As AI becomes more accessible across the workplace, organisations are understandably looking for ways to measure adoption.
How many people have logged in?
How often are they using it?
Which teams are using it the most?
Some organisations are even starting to compare usage across departments, setting adoption targets and introducing AI-related KPIs.
On the surface, that makes sense.
After all, organisations have invested in the technology and they want to see people using it.
The problem is that high usage doesn’t necessarily mean successful adoption.
In fact, it can sometimes mean the opposite.
If people are encouraged to use AI simply because they need to increase their usage statistics, they will find ways to use it. They’ll ask it to rewrite emails, summarise meeting notes or answer questions they already know the answer to. Usage increases, dashboards look healthier and adoption appears to be improving.
But has anything actually changed?
Has productivity improved?
Are people making better decisions?
Are customers receiving a better experience?
Are employees more capable, more confident or more efficient in their roles?
Or have we simply created another metric for people to chase?
Successful adoption has never been about getting people to use technology for the sake of it.
It’s about helping people understand when a capability adds value, when it doesn’t and giving them the confidence to make that judgement themselves.
AI is no different.
The organisations seeing the greatest value from AI aren’t necessarily the ones with the highest usage numbers. They’re the ones investing in their people.
They’re helping employees understand where AI can genuinely save time, improve quality or remove repetitive work. They’re encouraging experimentation in a safe environment, sharing practical examples and creating opportunities for teams to learn from one another.
Most importantly, they’re giving people permission not to use AI when it isn’t the right tool for the job.
That might sound counterintuitive, but it builds trust.
If employees feel they are expected to use AI simply to meet a target, the technology quickly becomes another compliance activity. If, instead, they understand why they are using it and where it creates value, adoption becomes far more meaningful.
The same principle has applied to every technology implementation over the past two decades.
Logging into a new system never meant people had adopted it.
Completing mandatory training never meant people had changed the way they worked.
High system usage never guaranteed better business outcomes.
AI should be no different.
Of course, organisations should measure adoption. Understanding how people are engaging with new capabilities is important, but usage data is only one part of the story. The more valuable questions are often harder to measure.
Are people solving problems more effectively?
Are they making better decisions?
Are they spending less time on low-value activities?
Do they understand when AI should be used and when human judgement remains critical?
These are the measures that tell us whether AI is becoming embedded into the way people work, rather than simply becoming another application people are expected to open each day.
The organisations that will see the greatest return on their AI investment won’t be those with the highest login statistics.
They’ll be the ones that invest just as much in building capability, confidence and understanding as they do in the technology itself.
Because successful adoption has never been about getting people to use a tool. It’s about helping them use it well.