Where AI actually earns its keep.

Every company we talk to is under the same pressure: "we need to do something with AI." The trouble is that pressure produces demos — flashy, board-meeting-friendly, and abandoned within a quarter. The projects that survive look different from day one.

The three tests

1. Volume. AI earns its keep on tasks that happen fifty times a day, not five times a quarter. A model that saves four minutes on a task your team does two hundred times a week pays for itself before the pilot ends. The same model applied to a rare, high-stakes decision mostly adds risk.

2. Checkability. Can a human verify the output in seconds? Drafting a support reply is checkable — the agent reads it before sending. Summarizing a contract you'll never read is not. The best AI features put a fast human check exactly where the cost of a mistake would land.

3. Tolerance. What happens when it's wrong 5% of the time? If the answer is "someone edits a draft," proceed. If the answer is "we get sued," you don't have an AI project — you have a research program, and you should budget it like one.

Boring is a feature

The highest-ROI projects we've shipped are unglamorous: ticket triage, document retrieval, reorder automation. Nobody demos them at conferences. They just quietly return fifteen hours a week to a team that was drowning. When a client insists on the moonshot first, we ask what they'd do with the boring win's savings — usually the honest answer is "fund the moonshot properly."

Start with the audit, not the model

You don't pick the model first. You map where time actually goes, find the tasks that pass the three tests, and rank them by payback. That's a two-week exercise, and it's the difference between "we tried AI" and "AI runs part of our ops now." It's also, not coincidentally, the first rung of how we scope every engagement.