Episode Details

What's up everyone, today we have the pleasure of sitting down with Jason Dobbs, Head of Marketing and GTM Engineering at Kumo AI.

• (00:00) - Intro
• (01:24) - In This Episode
• (01:57) - Sponsor: MoEngage
• (02:54) - Sponsor: Knak
• (04:35) - How Undefined Data Definitions Make AI Confidently Wrong
• (08:18) - Why Context Engineering Replaces Prompt Engineering as the AI Bottleneck
• (12:59) - The Five Non-Negotiables for AI Readiness in Marketing Ops
• (15:42) - Why Marketing Ops Is the Context Architect in an AI-First GTM Stack
• (24:50) - Which Data Problems Block AI Deployment and Which You Can Ignore
• (28:29) - Sponsor: GrowthLoop
• (29:32) - Sponsor: AttributionApp
• (34:24) - What Goes Wrong When Agentic AI Optimizes Directly on Warehouse Correlations
• (42:02) - When to Ship AI Before Your Data Is Ready and When to Fix the Foundation First
• (48:23) - What GTM Engineering Actually Means When AI Automates the Middle
• (50:55) - How Jason Dobbs Decides What Deserves His Energy
• (53:08) - What Jason Is Reading: Intelligence History, Mind-Opening Nonfiction, and Dune

Summary: Jason Dobbs spent 7 years assembling intelligence briefings for the President, and he says most AI failures in martech are the same problem he was solving in 2003: teams acting on context they never actually agreed on. In this episode, he breaks down the 5 non-negotiables of minimum viable readiness before you deploy any AI agent, explains why the marketing ops function is becoming more critical as AI takes over execution, and argues that unbounded AI autonomy creates more risk than warehouse data ever will. He also defends GTM engineering as a real discipline rather than a rebrand, and closes with a Dune analogy that lands better than it has any right to. If you think AI readiness is primarily a data engineering problem, this episode will change how you think about your team's role in it.

About Jason Dobbs

Jason Dobbs is the Head of Marketing and GTM Engineering at Kumo AI, where he leads go-to-market for KumoRFM, the world's first relational foundation model, which generates accurate, explainable predictions directly from warehouse data. Before Kumo, he served as Global Head of Revenue Marketing at Logitech, where ABM and advanced segmentation drove 40% of B2B sales revenue and 79% YoY ARR growth. He also co-founded Trypp, an autonomous UX research agent for continuous post-ship product monitoring, and has held marketing and analytics leadership roles at Seagate, HTC Vive, Apple, and Google.

Jason spent 7 years as a United States Air Force intelligence officer, including work on the President's Daily Intelligence Briefing, an experience that shapes how he thinks about assembling trustworthy context for high-stakes decisions under uncertainty.

How Undefined Data Definitions Make AI Confidently Wrong

Every marketing ops team has heard the warning: AI is only as good as the data you feed it. You've nodded along. You've probably said it yourself. But the warning leaves out the most important detail, which is what the failure actually looks like when the model is running.

Jason Dobbs knows what it looks like. He learned it from a crash. He rides high-speed F1 electric skateboards at 50 to 60 miles an hour, and he's fallen before. He can tell you he's never fallen the same way twice. When he greenlit agentic and predictive workflows at Kumo AI before the data architecture was ready, the failure followed the same logic: unexpected, and avoidable only in hindsight.

The model returned results that looked operational. Scores came back precise. Summaries sounded coherent. Recommendations felt grounded. The failure was invisible to anyone who didn't already know what correct should look like.

The weakness surfaced when someone pushed. Ask the foll