Building in the Open01 · The short versionThe short version.
Six findings came up often enough, and from enough different corners of the room, that they stopped being anecdotes. They are the spine of this paper. Each becomes a chapter.
The first build is usually a misunderstanding. Before anyone ships anything, they have to settle an argument inside their own organization about what AI even is. Comprehension is the binding constraint. Capability rarely is.
Every project hits the same wall, and the wall is trust. Different missions, same two questions. Where does sensitive data live, and what does an AI say to a person who is not okay at 2 a.m. Several teams are solving the second question in parallel without knowing about each other.
The tools are dissolving. The cohort is drifting off packaged software toward bespoke, internally built tools, and discovering it needs a new kind of person to operate them. Close to the mission, fluent with the machine.
"Time saved" is the wrong scoreboard. Efficiency that backfills with more work misses the point. The real question is what the time is for. That question exposes what a leadership team actually values.
You build trust by being honest, not by selling. The organizations getting buy-in are admitting trade-offs out loud and putting the people they serve inside the design process.
The cohort treats "good actor" as a claim that has to be tested. Learning in the open, and lifting the organizations who were never in the room, is how the claim gets earned.