Where AI belongs in finance
AI works best in finance when it helps a named owner investigate an exception, interpret evidence or draft a conclusion after reliable rules have established the facts.
The short answer
Do not begin with an AI assistant that writes commentary over unreliable numbers. Begin with a defined finance decision, governed source data and deterministic rules that separate normal transactions from exceptions. AI can then make the exception queue easier to understand, prioritise and resolve while finance retains approval and accountability.
A practical finance AI pipeline
- Define the population. Decide which transactions, balances, contracts or close activities are in scope.
- Apply rules. Test completeness, matching, thresholds, period, approval and accounting conditions.
- Auto-process the normal. Let repeatable, low-risk items move through the workflow with recorded evidence.
- Create an exception queue. Show the failed rule, value, risk, age, owner and supporting evidence.
- Use AI for interpretation. Classify patterns, summarise evidence, suggest questions and draft commentary.
- Escalate to a human. The controller or process owner decides, records the action and owns the audit trail.
What AI should do
AI is useful when the task involves language, investigation or prioritisation. It can retrieve an approved policy clause, group recurring reconciliation breaks, identify the evidence most relevant to a variance, or prepare a first draft for a controller. These tasks improve the speed and consistency of review without pretending that an interpretation is a control result.
What deterministic controls should do
Rules should calculate, match, validate and gate. Examples include actual-versus-budget calculations, reciprocal intercompany matching, missing-field checks, ageing thresholds, sequential revenue gates and approval limits. The same inputs and rules should produce the same conclusion. If the result depends on judgment, route it visibly rather than hiding it behind a generated answer.
How to measure value
Measure time to decision, exception ageing, repeat causes, value at risk, resolution at source, reviewer effort and evidence completeness. Automation percentage by itself can reward the wrong behaviour. A finance team creates value when it resolves the right exceptions faster and leaves a stronger control trail.