Most companies today "have AI". They bought the licenses, ran the training, the guidelines are on the intranet. And six months later, leadership asks where the results are.
The answer is uncomfortable: nowhere. Because buying a tool is not adoption. Adoption is changing the way a company works. And that is a discipline only half related to technology.
Three failure patterns
1. AI as a layer on top. The company takes its existing processes and sticks an AI tool on top of them. People do everything the same way as before, they just occasionally copy something into a chat. A few minutes saved per day, no change in the economics of the work. The real benefit comes only when you rebuild the process around what AI can do - not when you glue AI onto a process designed for people with a different tool.
2. Adoption delegated to IT. AI is not an IT project. It is a project of changing the behavior of hundreds of people. IT can handle security, access, and integrations, but it cannot convince anyone to change a work habit they have had for ten years. Without a business-side owner and visible leadership support, adoption stalls at the first enthusiasts.
3. Measuring activity instead of outcomes. Number of licenses issued, number of people trained, number of prompts per month. These are all activity metrics. Nobody on the board cares about them. They care about time saved in a specific process, faster customer service, lower cost per case. If you do not measure adoption on business metrics, you cannot manage it - and you cannot defend it either.
You can buy licenses in a day. Changing work habits takes months. The companies that know this are the ones that win.
What the successful companies do
Across the projects I have led in banks and industrial companies, the same success pattern repeats:
- They start with specific processes, not tools. They pick three to five processes with the biggest potential, measure the baseline, and rebuild them with AI inside.
- They build internal champions. In every team, one person with the capacity, appetite, and mandate to help others. Bottom-up change management works better than top-down directives.
- Leadership leads by example. When the CEO uses AI to prepare for the board, AI stops being "a toy for juniors". Nothing accelerates adoption more.
- They measure business outcomes from day one. Every use case has an owner, a metric, and a deadline. What does not work gets cut. What works gets scaled.
None of this is rocket science. It is discipline. And that is exactly why it is so rare: discipline does not come bundled with the license.
If you are trying to get AI adoption in your company off the dead center, drop me a line. I am happy to look at where exactly it is getting stuck.
