The rapid evolution of AI makes traditional product development cycles too slow. GitHub's CPO advises that every AI feature is a search for product-market fit. The best strategy is to find five customers with a shared problem and build openly with them, iterating daily rather than building in isolation for weeks.
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To successfully automate complex workflows with AI, product teams must go beyond traditional discovery. A "forward-deployed PM" works on-site with customers, directly observing workflows and tweaking AI parameters like context windows and embeddings in real-time to achieve flawless automation.
Many teams wrongly focus on the latest models and frameworks. True improvement comes from classic product development: talking to users, preparing better data, optimizing workflows, and writing better prompts.
For leaders overwhelmed by AI, a practical first step is to apply a lean startup methodology. Mobilize a bright, cross-functional team, encourage rapid, messy iteration without fear, and systematically document failures to enhance what works. This approach prioritizes learning and adaptability over a perfect initial plan.
In the fast-paced world of AI, focusing only on the limitations of current models is a failing strategy. GitHub's CPO advises product teams to design for the future capabilities they anticipate. This ensures that when a more powerful model drops, the product experience can be rapidly upgraded to its full potential.
Unlike traditional software where PMF is a stable milestone, in the rapidly evolving AI space, it's a "treadmill." Customer expectations and technological capabilities shift weekly, forcing even nine-figure revenue companies to constantly re-validate and recapture their market fit to survive.
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.
In AI-native companies that ship daily, traditional marketing processes requiring weeks of lead time for releases are obsolete. Marketing teams can no longer be a gatekeeper saying "we're not ready." They must reinvent their workflows to support, not hinder, the relentless pace of development, or risk slowing the entire company down.
Technical implementation is becoming easier with AI. The critical, and now more valuable, skill is the ability to deeply understand customer needs, communicate effectively, and guide a product to market fit. The focus is shifting from "how to build it" to "what to build and why."
