuss why your ontologist needs to visit the fulfillment center, how to shift data teams from afterthought to proactive partner, and why the five why's matter more than your tech stack.See omnystudio.com/listener for privacy information.
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Feb 12, 2026 • 54min

The way we build agents today is dumb with Vaibhav Gupta

Vaibhav Gupta, co-creator of BAML and founder at Boundary ML focused on safe, typed agentic AI tooling. He argues most agent code is fragile and unfamiliar with probabilistic failure. He traces how abstractions evolved in web dev and gaming. He introduces BAML, demoing schema-driven extraction, prompt preview, and static checks to make LLM systems more reliable.
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Feb 12, 2026 • 4min

TAKEAWAY - The way we build agents today is dumb with Vaibhav Gupta

This is the takeaway episode with Vaibhav Gupta, co-creator of BAML, where we unpack why most agentic AI code out there is dumb. If you like what you hear, you should listen to the full episode. See omnystudio.com/listener for privacy information.
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Feb 6, 2026 • 28min

It's Friday, Juan and Tim rant about Decisions, Context, MCP and Maturity Models

Two colleagues rant over decision intelligence and how context graphs can codify choices. They debate MCP versus skills and whether interoperability or ease wins. They talk about capturing decision traces, measuring exceptions over time, and treating context as a business asset. They argue organizations actually face a work problem and outline maturity stages for scaling context and AI.

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