Getting out of pilot purgatory
Only about 5% of enterprise GenAI pilots reach production. The other 95% aren’t failing because the models are bad — they’re failing for reasons you can design around.
The number everyone quotes, read correctly
MIT’s NANDA initiative reviewed 300 public deployments, more than 150 executive interviews and tens of billions in spend, and found roughly 95% of enterprise generative-AI pilots produced no measurable P&L impact.¹ Read past the headline: of the enterprise-grade tools evaluated, about 60% got assessed, 20% reached a pilot, and only 5% reached production.²
The barrier wasn’t infrastructure, regulation or talent. It was learning — most systems couldn’t retain feedback, adapt to context, or improve over time, so they stayed frozen at demo quality.²
The four ways pilots die
One: no owner — a pilot with no business owner and no baseline metric can’t prove value, so it never graduates. Two: horizontal hope — buying a generic chatbot and praying departments find a use. Three: no feedback loop, so the tool never gets better than day one. Four: building internally when a specialized partner would ship faster — vendor partnerships succeeded about twice as often as internal builds in MIT’s data.³
“Help employees use AI” is a technology goal. “Cut reconciliation by two days without losing auditability” is an operating problem. Only one of them scales.
What the survivors do
They pick one workflow with a named metric, wire the tool directly into it, and instrument a correction loop so every error becomes training signal. They start narrow and go deep. Mid-market teams that worked this way reported roughly 90 days from pilot to full implementation — faster than the enterprises with far bigger budgets.⁴
That’s how Gigabit deploys: forward-deployed engineers who embed in your stack, own one outcome end to end, and stay past launch. Not a demo — a system in production.