How to measure the effects of AI training? Metrics for HR and team leaders

Plan the measurement before the programme starts. Then you know what to compare afterwards and what data to collect. You do not need an elaborate reporting system at once. A few clearly described tasks and a simple record of their completion is enough to begin.

What does it mean that an employee uses AI?

Logging into a tool, asking a question and using the result in work are three different events. For measurement, adopt a specific definition, for example completing a professional task, checking the result and using it in further work.

The definition should match how often the tasks occur. Someone preparing a monthly report can use AI effectively without doing so every week. Comparing them with someone handling daily enquiries requires that difference to be taken into account.

A record is helped by the task name, the date, how the result was used and any correction. The aim is to assess the usefulness of the solution and the training needs, so agree the scope of information with participants and limit it to what serves the measurement.

1. 01AttendanceWho completed the programme?
2. 02UseDid the AI output reach real work?
3. 03Quality and timeWas the task done well and faster?
4. 04Effect on the teamWhat changed in service or workload?

How to separate activity from benefit

A rise in use is a good signal to look further. You still have to establish whether the results are of adequate quality and whether the whole way of working has become simpler. Frequently generating content that then has to be rewritten can raise activity without saving time.

Worked example, fictional figures: in a group of 30 people, eight initially meet the agreed criterion for regular use. After eight weeks 21 people meet it, that is 70% of the group. We assume records are assessed over comparable four-week periods.

Such a result shows a change in tool use. It does not yet prove a specific percentage rise in productivity. For that you need results from concrete tasks: time, the number of corrections and final quality. If tools, support and the way work is organised all changed at once, the whole change cannot be attributed to one element.

Which metrics to choose for different teams

In sales you can check the preparation of comparable quotes and the number of corrections. In administration, completeness of summaries and the time to prepare them may be useful. In an analytics team, accuracy of information, references to sources and verification time will matter.

Not every department needs an identical metric. What should be shared is the reasoning: an agreed result, a similar scope of comparison, explicit checking time and a named person doing the assessment. That lets HR compare experiences without creating a ranking that only looks comparable.

What should happen after the training

Name a person to lead what comes next and agree time for practice. Participants also need to know which tools and materials they may use, and where to report the cases they cannot handle.

A short review of real tasks shows whether the difficulty comes from how the request was phrased, from missing data, from the tool’s limits or from the process itself. These are different problems requiring different action. Further training is useful when it answers a recognised need.

What this looks like in practice is shown in the case study Eight weeks after the training, 21 of 30 people were using AI regularly.

How to present the result to the board

Show which uses are live, what passed the tests and what was stopped. For each working task add the effect, the basis of measurement and the remaining limitations. Mention too the situations where checking the result proved too laborious.

Such a review makes the decision about further support easier. It may lead to widening one use, changing a tool or focusing on a smaller set of tasks.

At dyrektor.ai we help connect skills development with running concrete initiatives and assessing results.