Before launch, product leaders must ask if their AI offering is a true product or just a feature. Slapping an AI label on a tool that automates a minor part of a larger workflow is a gimmick. It will fail unless it solves a core, high-friction problem for the customer in its entirety.

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.

AI tools can handle administrative and analytical tasks for product managers, like summarizing notes or drafting stories. However, they lack the essential human elements of empathy, nuanced judgment, and creativity required to truly understand user problems and make difficult trade-off decisions.

It's a common misconception that advancing AI reduces the need for human input. In reality, the probabilistic nature of AI demands increased human interaction and tighter collaboration among product, design, and engineering teams to align goals and navigate uncertainty.

The ease of AI development tools tempts founders to build products immediately. A more effective approach is to first use AI for deep market research and GTM strategy validation. This prevents wasting time building a product that nobody wants.

As AI commoditizes the 'how' of building products, the most critical human skills become the 'what' and 'why.' Product sense (knowing ingredients for a great product) and product taste (discerning what’s missing) will become far more valuable than process management.

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.

In the rush to adopt AI, teams are tempted to start with the technology and search for a problem. However, the most successful AI products still adhere to the fundamental principle of starting with user pain points, not the capabilities of the technology.

Teams that become over-reliant on generative AI as a silver bullet are destined to fail. True success comes from teams that remain "maniacally focused" on user and business value, using AI with intent to serve that purpose, not as the purpose itself.