In AI development, trace analysis is a point of tension. Product Managers should become fluent enough to ask intelligent questions and participate in debugging. However, they should avoid owning the process or tooling, respecting it as engineering's domain to maintain a healthy division of labor.
Related Insights
To successfully automate complex workflows with AI, product teams must go beyond traditional discovery. A "forward-deployed PM" works on-site with customers, directly observing workflows and tweaking AI parameters like context windows and embeddings in real-time to achieve flawless automation.
Product managers should leverage AI to get 80% of the way on tasks like competitive analysis, but must apply their own intellect for the final 20%. Fully abdicating responsibility to AI can lead to factual errors and hallucinations that, if used to build a product, result in costly rework and strategic missteps.
For product managers not yet working on AI, the best way to gain experience is to build simple AI tools for personal use cases, like a parenting advisor or a board game timer. Using no-code prototyping tools, they can learn the entire development lifecycle—from ideation to prompting and user feedback—without needing an official AI project at work.
AI automates tactical tasks, shifting the PM's role from process management to de-risking delivery by developing deep customer insights. This allows PMs to spend more time confirming their instincts about customer needs, which engineering teams now demand.
As AI becomes foundational, the PM role will specialize. A new "AI Platform PM" will emerge to own core infrastructure like embeddings and RAG. They will expose these as services to domain-expert PMs who focus on user-facing features, allowing for deeper expertise in both areas.
Product leaders must personally engage with AI development. Direct experience reveals unique, non-human failure modes. Unlike a human developer who learns from mistakes, an AI can cheerfully and repeatedly make the same error—a critical insight for managing AI projects and team workflow.
Early AI adoption by PMs is often a 'single-player' activity. The next step is a 'multiplayer' experience where the entire team operates from a shared AI knowledge base, which breaks down silos by automatically signaling dependencies and overlapping work.
When pursuing a long-term strategic solution, dedicate product management time to high-level discovery and partner alignment first. This doesn't consume engineering resources, allowing the dev team to remain focused on mitigating the immediate, more visceral aspects of the problem.
The key technical skill for an AI PM is not deep knowledge of model architecture but a higher-level understanding of how to orchestrate AI components. Knowing what AI can do and how systems connect is more valuable than knowing the specifics of fine-tuning or RAG implementation.
As AI automates synthesis and creation, the product manager's core value shifts from managing the development process to deeply contextualizing all available information (market, customer, strategy) to define the *right* product direction.