Using AI to Write Management Accounts Commentary and Board Pack Narrative

Why the Narrative Takes Longer Than the Numbers

By working day five or six, the hard accounting is usually done. The trial balance is closed, the accruals are in, the management accounts reconcile. What remains is the part that quietly eats the next day and a half: turning those numbers into words a non-executive director can absorb in fifteen minutes. What kind of month was it? What changed, and why? What should the board worry about next?

This writing work is not analysis (the analysis is done), and it is not free composition (the pack follows the same shape every month). It is translation: finished numbers plus known context into a fixed format, in a house tone, at a length the board expects. That is precisely what large language models draft well, which is why board pack narrative has become one of the highest-return AI use cases in finance. Our month-end close audits find the narrative phase among the worst bottlenecks almost every time.

One boundary to draw before we start. This guide covers the pack-level narrative: the executive summary, the KPI story, cash, and outlook. The line-by-line variance write-ups that sit inside the pack are their own discipline with their own templates, and we cover those in depth in our variance commentary prompts guide. Here we treat the variance section as one input among several and focus on the layer above it.

What a Board Pack Narrative Actually Contains

Strip out the tables and charts and most UK board packs contain four or five written components, each doing a different job for the reader.

The executive summary. The only page some directors read properly. It answers three questions in under 200 words: what kind of month was it, what drove the result, and is anything on the horizon that needs board attention. It is written last but read first, which matters for the workflow later.

KPI commentary. A short narrative around the metrics page: which indicators moved, which matter, and whether a movement is a trend or noise. Good KPI commentary only writes about the metrics that changed the story.

The variance section. Line-level explanations of actual against budget. This is the part covered by our RACEF variance templates, so we will not re-teach it here; its output feeds the executive summary.

Cash and working capital. Often the section boards care about most and the one written in the biggest hurry. It needs the closing position, the bridge from last month, debtor and creditor movement, and a forward view against facilities or covenants.

Risks and outlook. The forward-looking close: next month and next quarter against forecast, known headwinds, and anything the board should decide rather than merely note. If your pack also carries a dashboard or metrics visuals, that is a separate build we cover in financial reporting dashboards with AI; this post is about the words, not the charts.

Six Copy-Paste Prompts, Section by Section

Each prompt below is self-contained: fill in the brackets and paste into Claude or ChatGPT. For monthly use, load them into a Claude Project or ChatGPT Project with two recent packs as style references so you only paste the data each month. Our ChatGPT setup guide for finance teams walks through that setup in detail.

Worked Example: One Month, One Pack

Here is the shape of this in practice, with realistic numbers for a UK distribution business turning over around £14m a year. The controller closes the month and summarises the finished accounts into a dozen lines:

Prompts 2 to 4 draft the sections from this summary plus the context notes. Then Prompt 1 compresses them into the executive summary. This is the kind of draft that comes back:

Every figure in that draft traces to the input summary; the judgement calls that make it useful came from the prompt structure and the context notes, and they are exactly what the reviewer checks. Board-pack commentary is also one of the hands-on lessons in our AI for Finance Leaders course, where you build this workflow on your own pack.

The Assembly Order That Makes This Work

The workflow matters as much as the prompts, and it mirrors how good packs were always written: sections first, summary last.

Once the accounts are closed, write the one-page numbers summary (ten minutes, and it doubles as your review checklist). Draft the variance section using the line-level templates, then run Prompts 2, 3 and 4 for KPIs, cash and outlook. Only then run Prompt 1, feeding it the drafted sections, so the executive summary is a genuine compression of the pack rather than a separate essay that drifts from it. Run the consistency pass (Prompt 6), fix what it flags, and hand the pack to the reviewer. Expect to tune tone and length in month one; by the third cycle the edits are mostly context you chose not to type in.

Limitations and Data Safety

Two failure modes account for nearly every bad AI-drafted pack. The first is missing context: the model cannot know the overspend was board-approved or that a customer is quietly in difficulty, so a draft built only from numbers will be fluent and miss the point. The context lines in every prompt above are the fix; they are not optional. The second is misplaced trust: confident-sounding output gets skimmed. Review an AI draft like a capable junior's first month: check every figure against the accounts, and ask whether the story is the one you would tell.

And the standing rule that applies to everything in this post: the AI drafts, a qualified person signs. Nothing goes to the board without a reviewer who can stand behind every sentence.

Frequently Asked Questions