I have been running AI initiatives for two years now. Year one was expensive in ways that did not show up on any budget line. Here is the honest version.

1. I thought adoption would follow deployment.
It does not. Deployment is a technical event. Adoption is a human event. They require completely different effort. I treated the go-live date as the finish line. It was the starting line.

2. I underestimated how much the pilot group skewed results.
The people who volunteered to test the tool were the most enthusiastic, most technically capable people we had. Their results were real and did not represent what would happen with the rest of the organization. I presented pilot results to the board as if they were predictive. They were not.

3. I did not define what success looked like before we started.
Six months in, when the CFO asked if the investment was working, I did not have a clean answer. Not because the tool was not working but because I had never made explicit what "working" meant in measurable terms. That conversation was uncomfortable and avoidable.

4. I let the vendor run the training program.
The vendor is motivated to show features. My team needed to learn workflows. These are different things. The training we got was a product tour. What we needed was workflow redesign support. I should have run the training myself with the vendor as a resource.

5. I assumed integration meant connection.
We connected the AI tool to our data sources. I called that integration. What I had was a technical connection, not an operational integration. The tool could access the data but nobody had designed when, why, or how it would actually be used in context. Real integration requires workflow design, not just API setup.

6. I ignored the manager layer.
Individual contributors either adopted or did not based on personal motivation. Managers stayed neutral because I had given them no reason to care. AI adoption without manager engagement is adoption that stalls at the enthusiast population and never reaches the mainstream.

7. I did not model the exit.
When one of our early tools lost a key enterprise feature after a pricing restructure, I discovered that we had built processes around that feature with no plan for what to do if it went away. Now I model the exit scenario before signing. What does leaving look like, what does it cost, what do we lose.

8. I treated security as a procurement checkbox.
Legal reviewed the DPA. IT checked the security certifications. Nobody mapped the actual data flows at the operational level, which documents were being sent where, under what conditions, retained for how long. That mapping happened during a compliance review sixteen months into the deployment. It should have happened on day one.

9. I confused activity with impact.
Usage metrics went up. I reported that as progress. Usage is an input metric. The output metrics, the ones that connect AI activity to business results, took much longer to define and instrument. By the time I had clean output data, I had already made three more tool decisions based on input metrics alone.

Year two has been better, largely because year one was honest enough to learn from.