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AITHENTIC finance use cases

Agentic AI use cases in finance and accounting, with governance and workflow redesign.

Five finance workflows, rebuilt as agent systems. Each one shows where the work breaks today, how the workflow is redesigned around agents, which parts stay deterministic, and where a human still signs. The organisations are illustrative composites drawn from live engagement patterns.

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The five rules every build follows

Governance is engineered into the workflow, not written into a policy memo.

These constraints apply to all five use cases below.

01Deterministic boundary

Arithmetic, thresholds, matching rules and anything reaching a filing are code the agent calls, never model output.

02Human at the authority line

Every checkpoint is anchored to the person who already holds that sign-off. No new approval roles are created.

03Run trace as the deliverable

Every tool call, retry, override and approval is logged as a structured event an auditor can read unaided.

04Author paired with evaluator

No agent verifies its own output. A second agent grades the deliverable, and disagreement escalates to a human.

05Earned autonomy

Each step moves from shadow, to drafting, to threshold review, to sampling, only on evidence. Any step can be moved back.

1
Accounts payable

Invoice to payment, without losing dual control

Multi-unit restaurant franchise operator, 40+ sites

Where the work breaks today
  • An eleven-step manual sequence spanning an invoice capture tool, a vendor portal, the ERP, spreadsheets and several banking sites.
  • Specialists validate and code every invoice by hand; managers route; the CFO approves at the end of a queue that has no visibility.
  • Payment review runs on a spreadsheet and bank reconciliation is manual, so duplicates and vendor bank detail changes are caught late or not at all.
The redesigned workflow
  • Capture, validation and three-way match run continuously as invoices arrive, not in a weekly batch.
  • Coding is proposed from vendor history with a confidence score; only low-confidence or high-value items reach a person.
  • Approval routing is driven by the existing delegation of authority matrix rather than by who is copied on an email.
  • Payment files are assembled by the agent but released only under unchanged dual control.
The agent roster
TaskerExtracts, validates and matches invoice to purchase order and receipt
AnalystInvestigates match failures and proposes coding with a confidence score
OrchestrationRoutes approvals against the delegation matrix and chases named owners
GuardianBlocks duplicates, unverified bank changes and out-of-policy payments
Deterministic · code, not the model
  • Three-way match and tolerance
  • Duplicate detection
  • Delegation of authority thresholds
  • Payment file assembly and totals
The model’s work
  • Coding suggestion from vendor history
  • Exception narrative for the reviewer
  • Chasing messages to approvers
Where the human signs
  • Coding exceptions: Accounting manager, before posting
  • Payment run release: Dual control, unchanged: controller and CFO
  • Vendor bank detail change: Always human, verified out of band. The agent has no path to approve it
Measured on
Touchless invoice rateCost per invoiceDuplicate payments preventedDays to pay against termsExceptions per 1,000 invoices
2
Budgeting & forecasting

From one spreadsheet scenario to parallel options

Community services non-profit, lean finance team

Where the work breaks today
  • Budgeting is sequential and spreadsheet-bound, with data gathered slowly from the accounting system and payroll reports.
  • By the time a single scenario is assembled the assumptions have moved, and there is no capacity to model an alternative.
  • Leadership is asked to approve a number rather than to choose between options.
The redesigned workflow
  • A standard planning brief is issued to every budget owner, so submissions arrive in one shape.
  • Data is ingested continuously and scored for reliability, with weak inputs flagged rather than silently averaged.
  • Two or three scenarios are generated in parallel, each with its assumptions stated and its sensitivity quantified.
  • Leadership spends its time choosing, not waiting.
The agent roster
OrchestrationIssues planning briefs, tracks submissions, chases what is missing
AnalystIngests and scores data, builds parallel scenarios, writes the assumption note
GuardianTests every scenario against reserve floors, restricted fund rules and headcount caps
Deterministic · code, not the model
  • Consolidation arithmetic
  • Payroll cost build
  • Restricted fund and reserve rules
  • Scenario version control
The model’s work
  • The planning brief wording
  • Scenario narrative and trade-offs
  • Sensitivity commentary
Where the human signs
  • Assumption set: Finance Director, before scenarios are built
  • Scenario selection: Leadership team, on the agent’s comparison pack
  • Final budget: Board, unchanged
Measured on
Days to first complete draftScenarios modelled per cycleData reliability scoreForecast accuracy against actualHours spent gathering data
3
Commercial expense planning

Classify spend at the source, not at the deadline

Consumer brands group, multi-market

Where the work breaks today
  • Every forecast cycle opens with a remapping crunch: purchase orders matched by hand to forecast lines across disconnected models.
  • The mapping logic lives in the planner’s head and in chat threads, so it is neither consistent nor transferable.
  • The output is a single budget scenario produced under time pressure, with no room to test alternatives.
The redesigned workflow
  • Classification moves upstream to the moment a purchase order is created, using historical vendor patterns and the line description.
  • Confidence is scored on every classification, so only the ambiguous or high-value cases need a human.
  • A consolidated forecast is maintained continuously rather than rebuilt each cycle, and corporate submission is automated against the template.
  • The planner’s role changes from spreadsheet operator to exception supervisor.
The agent roster
OrchestrationTriggers on purchase order creation and manages the submission calendar
AnalystClassifies spend to forecast line with a confidence score and a stated basis
TaskerMaintains the consolidated forecast and files the corporate submission
Deterministic · code, not the model
  • Mapping table lookup
  • Confidence and value thresholds
  • Forecast aggregation
  • Submission template validation
The model’s work
  • Classification proposal from vendor and description patterns
  • Why this mapping was chosen
  • Variance narrative at review
Where the human signs
  • Low-confidence or high-value classification: Planner, before it enters the forecast
  • New vendor or new spend category: Category owner. The agent cannot create a mapping
  • Funding review: Unchanged, human, at the existing forum
Measured on
Auto-classification rate at sourceDownstream reclassification rateRemap hours per cycleSubmission timelinessExceptions per cycle
4
Revenue & billing operations

One event spine, and only the real anomalies reach a person

Subscription software business, high transaction volume

Where the work breaks today
  • Product, billing and ledger events sit in separate platforms with different clocks, so ordinary timing differences present as breaks.
  • Analysts validate everything daily because nothing distinguishes a latency artefact from a genuine variance.
  • Real revenue leakage hides inside the noise, and customers hear about problems after they have felt them.
The redesigned workflow
  • A canonical event spine normalises product, billing and ledger events onto one identifier and one clock.
  • Daily reconciliation runs automatically, with timing windows encoded so latency is recognised rather than escalated.
  • Multi-state subscription transitions are tracked as typed events rather than inferred from balances.
  • Only genuine anomalies reach a human, and each arrives with a drafted customer briefing.
The agent roster
OrchestrationRuns the daily cycle and holds the dependency between feeds
TaskerReconciles balances and tracks subscription state transitions
GuardianSeparates timing differences from true anomalies and holds release on unresolved variance
AnalystExplains the anomaly and drafts the customer outreach briefing
Deterministic · code, not the model
  • Matching rules and identifiers
  • Timing window logic
  • Revenue recognition schedule
  • Variance tolerance
The model’s work
  • Anomaly explanation with references
  • Customer outreach briefing
  • Trend commentary for the revenue review
Where the human signs
  • Daily reconciliation acceptance: Revenue manager, on the exception list only
  • Customer-facing briefing: Human approval before it leaves the building
  • Unresolved variance above tolerance: Blocks sign-off. The agent cannot write it off
Measured on
Days auto-reconciled without interventionTrue anomaly rate against false breaksTime to detect a genuine breakRevenue leakage identifiedManual validation hours
5
Master data & metadata

Chart of accounts governance in real time, not quarterly

Clinical-stage life sciences company

Where the work breaks today
  • A cross-functional committee meets quarterly to review metadata changes tracked in spreadsheets and email.
  • Approved changes are then applied by hand across the ERP, procurement, planning and HCM systems, in sequence.
  • The lag causes misalignment between systems, and the misalignment surfaces as a delay in the next close.
The redesigned workflow
  • Requests are triaged as they arrive, against a published standard, instead of being batched to a quarterly meeting.
  • Impact across every downstream system and report is assessed before the change is accepted, not after.
  • Approved updates are executed in parallel across systems rather than sequentially.
  • Cross-system alignment is independently verified after execution, and the committee moves from processing changes to setting policy.
The agent roster
AssistantIntakes the request, completes it against the standard and returns it if incomplete
AnalystAssesses downstream impact across systems, reports and hierarchies
OrchestrationExecutes synchronised updates across all connected systems
GuardianVerifies cross-system alignment after execution and blocks integrity breaks
Deterministic · code, not the model
  • Naming and structure standards
  • Hierarchy validation
  • Cross-system alignment test
  • Mapping coverage check
The model’s work
  • Impact narrative for the approver
  • Request classification and routing
  • Policy exception write-up
Where the human signs
  • Every change: Master data domain owner, unchanged authority
  • Structural change to the hierarchy: Controller, with the impact assessment attached
  • Committee: Meets on policy and exceptions, not on processing
Measured on
Request turnaround timeCross-system alignment failuresClose delays attributable to metadataRequests auto-validatedUnmapped accounts at close
Faster is not the point. Defensible is. Every one of these workflows can be built so that an auditor reads the trace unaided, and so the people who sign today still sign tomorrow. That is the difference between an automation and an operating system.

Which of these workflows breaks most often in your finance function?

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