Why finance needs a different pilot
Finance teams are not short of ideas for AI. They are short of a way to use it that a controller will sign. Management reporting is the obvious target: after close, someone pulls the trial balance, budget and prior-year extracts, builds the tables, and then writes pages of commentary explaining why marketing spend is over budget and why debtor days moved. The commentary is slow to write and easy to get subtly wrong.
A general-purpose assistant can draft that commentary in minutes. The problem is that the draft looks finished whether or not its numbers are right. In finance, a plausible wrong number is worse than no draft at all. So the pilot is designed around one rule: the model drafts words; your systems supply numbers; a person signs every step.
What the work involves
Source mapping. We take one recurring report and list every figure in it: which ledger account, query, BI report or spreadsheet cell it comes from, and at what point in the close it becomes final. Figures without a reliable source are the first finding.
Source-linked drafting. The drafting step receives figures from your extracts, not from the model’s memory. Each figure in the draft carries a reference back to its source, so a reviewer can open the query or cell beside the sentence that uses it. Text that interprets a figure (“driven by the timing of the annual licence renewal”) is marked as needing confirmation from the budget holder.
Reconciliation checks. Before a reviewer sees anything, automatic checks run: report totals tie to the trial balance, sub-ledger totals tie to control accounts, the period and entity are right, and every variance above your threshold has an explanation. A failed check blocks the draft and says why.
Sign-off steps. The workflow records the preparer, the reviewer and the approver, what each checked, and what each changed. That record is the audit trail.
I have designed and built components of an AI agent stack for regulated financial services, and published a tiered governance model for AI in financial services. That experience shapes where the human approval points sit.
The signature deliverable
You end with a source-linked reporting workflow, reconciliation checks and reviewer sign-off steps. Illustrative example of a sign-off checklist:
| Step | Who | Must confirm | Recorded |
|---|---|---|---|
| Extracts refreshed | Preparer | Period, entity and extract timestamp correct | Extract log |
| Checks pass | Preparer | All reconciliation checks green or exceptions explained | Check report |
| Commentary reviewed | Reviewer | Each explanation confirmed with budget holder or marked | Tracked changes |
| Pack approved | Approver | Totals tie; material variances explained | Approval record |
Illustrative example. The format, not results from a client.
How acceptance is judged
Baseline and pilot are measured the same way: working days from close to approved pack, reviewer corrections per pack, the share of figures with a working source link (the target is all of them), and check failures caught before review. The owner, usually the financial controller, signs off after at least one real month-end where the new workflow ran alongside the old one. The adoption measure is whether the team chooses the new workflow for the next close without being asked.
Ownership and handover
The report, the checks and the templates belong to the finance owner. A named team member pairs with me on the build and maintains the prompts, checks and source map afterwards. The runbook covers how to add a new figure, what to do when a check fails, and how to fall back to the manual process.
Boundaries
This page is about a finance department’s own reporting workflow. If you are deciding whether to invest in AI and how to challenge the value case, that is a CFO workshop at dipankar.org. If you are an accounting or advisory firm handling client data, the controls are about separating clients and partner review: see AI enablement for client-service teams, or AI adoption for professional-services firms for firm-level sequencing. For document-heavy work such as invoice extraction, see document processing and reviewed reporting workflows. For regulated financial-services operations more broadly, see AI engineering and enablement for financial services.