
For your company
AI in the Functions
Module · Numbers that reach the board unchecked
The AI-in-Finance Check
A model will produce a variance analysis that looks flawless and cites a figure that does not exist. In finance that is not an embarrassment, it is a misstatement with your name under it. This module checks the five disciplines that decide whether AI speeds the close or quietly corrupts it: verification of the figures, traceability to a source, a clean line between spreadsheet and model, an audit trail of adjustments, and close-process control that does not bend for a fast answer.
What the five levels look like
Every dimension in this assessment is scored 1 to 5. This is what the levels mean, dimension by dimension. The graded report diagnoses where your own answers land and what to do about it.
Figures are verified
- 1Taken on trust
- 2Eyeballed
- 3Checked when material
- 4Verified before use
- 5Verified against source
At the low end: Trusting AI figures unread is how a hallucinated number ends up in a board pack. Require that every figure headed for a report is checked before it gets there, starting today. What good looks like: Figures verified against their source are the only ones that belong in a report you sign. Keep the bar tied to materiality; the check should scale with the cost of being wrong.
Sources are traceable
- 1No traceability
- 2Source is the prompt
- 3Partial trail
- 4Traceable to data
- 5Full documented lineage
At the low end: A figure with no traceable source is an assertion, and assertions do not survive an audit. Require that AI-produced numbers carry a pointer back to the underlying data before they enter any report. What good looks like: Full documented lineage is what lets you defend an AI-assisted number under scrutiny. Keep it attached to the figure, not filed separately; a trail nobody can find is not a trail.
Spreadsheet-AI line is clean
- 1Mixed invisibly
- 2In people's heads
- 3Loosely marked
- 4Clearly separated
- 5Labelled and controlled
At the low end: AI output blended invisibly into spreadsheets means you no longer know which figures a human stands behind. Mark what came from the model so the checked and the assumed are never confused. What good looks like: A labelled, controlled boundary between model and spreadsheet is what makes the whole workbook auditable. Keep the convention enforced; one unlabelled paste reopens the ambiguity you closed.
Adjustments are logged
- 1No record
- 2Value only
- 3Change noted
- 4Who and what
- 5Who, what and why
At the low end: Unrecorded adjustments erase the moment human judgement changed the numbers, which is the moment an auditor most wants to see. Start logging every override with who made it and why. What good looks like: A full adjustment trail of who, what and why is what separates defensible judgement from unexplained edits. Keep it immutable; a log that can be quietly rewritten is not evidence.
Close discipline holds
- 1Controls skipped
- 2Bends under deadline
- 3Mostly held
- 4Controls hold
- 5Controls hold, tested
At the low end: A close that skips controls for an AI number under deadline pressure has no controls, only intentions. Make review and sign-off non-negotiable regardless of how finished the output looks. What good looks like: Close controls that hold under deadline pressure are what let you move fast without moving loose. Test them deliberately in a real crunch; a control you have never stressed is one you have never verified.