Ask almost any CTO how their company's AI initiatives are going and you'll get an oddly consistent answer: a handful of promising pilots, a lot of enthusiasm from the original project team, and very little of it actually running the business a year later. Industry surveys keep landing on the same rough number — somewhere around eight or nine out of every ten AI pilots never reach production. That's not a model problem. If it were, better models would have fixed it by now, and they haven't. It's an infrastructure problem, and it's almost always invisible until the project is already underway.
Here's what actually happens. A team builds a proof-of-concept — maybe a document intelligence tool, maybe a customer-facing assistant, maybe a fraud model — and it performs well against clean, curated test data. Leadership gets excited. Budget gets approved for a real rollout. And then it meets reality: real documents are messier than the samples, real usage volume is far higher than the pilot ever saw, and somewhere around month three, someone from security or compliance asks a question nobody prepared for — where exactly is this data going, who can see the logs, what happens if the model gets something wrong on a decision that actually matters. That's usually the moment the project quietly stalls, gets rebuilt, or gets shelved entirely.
