The AI Pilot Trap: Why Most Proofs of Concept Never Scale
80% of AI pilots never make it to production. Here's what separates the 20% that do.
The graveyard of pilots
Every enterprise has them: AI pilots that worked in the lab but never made it to production. The demos were impressive. The business case was solid. And then... nothing.
This is so common it has a name: pilot purgatory.
Why pilots fail to scale
The data problem. Pilots use clean, curated data. Production uses messy, incomplete data. The model that worked perfectly on test data fails spectacularly on real data.
The integration problem. Pilots run in isolation. Production requires integration with existing systems, workflows, and permissions. This is often harder than building the model itself.
The change management problem. Pilots have enthusiastic early adopters. Production requires buy-in from people who didn't volunteer.
What successful pilots do differently
Start with production in mind. Before building anything, map out what production deployment looks like. If you can't see a path, the pilot is academic.
Use real data from day one. Resist the temptation to use clean test data. The messy reality is better to face early.
Define success metrics upfront. What business outcome matters? How will you measure it? What's the threshold for scaling?
Plan for the humans. Who needs to use this? What do they need to believe? How will their workflow change?
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