What AI document analysis means for owner reporting
Extracting structure from long documents - contracts, statements, specifications, policies - became reliable enough to build around. The useful pattern is asking targeted questions of a document you already hold, rather than requesting a general summary you have no way of checking against the original.
( The Detail )
What actually changed
Failures cluster around omission rather than fabrication: a clause missed, an exception not surfaced, a figure taken from the wrong column. Because the answer looks complete, nobody goes looking for what was left out. Anything with legal or financial weight still has to be read by a person.
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.