The gap between a proof of concept and a production system is where most AI initiatives die. Not because the technology doesn't work. Usually it does, in a demo. The problem is everything around it: the data isn't clean, the team doesn't trust it, the process it's supposed to replace is more complicated than anyone realised, and nobody budgeted for the six months of iteration it takes to get something reliable.
The pattern we keep seeing
A company hires a consultancy or builds an internal team to run an AI pilot. The pilot works on a curated dataset. Leadership gets excited. Then someone tries to run it on real data and it falls apart. The pilot gets shelved. Six months later, someone proposes another pilot.
The issue isn't ambition or talent. It's that pilots are designed to prove a concept, not to survive contact with reality. They optimise for showing what's possible, not for handling what's messy.
What to do instead
Start with the workflow, not the technology. Find the thing that's costing you the most time or the most errors. Build a solution that handles the boring 80% reliably, and leave the interesting 20% to humans. Ship something small that works every day rather than something impressive that works once.