Julie came into data sideways from content marketing and turned that into a 15-year career running lean data teams at some of the coolest martech startups in the landscape. In this episode she breaks down how they cloned their best analyst into an AI agent named JimBot, why she pushes back on AI projects without ever saying no, and how she gets companies to fund the invisible foundation work nobody claps for.More
Tag Archives: AI and Automation
228: The Dispatch tower (The Dungeon of martech architecture, part 4)
Every vendor in your stack just turned on an agent. Your ESP has one. Your CDP has one. Your MAP has one. None of them know what the others are doing, none of them were told to check, and nobody volunteered to referee it. Welcome to the final floor.More
227: The Correlation masquerade (The Dungeon of martech architecture, part 3)
Your warehouse is clean, your agents are running, everything looks like it’s working, and that’s the trap. This floor is where AI mistakes correlation for causation and scales the mistake at machine speed, eroding revenue behind a green dashboard. We unpack the fix: holdouts, guardrails, and a causal context graph that proves what actually works.More
226: The Eye of context (The Dungeon of martech architecture, part 2)
Agents operating on data without anything to help them causes “believable nonsense.” Data quality stops agents from misrepresenting what the warehouse contains but you need context engineering to put the right meaning, rules, and situational information in front of the model at the right moment.More
225: The Fall of CRM gravity (The Dungeon of martech architecture, part 1)
At some point in the last decade, the CRM became a shared apartment with 19 roommates, each adding their own version of the source of truth. This episode argues for a cleaner path: raw data into the warehouse, transformation with tools like dbt, then activation through reverse ETL so definitions stay centralized and audiences stay portable. More
224: Keith Jones: How OpenAI’s GTM leader structures teams and spots standout candidates
Keith walks through the full restructuring journey of the GTM org at OpenAI and how GTM Systems ended up under finance. He shares his interview process, the two archtypes that make up his team as well as his filter for separating human candidates from AI-generated applications.More
214: Austin Hay: Claude Code is creating a new class of elite marketers and the mental models that make it click
You’ll be hard pressed to find someone that understands martech and is more advanced in their Claude Code journey than Austin Hay. He maps the 2 chasms separating most marketers from big AI leverage, makes the case for a new class of professional he calls the white collar super saiyan, and walks through the automations he’s actually built.More
212: Tobias Konitzer: The Causal AI revolution and the boomerang effect in marketing decision science
Tobi challenged marketing’s fixation on prediction. He has built highly accurate LTV models, but accuracy alone does not move revenue. Marketing is intervention. Correlation shows patterns; causality tells you what happens when you pull a lever. That shift reshapes experimentation, explains why dynamic allocation can outperform static A B tests, and highlights how self learning systems can backfire or get stuck in local maxima.More
181: Alison Albeck Lindland: Climb the AI literacy pyramid and stand out as a customer‑first marketer
Alison believes marketing careers thrive when you stay close to the people who buy from you, and at Movable Ink she has built that into the culture with a customer strategy team, advisory boards, and events that create real connections customers carry into new roles. More
179: Tiankai Feng: The comeback of data quality and how NLP is changing the data analyst role
Data governance feels like the Jedi Council, steady with its rules, while marketing ops moves like the Rebel Alliance, quick to adapt when perfect data never arrives. Tiankai believes progress comes from blending discipline with curiosity, bringing data in early as a partner, not a critic.More