The Hidden Cost of How Your Business Actually Works | Process Mining with Dan Grady

Your dashboards show you what's happening. Process mining shows you why — and what to do about it. In this episode, Bobby Brill sits down with Dan Grady to demystify process mining: what it is, how it works within the ServiceNow platform, and why organizations using it are uncovering years of hidden inefficiency they never knew existed. Dan walks through real customer stories — from a double approval process for printer toner that was costing 2 days per request to a single form-field change that saved 7 years of manual work per month — and explains how process mining moves you from raw data to actionable insight faster than any traditional approach. Dan Grady has a technology career that spans over 25 years. The last 10 spent at ServiceNow. In his current role as Director of Product Management, he is focused on helping customers identify process improvement opportunities through the use of ServiceNow's Process and Task Mining solution. Outside of the office, he enjoys spending time with his wife and 3 girls, public speaking, bad NY sports teams, a good happy hour deal, and storytelling. Whether you're new to ServiceNow or a seasoned admin, this episode will change how you think about your workflows. 🔗 Get started with Process Mining: https://www.servicenow.com/community/process-mining-article/servicenow-process-mining-formerly-process-optimization-faq/ta-p/2309083 📄 Learn more about ServiceNow: https://www.servicenow.com/docs/ 🎓 ServiceNow Docs YouTube Channel: https://www.youtube.com/channel/UCQjE37R-Y4DTq7kUWPO83Wg Chapters0:00 – Introduction & Dan Grady's Background 1:13 – What Is Process Mining? (The X-Ray for Your Business) 2:08 – Why Dashboards Alone Aren't Enough 3:56 – From Raw Data to Actionable Insights 4:59 – The Printer Toner Story: Removing a Step, Saving Two Days 6:01 – The Spaghetti Bowl: Visualizing Your Processes 7:34 – The "47 Years" Moment That Shocks Every Customer 8:43 – Finding Automation Opportunities with Process Mining 10:49 – Best Practices: How to Avoid Getting Overwhelmed 12:35 – Getting Started: First Steps on the Platform 15:11 – Real Customer Use Cases (Approvals, Routing & More) 17:59 – Why You Never Stop Needing Process Mining 20:50 – The Analytics Stack: Operational Reporting → Platform Analytics → Process Mining 22:45 – Closing & Where to Learn MoreSee omnystudio.com/listener for privacy information.

Want to get the honest no bs scoop of what happened at the Gartner Data & Analytics in Orlando, then this short and sweet episode is for you. This was an impromptu recording where Sanjeev Mohan and Ryan Dolley join Juan and Tim to share what they learned. See omnystudio.com/listener for privacy information.

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.

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.

Most data and AI initiatives don't fail on technology — they fail on meaning. In this episode, Juan and Tim chat with Säde Haveri, who makes the case that metadata is the bridge between systems and people: the language and structures that help humans understand, trust, and use data in their daily work. Her focus isn't on building smarter pipelines, but on turning complexity into shared understanding so that data actually drives decisions and behavior change. Because if your data doesn't speak your language, it simply won't work for you.See omnystudio.com/listener for privacy information.

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.

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.

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.See omnystudio.com/listener for privacy information.

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.

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!). See omnystudio.com/listener for privacy information.