The traditional, linear handoff from product (PRDs) to design to dev is too slow for AI's rapid iteration cycles. Leading companies merge these roles into smaller, senior teams where design and product deliver functional prototypes directly to engineering, collapsing the feedback loop and accelerating development.

Instead of a traditional product roadmap, give engineers ownership of a broad "problem space." This high-agency model pushes them to get "forward deployed" with customers, uncover real needs, and build solutions directly. This ensures product development is tied to actual pain points and fosters a strong sense of ownership.

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.

Whether an idea originates as a problem or a solution is less important than the rigorous validation process that follows. Success hinges on navigating this 'messy middle' to confirm the idea creates enough value that customers will pay for it, regardless of its origin.

Conventional innovation starts with a well-defined problem. Afeyan argues this is limiting. A more powerful approach is to search for new value pools by exploring problems and potential solutions in parallel, allowing for unexpected discoveries that problem-first thinking would miss.

Instead of starting with a blank slate, Nike's team prototypes new ideas by physically cutting and modifying existing products. This "cobbling" method enables rapid, low-cost testing of core concepts before investing in new designs and expensive molds, allowing them to fail fast and forward.

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.

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.

To avoid choosing between deep research and product development, ElevenLabs organizes teams into problem-focused "labs." Each lab, a mix of researchers, engineers, and operators, tackles a specific problem (e.g., voice or agents), sequencing deep research first before building a product layer on top. This structure allows for both foundational breakthroughs and market-facing execution.