A common trap is starting with the assumption that AI must be used, leading to a search for a place to tack it on. This results in superfluous features like a generic "AI assistant," rather than solving a real user need. The correct approach begins with the user's pain.

The design firm Herbst Product operates on the principle that elegantly solving an irrelevant problem is a total failure. This emphasizes the supreme importance of the discovery and definition phases in product development. Before building, teams must ensure they are addressing a genuine, high-value customer need.

A common misconception is that user research involves asking customers to design the product. This is wrong. The process is a clear division of labor: customers articulate their problems and pain points. Your team's role is to then use its expertise and resources to devise the best solution.

A key criticism of AI prototyping is that it encourages teams to immediately build solutions without sufficient problem-space research. PMs must consciously complete user research and define the problem, user story, and rough feature shape before using these powerful solutioning tools.

Just as PMs are warned against solution-bias, the same discipline applies to problems. The goal is not just to find one problem, but to find multiple, then assess which is most valuable, strategically aligned, and worth pursuing for the right audience before committing resources.

Early demos shouldn't be used to ask, "Did we build the right thing?" Instead, present them to customers to test your core assumptions and ask, "Did we understand your problem correctly?" This reframes feedback, focusing on the root cause before investing heavily in a specific solution.

While AI makes prototyping easy, it's not always the right first step. A prototype can create a "primal mark" that biases the team towards a specific execution. For clarifying a vague problem space, a document may be better to avoid anchoring to a visual solution too early.

Directly asking customers for solutions yields generic answers your competitors also hear. The goal is to uncover their underlying problems, which is your job to solve, not theirs to articulate. This approach leads to unique insights and avoids creating 'me-too' products.

Without a strong foundation in customer problem definition, AI tools simply accelerate bad practices. Teams that habitually jump to solutions without a clear "why" will find themselves building rudderless products at an even faster pace. AI makes foundational product discipline more critical, not less.