GenAI failures are poised to cost enterprises billions in wasted budget. Many won’t be tech failures.
Gartner predicts 30% of GenAI projects will be abandoned after proof-of-concept. Not because the AI didn’t work, but because the organization wasn’t ready.
The failure patterns are predictable:
Data quality issues. GenAI doesn’t clean your data; it launders your assumptions at scale, producing plausible but incorrect outputs.
Governance gaps. Who approved the training data? Who owns the output? Who’s liable when it hallucinates in production?
Integration debt. The POC worked in isolation. Connecting it to your actual systems can cost multiples of the pilot.
Expectation mismatch. Leadership expected magic. They got a probabilistic system that requires supervision, guardrails, and constant monitoring.
Deployment friction. Without DataOps, every model release is a manual scramble. No CI/CD for data means no fast iteration, and GenAI punishes slow feedback loops.
The organizations succeeding with GenAI aren’t the ones with the best models. They’re the ones with mature data operations, clear ownership, tested pipelines, and governance that doesn’t block every deployment.
AI readiness is data readiness. If you can’t trust your data for dashboards, you can’t trust it for AI.
Before launching another GenAI pilot, ask: Is our data clean enough to train on? Is our governance transparent enough to deploy?
What’s blocking your AI projects from moving past POC?