Measuring the impact of AI
You have invested in AI and the board is asking what it delivered? We connect licences, tokens and pilot projects with what leadership tracks – freed-up capacity, lead time, costs and profit. Before you decide to scale.
You have invested in AI and the board is asking what it delivered? We connect licences, tokens and pilot projects with what leadership tracks – freed-up capacity, lead time, costs and profit. Before you decide to scale.
Nobody measured how the work looked before AI, so there is nothing to compare with.
The number of licences and token consumption are not results. High usage does not mean higher profit.
Development gets faster, but work piles up in testing or product management and the company gains nothing.
Reviewing AI output, maintaining prompts, continuous learning and infrastructure reduce the net benefit – often significantly.
We set up measurement as a stand-alone service or as part of an AI transformation, process mapping or a pilot project. We always connect what you put into AI with what the company gets out of it.
Licences, tokens, hours of training and consulting.
Generated code, automatically created tests and offers, running agents.
Lead time, request resolution time, failure rate – in a specific team or process.
Profit, customer churn, cost-to-serve – after deducting all AI costs.
Measuring just one level is risky. Only the whole chain shows whether AI pays off.
On all four levels, with a baseline and a target.
Performance usually dips in the first months (the J-curve). You know in advance when not to panic and not to stop the pilot.
An overview of hidden costs and real savings instead of rough estimates.
Which AI initiatives to scale, which to adjust and which to stop.
An illustrative example from a software company. Leadership wanted to roll out AI across development so that code would be written faster. First, however, they set a goal that matters to the business – fewer customers leaving – and mapped the whole process from idea to deployment. It turned out that writing code was only a small part of the time; the rest was waiting for tests and integration. So that is where they deployed AI. In the first months delivery slowed down while the team learned, then it sped up significantly. The net saving after accounting for reviewing AI output was smaller than the raw speed-up, but it showed exactly where leadership needed it.
The full approach with model numbers in the article Measuring the business value of AI adoption
A quick AI maturity diagnostic for leadership: where AI already helps you and where to start.
More about AI ScanHalf a day to a day with leadership – metrics for your AI initiatives.
Depending on whether AI is already running or just starting.
When the numbers show where to go next: leaders, processes, teams and roles.
More about AI transformationImprovements in a specific team or process usually within a few months; the financial impact often only after 6–9 months. In the first weeks performance typically dips because people are learning. That is why we set expectations and leading indicators in advance.
No. With a retrospective analysis we build a rough baseline from available reports, add up the costs and evaluate what pays off. A rough estimate today beats perfect data that will never exist.
Those are inputs, not results. High usage says nothing about whether delivery to customers got faster or profit grew. That is why we connect inputs with outputs, local outcomes and company-wide impact.
It can be – as a leadership workshop or a retrospective analysis. Most often, though, we set it up as part of an AI transformation, process mapping or a pilot project, so the metrics are there from the start.
Yes. We use the IOOI framework in agile and organisational transformations and OKR roll-outs too – wherever an investment needs to be linked to a change in results.