World Model Readiness When AI touches the numbers

Module · When AI touches the numbers

The Finance Team AI Check

Finance runs on numbers that have to be right, and AI produces numbers that are only usually right. A model that fabricates a plausible figure with total confidence is a specific hazard when the output becomes a forecast, a reconciliation, or a line in the close. This module checks the five habits that keep AI a help rather than a hidden error: whether your team verifies before trusting, whether AI outputs get reconciled to a source, whether you can audit how a number was produced, whether the same person builds and approves, and how far AI has crept into the close itself.

Question 1 of 5 · Verify before trusting

Does your team verify AI-produced numbers before using them, or trust them by default?

A language model will state a wrong figure as confidently as a right one, with no tell in the output. In finance that confidence is the danger: a fabricated number reads exactly like a correct one until someone traces it. The habit of checking before trusting is the whole defence, and it erodes the moment the tool seems reliable.

Question 2 of 5 · Reconciled to a source

Are AI outputs reconciled back to a system of record, not just accepted as given?

An AI can summarise a ledger, categorise transactions, or draft a reconciliation, and be subtly off in ways that only surface against the source. Reconciliation is the control that catches this: the AI proposes, the system of record disposes. Skip it and the AI's version quietly becomes the truth nobody checked.

Question 3 of 5 · The number is auditable

For an AI-assisted figure, can you show how it was produced, from prompt to formula?

When an auditor or your CFO asks how a number was arrived at, "the AI worked it out" is not an answer. You need the trail: the inputs, the prompt or formula, the model, the version. A figure you cannot reconstruct is a figure you cannot defend, and finance lives on defensible figures.

Question 4 of 5 · Duties stay separated

Does AI let one person now do what used to need two, collapsing your segregation of duties?

Segregation of duties exists so the person who creates a transaction is not the person who approves it. AI quietly erodes this: a single analyst with a capable assistant can now build, populate and approve work that once required a second pair of eyes. The control did not fail loudly, it just stopped applying.

Question 5 of 5 · Close-cycle AI is governed

How far has AI moved into your period close, and is that use controlled?

The close is the highest-stakes, most time-pressured work finance does, which makes it exactly where an unverified AI shortcut is most tempting and most dangerous. Using AI to draft journal narratives is one thing; letting it propose entries or accruals under deadline pressure is another. The question is whether that line is drawn deliberately.

For the statistics · one click each

Three questions for the public picture

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Where does your finance team use AI on the numbers today?

Not on the numbers
Analysis and drafting
Reconciliation and categorisation
Forecasting and reporting
Inside the period close

What does your team do before trusting an AI-produced number?

Trust it as given
A quick glance
Verify when it matters
Verify against a source
No AI on the numbers

Can you reconstruct how an AI-assisted figure was produced?

No trail
Only from memory
Partly documented
Fully reproducible
No AI-assisted figures

Your context

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