For complex features, a 17-page requirements document is inefficient for alignment. An interactive AI-generated prototype allows stakeholders to see and use the product, making it a more effective source of truth for gathering feedback and defining requirements than static documentation.
Product teams often fear showing prototypes because strong customer demand creates pressure. This mindset is flawed. Having customers eager to buy an unbuilt feature is a high-quality signal that validates your roadmap and is the best problem a product manager can have.
Instead of guarding prototypes, build a library of high-fidelity, interactive demos and give sales and customer success teams free reign to show them to customers. This democratizes the feedback process, accelerates validation, and eliminates the engineering burden of creating one-off sales demos.
AI tools reduce the communication overhead and lengthy handoffs that traditionally separated product and engineering. By streamlining the path from idea to code, AI makes the combined Chief Product and Technology Officer (CPTO) role more viable, enabling a single leader to manage both functions effectively.
Using a non-intrusive hardware device like the Limitless pendant for live transcription allows for frictionless capture of ideas during informal conversations (e.g., at a coffee shop), which is superior to fumbling with a phone or desktop app that can disrupt the creative flow.
AI models tend to be overly optimistic. To get a balanced market analysis, explicitly instruct AI research tools like Perplexity to act as a "devil's advocate." This helps uncover risks, challenge assumptions, and makes it easier for product managers to say "no" to weak ideas quickly.
When an engineering team is hesitant about a new feature due to unfamiliarity (e.g., mobile development), a product leader can use AI tools to build a functional prototype. This proves feasibility and shifts the conversation from a deadlock to a collaborative discussion about productionizing the code.