Finance AI Systems Discuss one workflow
Examples

Workflow examples

These are not generic AI examples. They show finance workflows moving from manual coordination to an AI-first operating model, with humans still owning judgement, review, and sign-off.

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Treasury Tax & transfer pricing Close & reporting FP&A / planning Finance transformation

Start with one named workflow

Two examples below are worked in detail. The other areas show where the same approach can apply when a workflow has repeated inputs, source evidence, checks, review points, and exceptions that need human judgement.

Detailed example

Treasury: FX exposure collection and treasury reporting

The shift is from scattered entity inputs and spreadsheet reconciliation to an agent-led workflow that fetches evidence, checks what it can, and escalates exceptions.

Legacy manual process

  • Local entities send templates, corrections, and follow-ups.
  • Treasury downloads ERP, TMS, bank, and cash evidence.
  • A master spreadsheet becomes the reconciliation engine.
  • Mismatches create loops back to entities and source files.

AI-first redesigned process

  • The AI work layer fetches expected inputs and source evidence.
  • Rule-based checks reconcile entity, ERP, TMS, and bank evidence.
  • Matched items flow into the exposure summary.
  • Only unresolved exceptions and draft follow-ups go to the reviewer.
Process map comparing manual FX exposure reconciliation with agent-led reconciliation and exception review.

Old process: distributed manual handoffs and reconciliation loops. New process: one controlled flow with an exception branch for human review.

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Detailed example

Tax & transfer pricing: charge preparation

The shift is from local data chasing and workbook consolidation to agent-led data collection, completeness checks, controlled calculation, and exception escalation.

Legacy manual process

  • Local finance teams send cost bases, allocation keys, and support files.
  • Tax downloads ERP, consolidation, and reporting extracts.
  • A transfer pricing workbook consolidates and calculates the charges.
  • Gaps, inconsistencies, and review questions create rework loops.

AI-first redesigned process

  • The AI work layer fetches expected inputs and checks completeness.
  • Controlled tools check source evidence and run allocation calculations.
  • Complete items flow into a draft charge pack.
  • Policy-sensitive, missing, or inconsistent items go to the tax reviewer.
Process map comparing manual transfer pricing data collection with agent-led charge preparation and exception review.

Old process: local data chasing and workbook reconciliation. New process: controlled preparation with exceptions routed to tax for judgement and sign-off.

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Workflow areas

Other AI-supported workflow areas

The same model can apply beyond treasury and tax wherever the workflow has repeated inputs, source evidence, checks, review points, and exception handling.

Workflow area Close & reporting

Account reconciliation follow-up, accrual preparation, intercompany matching, variance explanation packs, and close evidence gathering.

AI-supported workflow: collect evidence, check completeness, draft follow-ups, prepare first-pass explanations, and route exceptions for review.

Workflow area FP&A / planning

Forecast input collection, scenario pack preparation, business commentary drafting, assumption checks, and KPI pack explanation.

AI-supported workflow: coordinate inputs, check assumptions, prepare scenario packs, draft commentary, and highlight judgement points.

Workflow area Finance transformation

Current-state discovery, process evidence collection, handoff mapping, AI work layer design, and capability transfer for finance transformation work.

AI-supported workflow: structure discovery evidence, map handoffs, identify redesign points, and turn the first workflow into a repeatable operating model.

Next step

Pick one workflow where manual coordination is already expensive.

The best starting point is not the most impressive AI demo. It is a real finance workflow with repeated inputs, source evidence, reconciliation rules, review points, and visible pain.

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