I keep a spreadsheet. Every AI tool I evaluate inside our corporate stack goes into row by row, with three columns: install date, uninstall date, and the one sentence I would tell a colleague who asked.
This year, the spreadsheet has 34 rows. 33 have an uninstall date. One does not.
This is the story of the one that stayed, and what made it different from the 33 that did not.
The Tool
It is a meeting transcription and summary tool. I am not naming it because the specific vendor is not the point. There are six tools in this category and they are mostly interchangeable on the feature axis. What kept this one installed was not its features.
What Killed the Other 33
I went back through the spreadsheet and tagged every uninstall reason. Three patterns covered 30 of the 33:
1. It required me to remember it existed. The tool sat in a tab. To get value, I had to open the tab, paste something in, copy something out. Friction kills tools faster than missing features. About half of the uninstalls were tools that worked fine but never made it into my actual workflow.
2. It produced output I did not trust enough to ship. Many tools produced summaries, drafts, or analyses that were 80% correct. That sounds high. In practice it means I had to read the entire output to find the 20% that was wrong, which took as long as writing it from scratch. About a third of uninstalls fell here.
3. It introduced a vendor that legal did not want to approve. The remaining handful were tools that worked beautifully but routed our data through a vendor we had no agreement with. I uninstalled before I got the rejection letter.
What the Survivor Did Differently
The tool that stayed solved exactly one problem: it ran in the background. It did not ask me to remember it. It did not ask me to open a tab. It transcribed every meeting on my calendar without me thinking about it, and it dropped a summary into a folder I already had open every day.
The output was not better than the competitors. It was the same. But because the workflow required zero conscious activation from me, I used it constantly. The competitors lost not because they were worse but because I forgot they existed by Wednesday.
The single biggest predictor of whether an AI tool sticks inside a corporate stack is not output quality. It is whether the tool can be used without changing any habit you currently have.
The Test I Now Run First
After the 33 uninstalls, I changed how I evaluate. The first question I ask now is not “what does this tool do?” It is “where in my existing day does this tool insert itself, and what step in my current routine does it replace?”
If I cannot answer that question in one sentence, the tool will not survive a month. I have not been wrong about this in the last six evaluations.
The Spreadsheet Itself
A few people have asked for the spreadsheet. I have not shared it because the column that matters, “the one sentence I would tell a colleague,” is full of opinions about specific vendors that I cannot publish without picking fights I do not want to have.
What I will share is the column header structure, which is the actual reusable artifact:
- Tool name
- Install date
- Stack location (where in my workflow does it live)
- Replaces (what existing step does it eliminate)
- Output trust (1–5: would I ship this without reading it?)
- Vendor status (approved / pending / unknown)
- Uninstall date
- One sentence
The output trust score is the column I added latest. It is the one that predicts uninstalls best.
What I Would Do Differently
If I were starting this evaluation process over today, I would not install anything for two weeks. I would spend those two weeks writing down every fifteen-minute block in my actual workday, and what task I was doing in it. Then I would only install tools that mapped to a block.
I did the opposite. I installed everything I read about and tried to find a use for it. The 33 uninstalls were the cost of learning that order matters.