When Finance is deciding what to automate, time saved is often the easiest benefit to estimate. But some of the best candidates are processes where small errors create disproportionate work: a report has to be reissued, a reconciliation has to be reopened, or someone has to trace a figure through several spreadsheets to find where it went wrong.
The business case should capture both benefits: the routine work the team can stop doing and the errors it can prevent or catch earlier. In some cases, improving accuracy may be the stronger reason to make the change.
Take a monthly report that draws on several systems. Someone exports the data, maps account codes and checks that the totals agree. Each step may be straightforward on its own, but a missing file, an outdated mapping or a duplicate import can lead to incorrect figures that take hours to investigate.
A workflow can check that every expected file has arrived, apply agreed mappings and reconcile totals to the source before the report is released for review. If a check fails, it can hold the output and tell the owner what needs attention. That can be done without AI, with Finance still responsible for reviewing the figures.
The rules need testing, because automation can repeat a bad mapping just as consistently as a good one. At Positive8, we assess the source data and how the systems can connect, then build and test the agreed checks, transformations and workflows with Finance and IT. That includes deciding what happens when the usual rules do not apply and how failures will be monitored.
Start with a few representative periods and record how many errors were found, how serious they were and what it took to put them right. Include investigation, correction and any repeat review or approval. Where an error led to a measurable cost, such as an external correction fee, record that separately.
Use the same definitions and review checks during the pilot. If the new workflow flags more issues at first, it may be catching errors that previously went unnoticed. A useful comparison is how many errors still reach the finished output, alongside the time the team spends dealing with exceptions.
Here’s an illustrative example for a process that handles 500 records a month:
| What changes | How to value it | Annual value |
|---|---|---|
| Routine preparation falls from 12 hours to 4 hours a month | 8 hours a month at £40 an hour | £3,840 |
| Errors needing correction fall from 20 to 5 a month | 15 fewer corrections at 20 minutes each, valued at £40 an hour | £2,400 |
| Total capacity released | 156 hours a year, before automation costs | £6,240 |
The preparation figures in this example exclude error correction. If the eight hours already include time saved on corrections, adding the £2,400 would count the same benefit twice.
The £6,240 represents the value of staff time released, which becomes a cash saving only if spending falls. Where a material error is rare and its financial impact is difficult to estimate, describe the risk separately and explain how the new checks would reduce it. That still gives Finance something useful to weigh in the decision.
Include the work needed to connect systems, prepare the data, build the workflow and test the controls, as well as licences, infrastructure, monitoring and support. Compare implementation and running costs over the same period as the expected benefits, and check whether the case still holds if fewer errors are prevented or more exceptions need manual review than the pilot suggested.
A good first project has a clear problem, rules Finance can agree and a result you can measure. If there’s a process your team regularly has to check twice or correct after the event, we can help you assess what it would take to improve it and whether the benefit justifies the cost.