The ultimate vision for AI in product isn't just generating specs. It's creating a dynamic knowledge base where shipping a product feeds new data back into the system, continuously updating the company's strategic context and improving all future decisions.
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Don't view AI as just a feature set. Instead, treat "intelligence" as a fundamental new building block for software, on par with established primitives like databases or APIs. When conceptualizing any new product, assume this intelligence layer is a non-negotiable part of the technology stack to solve user problems effectively.
Generative AI's most immediate impact for product managers isn't just writing user stories. It's consolidating disparate information sources into a single interface, freeing up the cognitive load wasted on context switching and allowing for deeper strategic thinking.
By creating a central repository infused with company strategy and market data, AI tools can help junior PMs produce assets with the same contextual depth as a 20-year veteran, democratizing product intuition and standardizing quality across the team.
Don't just sprinkle AI features onto your existing product ('AI at the edge'). Transformative companies rethink workflows and shrink their old codebase, making the LLM a core part of the solution. This is about re-architecting the solution from the ground up, not just enhancing it.
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.
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.
Products are no longer 'done' upon shipping. They are dynamic systems that continuously evolve based on data inputs and feedback loops. This requires a shift in mindset from building a finished object to nurturing a living, breathing system with its own 'metabolism of data'.
Instead of holding context for multiple projects in their heads, PMs create separate, fully-loaded AI agents (in Claude or ChatGPT) for each initiative. These "brains" are fed with all relevant files and instructions, allowing the PM to instantly get up to speed and work more efficiently.
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.