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.

Progress in drug development often hides inside failures. A therapy that fails in one clinical trial can provide critical scientific learnings. One company leveraged insights from a failed study to redesign a subsequent trial, which was successful and led to the drug's approval.

The most valuable lessons in clinical trial design come from understanding what went wrong. By analyzing the protocols of failed studies, researchers can identify hidden biases, flawed methodologies, and uncontrolled variables, learning precisely what to avoid in their own work.

In operations, failure is a problem to be eliminated. In innovation, where new ground is being broken, failures are expected and necessary. Instead of being viewed as mistakes, they must be reframed as valuable data points that provide crucial learnings to guide subsequent experiments and decisions.

Much like a failed surgery provides crucial data for a future successful one, business failures should be seen as necessary steps toward a breakthrough. A "scar" from a failed project is evidence of progress and learning, not something to be hidden. This mindset is foundational for psychological safety.

Reflecting on his PhD, Terry Rosen emphasizes that experiments that fail are often the most telling. Instead of discarding negative results, scientists should analyze them deeply. Understanding *why* something didn't work provides critical insights that are essential for iteration and eventual success.

Negative clinical trial results should not be seen as complete failures. Dr. Adam Arthur explains that even when an intervention fails its primary goal, the data provides crucial learnings that redirect research toward more promising pathways for patient care.

The most effective digital teams and cultures aren't defined by uninterrupted success, but by their capacity to fail, learn, and iterate. This paradoxical approach builds strength and a resilient culture, which is more valuable for long-term innovation than avoiding failure altogether.

In complex systems like rockets, failures during testing are not setbacks but essential parts of the development process. The key is whether the 'failure' produces data that leads to improvements. This 'launch and learn' ethos, pioneered by SpaceX, accelerates progress far faster than trying to predict every issue.