The temptation to use AI to rapidly generate, prioritize, and document features without deep customer validation poses a significant risk. This can scale the "feature factory" problem, allowing teams to build the wrong things faster than ever, making human judgment and product thinking paramount.
Synthetic users, like a stranger at a bar, can provide unfiltered, emotionally rich feedback during simulated interviews. This happens because there's no social barrier or fear of judgment, leading to the discovery of edge cases and deeper motivations that real users might not share with a human interviewer.
AI tools like Vibe Coding remove the traditional dependency on design and engineering for prototyping. Product managers without coding expertise can now build and test functional prototypes with customers in hours, drastically accelerating problem-solution fit validation before committing development resources.
As AI automates time-consuming tasks like data analysis, requirement writing, and prototyping, the product manager's focus will shift. More time will be spent on upstream activities like customer discovery and market strategy, transforming the role from operational execution to strategic thinking.
AI-driven synthetic user interviews can uncover deep emotional insights that real users might not share with a stranger. However, they fail to capture unique, real-life situational problems (e.g. a parent escaping a toddler), making a hybrid research approach essential for a complete picture.
AI's benefits for product teams are not just about acceleration. The "Accelerate, Expand, Simplify" framework highlights AI's ability to enable previously impossible tasks (Expand) and remove reliance on other teams like subject matter experts (Simplify), offering a more holistic view of its impact.
Despite public hype around powerful consumer AI, many product managers in large companies are forbidden from using them. Strict IT constraints against uploading internal documents to external tools create a significant barrier, slowing adoption until secure, sandboxed enterprise solutions are implemented.
When using prioritization frameworks like RICE for AI-generated ideas, human oversight is crucial. The 'Confidence' score for a feature ideated by AI should be intentionally set low. This forces the team to conduct real user testing before gaining confidence, preventing unverified AI suggestions from being fast-tracked.