Accountancy

Month-end variance commentary drafted from the trial balance

A finance team could use AI to draft variance commentary directly from the trial balance and prior-period narratives, ready for the FD to review.

In-house finance team A couple of months to value

The challenge

The FD often spends a full day at month-end writing narrative commentary explaining cost-centre variances. The numbers are ready by Tuesday but the board pack does not go out until Friday.

The approach

1Export the trial balance and prior-period commentary into a structured format.
2Use AI to draft variance explanations cost-centre by cost-centre, in the firm's house style.
3Surface anomalies and material changes for the FD to focus on.
4Keep a clear audit log of edits between draft and final.

Potential outcome

Board pack turnaround reduced from days to hours.
More consistent commentary across months and reviewers.
FD time spent on advice and forecasting, not first drafts.

Tools used

Finance system export LLM with house-style prompts Audit log

How a sensible pilot could look

The fastest way to test a use case like this is a tightly scoped 30-day pilot rather than an open-ended rollout. The shape we recommend in almost every UK SMB is the same: one workflow, one owner, one success metric, one decision date. The point is to learn quickly and cheaply, not to transform the business in month one.

In week one, map the current workflow end to end and time it. This baseline is non-negotiable - without it, you can't tell whether the AI made things better, worse, or about the same. In week two, set up the tool and train two or three people deeply rather than rolling it out widely. In week three, run the new workflow alongside the old one and capture friction in writing. In week four, review the data, decide go or no-go, and write up what you learned.

Even a no-go is a successful pilot if you understand why. The worst outcome is a 'maybe' that drags on for another month and quietly absorbs the budget.

What to watch out for

Picking too broad a workflow. Narrow it until it almost feels too small - you can always widen later.
Skipping the baseline. If you don't know what 'before' looked like, you'll argue about whether 'after' is better.
Removing the human review too early. Almost every successful AI rollout in an SMB keeps a person on the final decision for far longer than the vendor suggests.
Letting the success metric become a feeling. 'The team likes it' is not a metric. Time saved, error rate, response time, conversion - pick something measurable.
Pasting client or customer data into a public tool. Use the approved one, or don't paste it at all.

Questions worth asking before you start

Who owns this? Not a steering group - one named person whose job it is to make this work, with the authority to change the workflow rather than just observe it.
What does success look like in numbers? Pick one metric, write it down on day one, and don't change it mid-pilot.
What data is the AI allowed to see? Be explicit about what's in scope, what's out of scope, and where outputs are stored. Document it before the pilot starts, not after.
What happens at day 30? Diary the go / no-go meeting now. Invite the people who can actually decide, not just the people who'll be in the room anyway.

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