Terms of Use

AI industry is one of those topics where the obvious answer is right about sixty per cent of the time, which is exactly often enough to be dangerous.

What changed

Measurement is usually where this falls apart. The decision is usually cheap and reversible; the execution is where the cost lives, and that is where the argument should have happened.

The tooling question is downstream of the constraint question. The things that are easy to count are rarely the things that matter, and once a number reaches a dashboard it starts shaping behaviour whether or not it deserves to. Try writing the constraint on one line before opening a vendor comparison; the line is usually harder than the comparison.

How we knew it was working

The interesting constraint is almost never the one in the brief. Teams that pick both end up with neither, and usually discover this at the point where reversing would have mattered. The version of this that works fits on an index card. The version that fails needs an onboarding session.

Nobody gets credit for the work that did not need doing. A team that changes approach every quarter pays a coordination tax that routinely exceeds whatever the change was meant to fix. One team we spoke to cut their review stage entirely and found throughput unchanged, which told them something the metrics had not.

A shared definition of "done" removes more friction than any tool. They are decisions made quickly, defended slowly, and built upon for six months before anyone recalculates. Set a date at which you will stop, and write down in advance what would make you stop earlier.

Every process is perfectly designed to get the results it gets.

— Overheard in a retrospective

The checklist we ended up with:

• Name one person accountable — not a group
• Decide in advance what would make you stop
• Prefer the reversible option when the evidence is thin

What we would do differently

Consistency is worth more than any individual improvement to AI industry. Subtraction is structurally underrated: the meeting that stopped happening leaves no artefact to point at in a review.

The compounding effects matter far more than the individual wins. Success has many causes and teaches very little; failure tends to have one, and it is usually obvious in hindsight. We ran both approaches in parallel for six weeks. The difference was smaller than the cost of the debate about it.

Documentation is a symptom: you write it where the design is unclear. AI industry rewards clarity here more than almost anywhere else, because the wrong target produces work that looks productive and moves nothing. It is worth saying that we have not run this long enough to be confident.

What we tried first

The expensive mistakes here are rarely the technical ones. Handoffs between people who each hold a coherent local picture and no shared one produce most of the pain later attributed to tooling. A useful test: if this disappeared tomorrow, how long before anyone noticed?

It helps to separate the decision from the execution. It is comfortable, it is legible to management, and it is close to worthless once you measure what it actually changes.

We will revisit this once we have another two quarters of data. The current answer feels right, which is exactly when it is worth checking.