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Summary

In this episode I’m joined by Chad Holdorf, longtime product and technology leader whose career spans John Deere, Salesforce, Pendo, and now Demandbase, where he leads AI initiatives across the company.

We explore how AI is fundamentally reshaping the way modern product teams test, ship, and learn, from debugging customer issues directly against live codebases to product managers and support teams submitting pull requests themselves. Chad shares how tools like Cursor and Claude are collapsing traditional handoffs between product, engineering, and support, creating a much faster feedback loop between customer problems, experimentation, and shipped solutions.

We also get into the messy reality behind enterprise AI adoption, including data quality, hallucinations, trust, evals, and why testing AI products inside real customer environments is much harder than most demos make it look. Chad gives us a peek into how his own workflow has changed, how his teams are learning by building in real time, and why this moment reminds him of the early days of Lean Startup, where he and I first met.

If you’ve been wondering what AI-native product development actually looks and feels like inside a real company, this episode is for you.

Takeaways

1. AI is collapsing traditional handoffs between product, engineering, and support teams. Chad described customer support teams going directly into code repositories with AI tools to investigate issues, understand root causes, and eventually submit merge requests themselves.

2. Most enterprise AI demos fall apart when connected to messy real-world customer data. Chad emphasized that “just putting Claude on top of the data” failed quickly without extensive labeling, validation, testing, and human feedback loops. Customers could detect hallucinations within a few prompts.

3. AI systems expose hidden data inconsistencies inside organizations. One example showed AI selecting a custom CRM field that technically produced better targeting results than the field support teams were trained to use, creating confusion about which “truth” was actually correct.

4. Trust has become the critical success factor for enterprise AI adoption. Chad explained that once customers encounter inaccurate outputs, confidence in the system drops immediately, which forces teams to spend enormous time improving prompts, SQL queries, evals, and validation workflows before broader rollout.

5. Product managers are increasingly becoming hands-on builders again. Instead of relying entirely on engineering handoffs, Chad now spends large portions of his week inside Cursor and AI coding agents investigating bugs, generating tickets, reviewing repos, and shaping product direction directly through code conversations.

6. AI-native workflows dramatically compress feedback loops. Problems that previously took days of back-and-forth between support, product, and engineering can now move from customer issue to deployed fix in under an hour through AI-assisted workflows and automated merge requests.

7. The biggest organizational bottleneck is shifting away from engineering speed toward enablement and adaptation. Chad compared this moment to early Agile adoption, where downstream teams like sales, support, and training struggled to keep pace with accelerated shipping cycles. AI is now amplifying that challenge even further.

8. Continuous learning and experimentation matter more than formal process mastery right now. Chad repeatedly compared the current AI moment to the early Agile movement: the people progressing fastest are the ones willing to try tools, build things, stay curious, and learn in public rather than waiting for established best practices or certifications.

Guest Links

LinkedIn: https://www.linkedin.com/in/chadholdorf/

Demandbase: https://www.demandbase.com/