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 Fun
28:50 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

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Juan and Tim are back for a LIVE episode with Nachiket Mehta, an experienced data leader who has lived and breathed the “shift left”. In this episode we unpack how ontology, events and culture unlock enterprise AI.

We discuss the gap between what your systems think is happening and what's actually happening on the ground and dive into real world examples: a trailer with expensive merchandise sat forgotten in a yard for weeks. $35M in delayed orders. The math added up, but nobody saw it coming. Why? Because we're obsessed with cleaning data and building dashboards, but nobody mapped the happy path vs. the exceptions actually happening on the ground. What is delivery when the customer isn't home? What's "lost in transit" versus "sitting in our own yard"?

The solution? Send your ontologist to the fulfillment center. Build tiger teams. Shift your data teams left to act like software product teams. And most importantly: connect the five why's back to your OKRs, or you're just building features nobody needs.

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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.

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Tim and Juan unpack why Vaibhav Gupta, co-creator of BAML, states that most agentic AI code out there is dumb. It's ugly, fragile, and built by people who've never had to wrestle with probabilistic systems before. But here's the thing: we've seen this movie before. Remember when building websites was painful? Then jQuery showed up... then React... then Tailwind. Abstraction always wins. It just takes time (and about 50 bad frameworks for every good one). The gaming industry also figured out about unreliable systems decades ago. The takeaway is that the real bottleneck isn't abstracting the AI, instead it's abstracting the failure. Oh, and this episode has a live demo (first time we ever do this!).

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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.

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Juan and Tim grab a beer and rant about decision intelligence and context graphs, MCP vs Skills, and how companies really have a work problem (not a data/AI problem) and what is the maturity model to get that work done

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Tired of executives breathlessly asking "what's our AI strategy?" while your data team drowns in 1,000 dashboards that nobody uses? In this episode, Juan and Tim cut through the noise with Juan Gorricho a data and analytics executive with more than 25 years of experience delivering business value at TD Bank, Visa, The Walt Disney Company

We cover: Why do data leaders keep building vanity metrics instead of business value? How do you go from being an "order-taking reporting team" to a trusted decision intelligence partner? And why does every data governance initiative feel like building policies nobody wants to follow?

The secret? Do it by slice. Not top-down. Not bottom-up. By slice—connecting each data product to real business outcomes while building reusable foundations underneath.

If you're ready to turn your data team's NPS around and build trust that actually sticks, this one's for you.

Spoiler: It starts with being brave enough to tell executives the truth. And btw, "I bet 40 columns drive 90% of your business."

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This is the takeaway episode of our chat with Juan Gorricho, a data and analytics executive with more than 25 years of experience delivering business value at TD Bank, Visa, The Walt Disney Company. We cover: Why do data leaders keep building vanity metrics instead of business value? How do you go from being an "order-taking reporting team" to a trusted decision intelligence partner? And why does every data governance initiative feel like building policies nobody wants to follow? And so much more. If you liked these takeaways, listen to the full episode!

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