AI Failures Aren't the Problem—Our Silence About Them Is
Estimated failure rate of AI pilots: 70-90%. Documented failures: nearly zero.
When a product defect occurs, manufacturers identify and rectify the root cause. When a software outage hits, engineers publish a post-mortem so others can learn. When a hospital logs a safety event, it feeds into protocols that save lives.
But when an AI pilot fails? Silence.
Across healthcare, agriculture, energy, and climate, teams invest millions in AI pilots. Some succeed. Many don't. And when they don't, the lessons vanish: code archived, contracts ended, insights trapped in abandoned project folders. Six months later, another team hits the exact same wall.
This isn't a failure problem. It's a knowledge capture problem.
Why AI Lags Behind
Other industries solved this decades ago. Software engineering has blameless post-mortems. Manufacturing has systematic defect analysis. Healthcare has adverse event reporting. These aren't just nice-to-haves, they're infrastructure. They transform individual setbacks into collective intelligence.
AI has no equivalent. We have scattered blog posts, private lessons learned documents, and conference papers that cherry-pick success stories. What we lack is a systematic way to capture knowledge—a structured approach to understanding what actually drives success and failure in real-world deployments.
The cost is measurable. An agricultural organization develops crop yield prediction models that farmers never adopt—the recommendations conflict with decades of experience, the interface doesn't work offline when connectivity is poor, and the predictions arrive too late for planting decisions. Two growing seasons later, a similar organization in another region builds the exact same system with the exact same results.
A hospital deploys pathology AI that matches expert accuracy but sits unused—pathologists can't understand the model's reasoning well enough to trust it, IT can't integrate it with existing lab systems, and liability concerns leave everyone defaulting to manual review. A year later, another hospital network launches an identical initiative.
Every organization wrestling with data drift, model governance, stakeholder trust, or production infrastructure is solving problems that dozens of teams have already solved. We're not building on shared knowledge. We're rebuilding it, repeatedly, at scale.
Building the Knowledge Base AI Needs
The State of Impactful AI Survey creates what other industries already have: a collective learning system for AI deployment in high-stakes domains.
This isn't about cataloging failures. It's about identifying patterns that drive success. When models perform well in validation but fail in production, what predicts that gap? Which governance frameworks actually reduce rework? What separates pilots that scale from those that stall? Which principles—robustness, sustainability, explainability—correlate with long-term success?
Individually, these are case studies. Aggregated, they become actionable intelligence that accelerates the entire field.
The Path Forward
AI now operates in domains where failure carries real cost. In healthcare, a failed diagnostic AI means delayed treatments and continued diagnostic backlogs. In agriculture, it means another growing season of suboptimal yields while farmers lose trust in the technology. In climate, it means mitigation strategies are deployed too slowly while the window for action narrows.
Progress in these areas requires more than better algorithms. It requires institutional learning at the field level—infrastructure for knowledge sharing comparable to what exists in aviation safety or clinical medicine. It means treating deployment insights as collective assets, not competitive secrets.
The survey establishes a foundation for this shift. By documenting what drives successful AI deployment across sectors, it creates a baseline—a state of learning that didn't exist before. Future teams won't need to rediscover basic principles. They can build on validated patterns and avoid documented pitfalls.
Your experience—whether leading successful deployments or navigating difficult failures—shapes what the field learns next.
The survey takes 10 minutes. In return, participants receive early access to the aggregated results—a first-of-its-kind benchmark showing what's actually working in AI deployment across sectors. You'll also be entered into a drawing for a $250 Amazon gift card.
📊 Contribute to the State of Impactful AI Survey and help build the knowledge base AI deployment needs.
The field's learning curve won't build itself. But it can be built—systematically, collaboratively, and starting now. Survey closes next week.
- Heather
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