AITHENTIC Finance Use Cases
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- AITHENTIC Finance Use Cases
With governance and workflow redesign
Agentic AI Use Cases in
Finance & Accounting
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.
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.
Deterministic boundary
Arithmetic, thresholds, matching rules and anything reaching a filing are code the agent calls, never model output.
Human at the authority line
Every checkpoint is anchored to the person who already holds that sign-off. No new approval roles are created.
Run trace as the deliverable
Every tool call, retry, override and approval is logged as a structured event an auditor can read unaided.
Author paired with evaluator
No agent verifies its own output. A second agent grades the deliverable, and disagreement escalates to a human.
Earned autonomy
Each step moves from shadow, to drafting, to threshold review, to sampling, only on evidence. Any step can be moved back.
1
The Three Pillars
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
Tasker
Extracts, validates and matches invoice to purchase order and receipt
Analyst
Investigates match failures and proposes coding with a confidence score
Orchestration
Routes approvals against the delegation matrix and chases named owners
Guardian
Blocks 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
•Touchless invoice rate •Cost per invoice •Duplicate payments prevented •Days to pay against terms •Exceptions 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
Orchestration
Issues planning briefs, tracks submissions, chases what is missing
Analyst
Ingests and scores data, builds parallel scenarios, writes the assumption note
Guardian
Tests 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
•Days to first complete draft •Scenarios modelled per cycle •Data reliability score •Forecast accuracy against actual •Hours 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
Orchestration
Triggers on purchase order creation and manages the submission calendar
Analyst
Classifies spend to forecast line with a confidence score and a stated basis
Tasker
Maintains 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
•Auto classification rate at source •Downstream reclassification rate •Remap hours per cycle •Submission timeliness •Exceptions 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
Orchestration
Runs the daily cycle and holds the dependency between feeds
Tasker
Reconciles balances and tracks subscription state transitions
Guardian
Separates timing differences from true anomalies and holds release on unresolved variance
Analyst
Explains 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
•Days auto reconciled without intervention •True anomaly rate against false breaks •Time to detect a genuine break •Revenue leakage identified •Manual 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
Assistant
Intakes the request, completes it against the standard and returns it if incomplete
Analyst
Assesses downstream impact across systems, reports and hierarchies
Orchestration
Executes synchronised updates across all connected systems
Guardian
Verifies 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
•Request turnaround time •Cross system alignment failures •Close delays attributable to metadata •Requests auto validated •Unmapped 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.