Measuring AI ROI in a mid-market company

The ROI of AI in mid-market is rarely a single company-wide number; it is a small set of before-and-after measures on a pilot workflow, plus the avoided cost of bad decisions, rework, and cycle time. You can and should use dollars where you already have cost accounting; avoid invented "AI dollar savings" for activities you never measured before.

Good metrics: time to complete a task, error or rework rate, lead response time, tickets resolved per hour, and revenue per rep where the pilot touched that path. Weak metrics: "hours saved" self-reported without a baseline, or "employee satisfaction" as a proxy for revenue.

Governance that protects ROI claims: a single source of truth for the workflow definition, a named owner, and a rule that the pilot is not "extended" to other teams until the first measurement window closes. Scope creep is how ROI math breaks.

For the board: a one-page view with pilot name, baseline, 90-day result, cost of the run (tools + time), and a decision: scale, stop, or redesign. The board is not being asked to love AI; it is being asked to fund the next tranche of evidence.

Programs that pair with this: AI Strategy for Business Leaders, AI for Finance, and Tier 3 Fractional CAO when you need someone to run the portfolio of pilots and the reporting pack.

Mid-market companies that win treat ROI as a discipline, not a press release. Elegantix programs bake measurement into the deliverables, because adoption without evidence does not survive the next budget cycle.