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.

Instead of creating a massive risk register, identify the core assumptions your product relies on. Prioritize testing the one that, if proven wrong, would cause your product to fail the fastest. This focuses effort on existential threats over minor issues.

Spend significant time debating and mapping out a project's feasibility with a trusted group before starting to build. This internal stress-test is crucial for de-risking massive undertakings by ensuring there's a clear, plausible path to the end goal.

When facing a major technical unknown or skill gap, don't just push forward. Give the engineering team a dedicated timebox, like a full sprint, to research, prototype, and recommend a path forward. This empowers the team, improves the solution, and provides clear data for build-vs-buy decisions.

Instead of complex prioritization frameworks like RICE, designers can use a more intuitive model based on Value, Cost, and Risk. This mirrors the mental calculation humans use for everyday decisions, allowing for a more holistic and natural conversation about project trade-offs.

Treating AI risk management as a final step before launch leads to failure and loss of customer trust. Instead, it must be an integrated, continuous process throughout the entire AI development pipeline, from conception to deployment and iteration, to be effective.

Shift the definition of "done" from "code checked in" to "logged in as the user and verified the feature works as intended." This simple directive forces engineers to engage with the product from a user's perspective, fostering ownership and higher quality work.

Traditional risk registers are performative theater. Use a 'Learning Board' with three columns: 'Assumption,' 'Test,' and 'What We Learned.' This reframes risk management as a continuous discovery process and serves as a transparent communication tool for stakeholders, replacing bureaucratic documentation.

Unlike a failed feature launch, business viability risks (e.g., wrong pricing, changing market) kill products slowly. By the time the damage is obvious, it's often too late. This makes continuous monitoring of the business model as critical as testing new features.