Join Domo’s CMO, Mark Boothe, and Jeff Clobes, Solution Engineer, Domo, as they pull back the curtain on a bespoke pro-code application that solves a tough challenge in every Domo rollout—getting people to log in the first time and keep coming back—by borrowing the mechanic that already runs every office fantasy league. Jeff will walk through how the app clones the personalities of four opinionated pundits, pits users against an algorithmic rival manager, generates hot takes and a full weekly audio recap grounded in real MLB game data, and writes every submitted pick and scored day back to a governed Domo AppDB collection the moment a user locks it in. All of this is engineered end to end on Domo.

What You Will See:

• It's Agentic: The app doesn't just visualize data; it analyzes daily performance, generates in-character takes from four distinct pundits, produces a Hot Take of the Day chyron, drafts a rival manager's deadpan reaction, and delivers a fully-scripted 2–3 minute audio broadcast with mood-detected intro music tuned to the week's outcome.

• It's Connected: One application, every layer. A custom pro-code front end, live queries against a governed Domo dataset, Domo AI calling foundation models for every take and recap, AppDB for pick persistence and scored daily reports, and browser-native voice synthesis giving each character their own sound—natively integrated, reading where it should and writing where it matters.

• It's Habit-Forming: Fantasy sports mechanics engineered into the fabric of Domo. Standings, roasts, weekly recaps, streaks. There’s no corporate onboarding, compliance training, or forced adoption. Just something people already want to check.

AI can spin up a slick prototype in seconds, but a demo that looks ready and a system that actually ships and gets adopted are two very different things. In this episode of Domo’s podcast, Governed Data for Agentic AI, Chris Willis, Chief Design Officer at Domo, speaks with Dan Hendriksen, who leads Domo’s Advanced Customer Enablement (ACE) program and spends his days helping customers turn AI experiments into things they can actually run. He’s watched the same patterns play out across build after build, and he’s here to share what separates the projects that ship from the ones that stall.

This conversation is all about “plausibility theater”: the trap of building something that looks like it should work but quietly skips everything real systems need (user volume, concurrency, security, edge cases). Dan explains why the planning conversation matters more than ever now that AI has taken the developer out of the loop, why “do not code” and plan mode are becoming essential habits, and why the time you save on a prototype is best spent up front with stakeholders instead of iterating 45 times on the wrong thing.

Chris and Dan also get into the bigger shifts AI is setting off: the rise of curious generalists over stay-in-your-lane specialists, the long tail of low-value problems that’s finally worth solving, and the move toward bespoke apps that do exactly what a team needs. Along the way Dan makes the case that some of the best AI is maybe not AI at all, and that it all starts with a deceptively simple question: Why? Listen for a practical, field-tested take on getting past the demo and building AI that ships and works.

After listening, you'll learn:

• What “plausibility theater” is and how to avoid it

• Why the planning conversation beats jumping straight to code

• How roles are shifting toward curious generalists

• When the right answer is a simple automation, not an app

Join Domo CMO Mark Boothe and Ron Karas, Principal Solution Engineer at Domo, as they pull back the curtain on a repeatable, AI-driven approach to operational data migration, built for organizations where the next acquisition is already on the calendar. Ron will walk through why these projects are so costly, then prove the alternative end to end on Domo: taking a real Microsoft Dynamics 365 Business Central customer extract, using genuine Microsoft-documented field names, and having the Domo plugin automatically generate, run, and verify a working ETL that outputs SAP-ready data. The use case is ERP, but the pattern travels, CRM, financial systems, and proprietary systems all migrate the same way. And you can get this live in hours with no new tooling on the Domo platform.

In this episode, CMO Mark Boothe sits down with Zach Ehasz, Director of Product Management for Hospital Quality at Healthgrades, to unpack how his team turned a decades-old sales tool into an AI-powered discovery engine. AMT had become a 100MB+ macro-enabled Excel workbook that broke constantly and spat out five static reports. Rebuilt on Domo with AI agents and the Context Engine, it now surfaces unique, comparative award messaging across city, state, and national levels in minutes instead of days. A masterclass in putting AI to work on your own data, safely.

Miles Herleikson presents an AI Command Center for franchise developers managing 50 to over 500 locations. This system monitors deal velocity, analyzes market expansion opportunities, detects store opening risks, forecasts revenue impact, and benchmarks franchisee performance—all in a single view.

Ben McClave of Yum! Brands shares his AI-first approach to business intelligence, from an app that builds apps to vibe-coded dashboards wired to a Snowflake Semantic Layer. This is a candid look at moving fast without breaking trust.

Scott Pulley shares a retrieval-augmented generation app built with Domo Documents as the backbone. This Intelligent Response Engine drafts RFPs using your institutional knowledge, security documentation, knowledge base, and past RFPs.

Every recruiting team has a backlog. Open reqs aging past SLA, hot jobs buried in spreadsheets, and recruiters spending their best hours building the briefs that should already be in front of them. What if your data could do that work instead?

At Domo, we built an AI Recruiting Agent that doesn't just surface the problem. Instead, it tells you what to do about it. The agent reads your Workday data, scores every open requisition for time-to-fill risk, segments your pipeline into Critical, At-Risk, and Healthy, and generates a recommended next action for each one—with the rationale built in. When a recruiter agrees, one click routes the decision through a governed workflow, logs it to AppDB, and sends a confirmation. Your best strategic thinking, automated. Your team's judgment, preserved.

Join Domo CMO Mark Boothe and Kenny Scott, Solution Engineer at Domo, as they walk through a live build of the Domo AI Recruiting Agent end to end, on the Domo platform. You'll see how App Studio, Workflows, Code Engine, and Domo AI work together as a single connected system; the agent analyzes your pipeline in real time, generates recommendations your recruiters would have spent hours writing, and routes every decision through a human before anything moves. There's no autonomous action and no black box. Just your data, your judgment, and AI doing the heavy lift.