There is an entire genre of AI conference talk that opens with a frontier model demo and closes with a slide of robots in a factory. We watch those talks too. Then we go back to the work — which, almost without exception, is not that.
The AI projects that actually pay for themselves in mid-market operations look like this: a model that decides which of three queues a customer ticket belongs in. A classifier that flags expense reports that look unusual against this team's historical pattern. A summarizer that turns yesterday's 400 inbound emails into a one-page brief for the regional manager.
None of these are demos. None of them get a press release. All of them save an hour a day, every day, for the rest of the year — and the hour they save tends to be the worst hour of someone's job.
The pattern we see in the engagements that work: pick a workflow where a human is currently doing three things — gathering inputs, applying a rule, and writing it down. Automate the first and the third. Leave the second to the human until you're sure the rule is stable. The wins compound because the gathered inputs and the written-down outputs become training data for the next thing.