- Most AI fails after launch, not before — the model works but people do not use it.
- Adoption depends on trust, workflow fit, incentives and skills, not announcements.
- A four-step approach took adoption from 12% to 33% on one programme.
- Treat adoption as a tracked metric and make the AI the easiest path, not an extra step.
Everyone talks about building AI. Almost nobody talks about getting people to actually use it — which is exactly why most AI fails after launch rather than before it. On one programme, the model worked perfectly and adoption sat at 12%. The expensive system was quietly reverting to the old way of working. We took adoption from 12% to 33%, and almost none of that came from touching the model. It came from treating adoption as the real deliverable it always was.
Why adoption stalls
The core mistake is treating launch as the finish line. The model ships, an announcement goes out, and everyone assumes usage will follow. It does not. Adoption depends on four things that have nothing to do with model quality:
- Trust. People will not act on output they do not believe, especially after one bad experience.
- Workflow fit. If using the AI is an extra step rather than the easiest path, people route around it.
- Incentives. If the old way is still rewarded and the new way is not, the old way wins.
- Skills. People need to know not just how to use the tool but when to rely on it and when to escalate.
"Build it and they will come" is the most expensive assumption in enterprise AI.
The four-step approach
1. Diagnose the real barrier
Before any training, find out why people are not using the tool. It is rarely a single reason and rarely just "they need training." On most programmes the real blocker is trust or workflow fit — and you cannot fix a problem you have not correctly named.
2. Make the AI the easiest path
Embed the AI where the work already happens, so using it is less effort than not using it. If people have to leave their workflow, open another tool and copy results back, adoption will never compound. The goal is for the AI to be the path of least resistance.
3. Enable champions
Identify the people other staff already turn to, and equip them first. Champions pull their teams along in a way that top-down mandates never achieve. They also surface the real friction early, because their colleagues tell them what they would never put in a survey.
4. Manage adoption as a metric
Track usage the way you track any other KPI, and tune relentlessly until it becomes habit. What gets measured gets managed; adoption that is nobody's number stays nobody's job.
Why it matters commercially
Adoption is not a soft metric — it is the multiplier on every dirham you spent building the AI in the first place. A system used by a third of the organisation delivers nearly three times the value of one used by an eighth, with no additional build cost. Tripling adoption was, in pure ROI terms, one of the highest-return things we did on the whole programme.
This is the work behind my AI Champions Programme and adoption engagements: diagnosing the real barriers, embedding the tool in the workflow, building champions, and managing adoption until it sticks — so the AI you paid to build actually earns its keep.