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
223: Lindsay Rothlisberger: Inside Zapier’s AI Center of Excellence for GTM and how they manage context and skills
Lindsay walks through the 6-component AI governance model at Zapier: a golden path to Cursor, a structured shared brain in Google Drive, data policies built with the security team, a visibility layer powered by a custom Zapier agent, a context engineering strategy that fights context rot, and a red-yellow-green skills review gate.More
222: Ashley Langford: How senior MOps practitioners are navigating the 2026 job search
Jason 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.More
221: Jason Dobbs: You need Minimum Viable Readiness for AI because perfect data doesn’t exist
Jason 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.More
220: Alex Halliday: How to build content engineering systems that get cited and scale without slop
Alex breaks down what content engineering actually means: building the systems infrastructure to maintain quality, freshness, and brand accuracy across everything a company has ever put online. He makes the counterintuitive case that great content engineering puts more humans into the content process.More
219: Elizabeth Dobbs: Inside Databricks’ stack with 3 AI agents, 1 lakehouse, and 6 years of data work
Liz spent 6 years at Databricks building the data infrastructure before deploying any AI on top of it. She’s shipped 3 production agents (Marge, Tagatha, and Atlas) and she’ll tell you exactly what broke first and why the team kept going anyway. You’ll hear how a marketing lakehouse becomes the foundation that makes every agent actually work, why the agent label debate is a distraction.More
218: Tata Maytesyan: Build a marketing career that survives AI as a deep generalist
Tata breaks down why the best AI automation targets are the boring, repeatable tasks nobody talks about on LinkedIn, and why the specialist-to-generalist shift in marketing is already happening whether your org chart reflects it or not. She also gets direct about the 10,000-hour threshold for building genuine competence across domains, and the self-preservation fear she hears from leaders every week. More
217: How to interview a company before you take the job (The Martech job hunt survival guide, part 3)
Darrell shares a firsthand account of taking a job under financial pressure, ignoring red flags he recognized in the moment, and landing in a toxic environment within months. What follows is a structured set of interview questions across 6 categories, from leadership self-awareness to what happened to the last person in the role, designed to help you separate the job offer from the job reality.More