The single strongest predictor we have found for whether an AI project reaches production is whether the team built a scored evaluation set before writing the feature. It is a better predictor than budget, team size, or model choice.
The reason is not mysterious. Building the evaluation set forces the argument that everyone was avoiding: what counts as a correct answer. That argument is uncomfortable, it involves the business rather than only engineering, and teams postpone it indefinitely if nothing forces it.
What a usable set looks like
A few hundred real examples beats ten thousand synthetic ones. Draw them from your actual channels, keep the messy ones, and include the cases where the correct behaviour is to refuse or escalate — those are the ones that separate a safe system from an embarrassing one.
Label them with the business, not with the engineers. If a subject-matter expert and an engineer disagree about the right answer, you have found a requirement, not a labelling error.
Then wire it into CI
An evaluation set that runs manually before a release is a document. One that runs on every pull request and blocks a merge when the score drops is a control. The difference in outcome between those two is larger than the difference between most model choices.