When developing internal AI tools, adopt a 'fail fast' mantra. Many use cases fail not because the idea is bad, but because the underlying models aren't yet capable. It's critical to regularly revisit these failed projects, as rapid advancements in AI can quickly make a previously unfeasible idea viable.

Encourage experimentation with AI, but mitigate risk by giving every pilot a clear budget limit ('a leash') and a deadline ('a clock'). This framework allows for innovation while ensuring underperforming projects are killed quickly, successful ones are funded, and great ones are scaled.

To foster experimentation, leaders should stop judging innovative projects individually and instead group them as a single portfolio. This reframes inevitable failures as part of a diversified strategy, similar to a stock portfolio, where the overall return matters, not the performance of any single asset.

Review your organization's incentive structure for AI. Are employees only rewarded for executing known use cases faster, or are they encouraged to experiment and share lessons? Without explicit rewards for exploration, companies risk stifling innovation and missing out on transformative AI applications that come from experimentation.

To accelerate organizational learning in AI, incentivize the sharing of failures. A Fortune 500 company gives employees redeemable points for sharing use cases, but offers *extra points* for detailing a failed experiment and the resulting lesson. This normalizes failure and prevents others from repeating the same mistakes.

To combat the natural reluctance to admit failure and to foster decisiveness, some innovative companies offer bonuses to employees who kill their own underperforming projects. This practice creates a culture of honesty and overcomes the personal attachment that often keeps bad ideas alive far too long.

When boards pressure CEOs for AI, the result is often a centralized, consultant-led project disconnected from operations. These initiatives fail because they lack alignment and nobody understands how they work, creating skepticism for future efforts.

To combat the natural reluctance to abandon a failing project, leaders should actively incentivize objectivity. One effective, counter-intuitive tactic is to offer a bonus to employees who kill their own ideas, fostering a culture where resources are not wasted on projects that are not working.

Unlike traditional software, AI prototypes can be built almost instantly. This requires a mindset shift: if a project doesn't demonstrate tangible value on its very first day, it should be abandoned immediately. Sticking with a weak AI concept leads to costly slow failure.