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.

Don't evaluate your team's AI readiness as a standalone capability. True AI strategy requires a deep understanding of customer problems and unique value. Without strong core product competencies, AI adoption is merely tactical, not strategic.

The barrier to building AI products has collapsed. Aspiring builders should create a one-hour prototype to focus on the truly hard part: validating that they're solving a problem people actually want fixed. The bottleneck has shifted from technical execution to user validation.

In AI, low prototyping costs and customer uncertainty make the traditional research-first PM model obsolete. The new approach is to build a prototype quickly, show it to customers to discover possibilities, and then iterate based on their reactions, effectively building the solution before the problem is fully defined.

The traditional SaaS method of asking customers what they want doesn't work for AI because customers can't imagine what's possible with the technology's "jagged" capabilities. Instead, teams must start with a deep, technology-first understanding of the models and then map that back to customer problems.

Don't let the novelty of GenAI distract you from product management fundamentals. Before exploring any solution, start with the core questions: What is the customer's problem, and is solving it a viable business opportunity? The technology is a means to an end, not the end itself.

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.

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.

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.