Finance teams often spend valuable time reconciling versions of the same information. Before adding more dashboards or AI tools, it helps to make the reporting chain explicit: where the figures come from, what they mean and who owns them.
Map the reporting journey
Pick a recurring report and follow it from source systems through spreadsheets, adjustments and approvals. Note where a person copies information or resolves a discrepancy. These handoffs are useful candidates for improvement, but first understand why each one exists.
Agree the meaning of the numbers
Document important measures, their calculation rules and their intended use. Revenue, margin, cost and forecast figures can differ across teams for valid reasons. Make those differences visible rather than hiding them behind a single label.
Automate with checks attached
Prioritise repeatable data collection and transformation steps. Add reconciliation checks and clear exception handling alongside automation. An automated process should make it easier to see what changed, what failed and who needs to review it.
Introduce AI where it can be evaluated
Potential starting points include drafting commentary from approved reporting packs or helping analysts find metric definitions. Keep calculations and authoritative figures traceable to their source. Review generated narratives for unsupported explanations and missing context.
Choose a small, useful measure of progress
Track the time needed to prepare one reporting pack, the number of unresolved reconciliation items and the effort required to answer a follow-up question. Use a consistent baseline so the team can assess whether the change made the reporting process more useful.
Consistent definitions and visible checks make a stronger starting point than another layer of reports.
A general perspective for planning discussions. Your organisation’s requirements and specialist advice should guide implementation.
Discuss your Data + AI priorities ↗