Strategy
How to measure the ROI of AI training
Training spend is easy to approve and hard to justify later. A simple framework to measure whether your AI education actually moved the numbers.
"We trained the team on AI" is a line item. "We cut report preparation from four hours to forty minutes" is a result. The gap between the two is measurement - and most companies skip it.
Start with the baseline
Before training, pick three to five tasks the team does often and time them. Hours per week, error rate, turnaround. You can't show improvement you never measured.
The three layers of ROI
- Adoption - are people actually using AI? Weekly active users, tasks touched by AI, tools in regular use.
- Productivity - time saved per task, throughput, rework avoided.
- Business outcome - revenue influenced, cost avoided, faster delivery, higher quality.
A simple formula
Net value = (hours saved per week × loaded hourly cost × weeks) − (training cost + tool cost + ramp-up time).
It won't be precise. It doesn't need to be. A defensible estimate beats a vague feeling.
What kills ROI
- No follow-up. A one-off workshop fades in a month. Practice and reinforcement are where the value compounds.
- No standards. If everyone prompts differently, quality is random and nothing scales.
- No owner. Someone has to track adoption and unblock the team.
What we do
Our program is built around your real tasks, with a baseline at the start and a measurable plan at the end. We train for adoption, not attendance.
If you want to talk through how to measure it in your team, get in touch.