They Tried Automating 100% of Product Management (Here’s What Happened)

Can product management be fully automated with AI, and what happens if you try? In this podcast hosted by Hoda Mehr, Co-founder and CEO of Up My Mojo and a Board Member at Products That Count, Descript CEO Laura Burkhauser shares how her team experimented with automating 100% of product management workflow using AI. She discusses the surprising results of that experiment, how AI is changing the role of product managers, and why the future of product work may look very different from the processes teams rely on today.

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Show Notes:

1. AI is pushing product managers to rethink how they work, not just what they build. The role is evolving from coordinating workflows to orchestrating intelligence across tools, data, and teams.
2. The most effective way to adopt AI in product teams starts with a clear goal: automate the tasks people dislike and amplify the work they find meaningful and strategic.
3. At Descript, the product team ran an internal exercise mapping every step of the product development lifecycle and asking how much of each step could theoretically be automated.
4. Trying to automate a process completely—even if unrealistic—helps reveal where AI genuinely adds value and where human judgment remains essential.
5. Giving product managers access to the development environment dramatically increases their ability to explore ideas, understand technical constraints, and collaborate with engineers.
6. Experimentation with “vibe coding” helped product managers understand the power of AI-assisted development, even if building production features is still best handled by engineers.
7. Product managers should use AI to pressure-test ideas before bringing them to engineers, exploring feasibility and edge cases earlier in the process.
8. Connecting AI tools to company knowledge systems—such as customer feedback platforms, documentation, and internal data—makes outputs significantly more useful.
9. The value of AI increases when it combines organizational context with the human judgment that product leaders bring from years of experience.
10. Dictation and voice-to-text workflows can accelerate ideation by capturing raw thinking quickly and letting AI structure it into usable documents.
11. AI can help generate early versions of product documentation, prototypes, and requirement drafts, allowing teams to iterate faster.
12. One of the most immediate productivity gains comes from automating documentation-heavy work such as help center articles, release notes, and feature descriptions.
13. When AI pulls information directly from the codebase, documentation and product explanations become more accurate and consistent.
14. Release marketing workflows that once required coordination across multiple roles can now be largely automated and generated within seconds.
15. Automating repetitive documentation frees roles like product marketing managers to focus on higher-value strategic work such as positioning and sales enablement.
16. The future of product development may rely on extremely small, highly capable teams made up of strong product thinkers, designers, and engineers augmented by AI.
17. A single high-performing trio—product, design, and engineering—can now accomplish what previously required much larger teams.
18. AI is likely to create two types of professionals: highly capable generalists who can build end-to-end products and deep specialists who become dramatically more productive.
19. Organizations should initially experiment without strict cost constraints to discover what AI workflows actually deliver meaningful results.
20. Career growth in product management often comes from following genuine curiosity and passion for building great products rather than strictly following traditional career ladders.