Success stories are clean, retrospective narratives. Failures are messy, complex systems problems. An engineer who has navigated a failed AI project has a practical PhD in what actually breaks: biased training data, brittle data pipelines, models that don't generalize to edge cases, and unrealistic stakeholder expectations. They don't just know the theory; they have the scar tissue.

This experience is a powerful filter against hype. These individuals have seen firsthand how flawed assumptions, inadequate resources, or a poor problem-to-model fit can derail a project. They have developed a critical eye for calling "bullshit" on overly optimistic timelines and flimsy cost justifications. They are less likely to be swayed by a vendor's slick demo and more likely to ask the hard, uncomfortable questions that you need to hear.

Stop asking candidates, "Tell me about a success." Start asking:

"Walk me through an AI project that failed. What was the root cause?"

"What was the single flawed assumption at the start that had the biggest downstream impact?"

"If you could go back, what is the one technical or architectural decision you would change?"

The answers will tell you more about their systems thinking and intellectual honesty than any success story ever could.

Hiring for failure isn't about celebrating mistakes. It's about acquiring a unique, high-value skillset: the ability to proactively identify and mitigate the real-world risks that kill AI projects. It's a strategic investment in resilience.