The AI Strategy No One Can Measure (Yet)
Why AI activity is easier to see than AI value
March, 2026
The interesting problem with AI measurement is that most organisations are not short of data. They are short of evidence.
They can count licences, logins, prompts, active users, pilot numbers and reported time savings. They can show adoption dashboards and point to teams using copilots, assistants and AI-enabled workflow tools across the business.
There is usually no difficulty producing a slide that proves activity is increasing.
The harder question is whether any of it changed something the organisation genuinely cares about.
Did a customer issue get resolved faster? Did a manager make a better call? Did a team reduce rework, catch a risk earlier or move through an approval with more confidence?
That is the measurement gap sitting underneath a lot of AI strategy in 2026.
The practical question is not simply how much AI is being used. It is what AI is improving, who owns the outcome and what evidence would make the result credible.
Activity is easy to see
AI is now embedded across everyday work.
Enterprise copilots, generative AI assistants and workflow tools are helping people draft, summarise, analyse, compare, search, recommend and decide faster.
That creates plenty of visible activity.
A team generates more summaries. A pilot reduces drafting time. A workflow produces recommendations. Usage rises, the line on the dashboard moves up and to the right, and everyone feels a little better about the investment.
Sometimes that confidence is justified.
But activity and value are not the same thing.
A tool can be popular while having only a loose connection to the decision it was meant to improve. A team can save time on drafting while the approval still waits in an executive inbox for two weeks. A model can produce a useful recommendation that nobody trusts enough to act on.
The pilot may have improved one part of the process without changing the outcome that justified the investment.
That is why another dashboard of prompts and licences will not solve the measurement problem. Leaders need a clearer view of what happens between AI-assisted work and the decision, service outcome or risk position that follows.
Where value gets lost
Adoption data is useful, particularly early on.
Licence activation, repeat use, pilot participation and feedback from early adopters all help show whether people are engaging with the capability. None of that should be dismissed.
But those measures do not answer the question a board, CFO or business owner will eventually ask:
What changed?
That question tends to make the room slightly quieter.
Often, the value is there. A customer case was handled with better context. A manager had a clearer view before making a call. Analysis that once took days took hours.
But if the organisation cannot connect that improvement to a decision, outcome or accountable owner, the value story becomes difficult to defend.
The dashboard may be accurate and still measure the wrong thing.
It can tell you who used the tool, how often they used it and perhaps how much time they believe they saved. It may not tell you whether the work improved, whether the decision changed or whether the benefit remained after the novelty wore off.
At that point, the organisation may be measuring attendance rather than value.
Shadow AI makes the picture less complete. Useful work may be happening in tools the organisation cannot see, while official reporting gives a very precise account of only the environment it can.
Name the decision, not the tool
The deeper issue is usually ownership.
One team buys the licence. Another runs the pilot. Someone reports adoption. A business area is expected to realise the benefit.
When scrutiny increases, everyone can explain their part, but no one can quite explain the outcome.
That is not only a measurement problem. It is an operating-model problem.
A useful shift is to name the decision or outcome AI is meant to improve.
If the ambition is better customer retention, faster triage, sharper pricing, stronger forecasting or more consistent service recovery, the relevant business leader needs to own that result.
The tool owner can report usage. The business owner needs to explain whether anything important changed.
AI creates value when it helps someone act sooner, make a better judgement, avoid rework, reduce risk or improve a service outcome.
Without a named decision and an accountable owner, it can become another layer of activity across an already complicated organisation: more tools, more dashboards and more governance meetings, without much more confidence.
A practical way to measure it
The answer is not to build a perfect enterprise-wide AI measurement framework before the next steering committee.
That way lies a long workshop and a very tired room.
Start with one decision stream.
Choose something repeatable and close enough to the operating model to show what is really happening: a customer escalation, a pricing approval, a claims decision, a field response, a campaign allocation or a workforce-planning call.
Then work through four questions.
How was the decision made before AI?
Establish the original process, including the time, hand-offs, rework, escalation and judgement involved.
Where is AI now contributing?
Be precise about whether it is drafting, summarising, recommending, prioritising or changing the information available to the person making the call.
What changed?
Look at speed, quality, rework, escalation, risk, confidence and the customer or business outcome. Not every measure will matter in every case.
Who owns the result?
Name the leader or function accountable for deciding whether the change is valuable enough to continue, scale or stop.
Perfect attribution is unlikely. In most organisations, AI will be one influence among several.
What matters is a credible line between the activity and the consequence.
Some use cases will prove valuable. Others will show that the real bottleneck was elsewhere in the process. Both are useful findings.
Progress is more likely when organisations stop treating AI consumption as the outcome and start connecting it to decisions the business already cares about.
Executive note for leaders
Before scaling an AI pilot, dashboard or licence rollout, ask:
What decision is this meant to improve?
If the answer is unclear, the organisation may be measuring activity rather than value.
Name the decision, assign the owner and agree on the evidence that would show whether AI changed the result.
Measure the decision, not the tool.
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If a piece raises a question, surfaces a pattern, or helps you think more clearly about a decision, I’d value the conversation.
Thanks for reading,
Stuart Gonsal MAICD
With occasional help from Springsteen, my Border Collie, who reminds me that clarity comes from movement 🐾.
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Disclaimer
Everything shared in The Ripple Effect reflects my personal views and does not reflect those of my current or past employers, clients or partners. Any examples are illustrative, drawn from publicly known patterns or anonymised experience.