The Pilot Graveyard

They fail because they're designed to prove technology, not prove production readiness. A pilot that runs on synthetic data with no integrations, no governance, and no operations plan has proven nothing except that the API works. Production requires solving context, integration, governance, and operations simultaneously, and most pilots only solve one.

Industry data from Gartner and McKinsey consistently shows that 85-95% of AI pilots never reach production. The exact number varies by industry and definition, but the pattern is consistent: the vast majority of funded AI projects die between demo and deployment.

Design for production from day one. That means real data (not synthetic), real integrations (not mocked), real governance (not deferred), and a real operations plan (not 'we'll figure it out'). If your pilot doesn't address all four, it's a demo with a timeline, not a production path.

Almost never. The models work. The APIs are reliable. The tools exist. The problem is methodology: how you structure context, connect systems, implement governance, and operate AI in production. These are engineering and organizational problems, not technology problems.

We skip the pilot. Our engagement model goes straight to production in 4 weeks: real data, real integrations, real governance, real operations. Business-as-Code gives agents structured context from day one. The Embed Model means our team operates inside yours until you hit Escape Velocity.