What KPIs to Track for AI Projects (and What to Ignore)

Track KPIs that tie to business outcomes: time saved and reinvested, output per person, error or rework rate, response time, and adoption. Ignore vanity metrics like number of prompts or tools bought, which measure activity, not value. The way to choose the right KPIs is to start from what success looks like for the project: if the goal is faster customer replies, response time is your metric, not how many times someone opened the tool. Pick two or three tied to that goal rather than a dashboard of ten that no one acts on, since too many metrics dilute focus. Adoption is worth tracking as an early signal, because if no one uses the tool, no other metric will move. And every KPI needs a baseline recorded before launch, or you are guessing at change rather than measuring it. This guide covers which KPIs matter, which to ignore, and how to use them to make a real decision rather than just to report.

Start from the outcome you want

Pick KPIs by asking what success looks like for this project. If the goal is faster customer replies, response time is your metric. Our guide on calculating AI ROI connects metrics to dollars.

KPIs worth tracking

• Time saved that is actually reinvested elsewhere.
• Output per person on the target task.
• Error or rework rate.
• Response or turnaround time.
• Adoption: how many people use it regularly.

Vanity metrics to ignore

• Number of prompts run.
• Number of tools purchased.
• Hours of AI usage with no link to output.
• Generic "engagement" with the tool.

Adoption is an early signal

If no one uses the tool, no other metric will move. Track regular use as a leading indicator. The McKinsey State of AI survey links value to embedding AI in real workflows, which shows up first as adoption.

A simple tracking template

1. 1

   Pick two or three KPIs

   Choose ones tied to the project's goal, not activity counts..

2. 2

   Record the baseline

   Capture each KPI before launch..

3. 3

   Track monthly

   Log the same KPIs on a fixed schedule..

4. 4

   Review and act

   Scale, adjust, or stop based on the trend..

For broader context on how technology adoption supports small-business performance, see the U.S. Chamber of Commerce.

Connect every KPI to a baseline

A metric without a starting point is just a number. Before you launch, record where each chosen KPI stands today, time, output, error rate, or response time, so you can measure change rather than guess at it. The baseline is the single most-skipped and most-costly step to skip, because without it you can never prove the project worked or justify expanding it. Two weeks of "before" data is usually enough to give you a fair comparison once the workflow is running.

Keep the number of KPIs small. Two or three tied to the project's goal beat a dashboard of ten that no one acts on. Too many metrics dilute focus and make it harder to see what is actually moving. Our guide on calculating AI ROI shows how to connect those few KPIs to a dollar value when you need to make the case.

Use KPIs to decide, not just to report

The point of tracking is to drive a decision: scale the workflow, adjust it, or stop. Review the metrics on a fixed schedule and be willing to act on what they show, including walking away from a project that is not paying off. Numbers that get collected but never acted on are their own kind of vanity metric. Broad research such as the McKinsey State of AI survey links value to disciplined follow-through, which is exactly what acting on your KPIs looks like in practice.