What AI developer tools means for owner reporting
The tooling around building with models - testing, versioning prompts, tracing what happened, controlling cost - matured to the point where a small team can run something in production without a platform team behind it. That lowered the floor for internal tools quite considerably.
( The Detail )
What actually changed
The remaining difficulty is operational rather than technical. Costs move with usage in ways that surprise people, behaviour shifts when an underlying model is updated, and debugging a probabilistic step is unlike debugging code. Budget for maintenance attention, not only for the initial build.
What to test first
Test it on assembling the weekly numbers you already track by hand: pulling exports together, calculating the same figures each week, and flagging what moved. The value is removing repeat labour rather than producing new insight, and the output can be checked against what you used to build manually.
It is not worth adopting if the figures cannot be reconciled to a source you trust, or if it produces commentary you would not be willing to defend. A report you verify line by line every week has replaced one task with another, and the honest response is to keep the spreadsheet.
How to approach it
Begin with the decision rather than the tool. Name the recurring judgement this affects, the information it depends on, and the person accountable for acting on the result. That framing keeps the first build small enough to inspect and useful enough to matter.
Keep a human review point in the loop until the quality and the failure modes are understood. A system that shows its working - what it drew on, where it is uncertain, and what it deliberately left alone - is one a business can keep running after the initial build.
( Next Step )
Start small enough to review, but on a workflow important enough to show whether a better system is worth building.