The misconception that discovery slows down delivery is dangerous. Like stretching before a race prevents injury, proper, time-boxed discovery prevents building the wrong thing. This avoids costly code rewrites and iterative launches that miss the mark, ultimately speeding up the delivery of a successful product.
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During product discovery, Amazon teams ask, "What would be our worst possible news headline?" This pre-mortem practice forces the team to identify and confront potential weak points, blind spots, and negative outcomes upfront. It's a powerful tool for looking around corners and ensuring all bases are covered before committing to build.
Engineering often defaults to a 'project mindset,' focusing on churning out features and measuring velocity. True alignment with product requires a 'product mindset,' which prioritizes understanding the customer and tracking the value being delivered, not just the output.
To get buy-in from skeptical, business-focused stakeholders, avoid jargon about user needs. Instead, frame discovery as a method to protect the company's investment in the product team, ensuring you don't build things nobody uses and burn money. This aligns product work with financial prudence.
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.
When handed a specific solution to build, don't just execute. Reverse-engineer the intended customer behavior and outcome. This creates an opportunity to define better success metrics, pressure-test the underlying problem, and potentially propose more effective solutions in the future.
Historically, resource-intensive prototyping (requiring designers and tools like Figma) was reserved for major features. AI tools reduce prototype creation time to minutes, allowing PMs to de-risk even minor features with user testing and solution discovery, improving the entire product's success rate.
When pursuing a long-term strategic solution, dedicate product management time to high-level discovery and partner alignment first. This doesn't consume engineering resources, allowing the dev team to remain focused on mitigating the immediate, more visceral aspects of the problem.
Instead of arguing for more time, product leaders should get stakeholder buy-in on a standardized decision-making process. The depth and rigor of each step can then be adjusted based on available time, from a two-day workshop to an eight-month study, without skipping agreed-upon stages.
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.
