Why adoption, not the model, is the hard part

Most enterprise AI projects don’t fail because the model isn’t good enough. They fail because nobody changes how they work once it ships. The model is the easy 20%. Adoption is the other 80% — and it’s where almost all the value lives.

The gap between “works in a demo” and “works in the business”

A model that scores well on a benchmark and a product that people trust with real decisions are two very different things. Closing that gap means confidence scoring, human-in-the-loop review for the cases that matter, explainability where the process is regulated, and a workflow that fits how the team already operates.

What actually moves adoption

• Start from the workflow, not the model. Find the step that’s slow, painful, or error-prone, and aim the system there.
• Make the first win obvious. One clearly-better outcome beats ten marginal ones.
• Design for trust. Show the work. Let people override it. Earn the autonomy.

The best AI product is the one people forget is AI — it’s just the faster way to do the job now.

This is the throughline across everything I’ve shipped: the technology is necessary, but the outcome is decided by whether people adopt it. That’s the part worth obsessing over.