Picking the first finance AI use case is less about ambition and more about evidence. The process you hand to AI agents first will set expectations with the board, the auditors and the team, so it needs to deliver a visible result without creating new control risk. This article sets out a simple scoring approach that ranks candidate processes on four factors: volume, rules, data quality and risk. It also covers how to weight them, which processes look attractive but should wait, and how to turn the top score into a scoped, governed pilot.
1. Why the first finance AI use case sets the tone
Most finance teams already hold a long list of processes they would like to automate. Ideas are rarely in short supply. The hard part is choosing one, under pressure, when every process owner believes theirs is the obvious candidate. The CFO wants a visible result, the controller wants nothing that weakens controls, and IT wants something that fits the current ERP without a long integration project. A first finance AI use case that stalls in month three colours every later conversation, while one that runs quietly and survives audit review earns permission for the next.
That is why ai use case prioritisation deserves more structure than a workshop vote. Votes reward whoever presents best. A scoring approach forces the same questions onto every candidate: how often does this happen, how clear are the rules, how clean is the data it depends on, and what goes wrong if the agent gets it wrong. Answering those questions in writing also leaves a record you can show the audit committee when they ask why you started where you did, and it gives disappointed process owners a fair reason for waiting.
2. What makes a finance process a good candidate for AI agents?
Good candidates share a recognisable shape, and spotting it is the first test of finance automation readiness. They involve a steady flow of similar transactions, a documented policy that experienced staff apply consistently, inputs that already sit in the ERP or arrive as structured documents, and a failure mode that can be caught before money leaves the business. Invoice verification against purchase orders, bank statement matching, customer remittance matching and expense claim review all tend to fit. Each has a clear start and end, a known exception list and a person who already reviews the edge cases.
Poor candidates usually fail on one of those points rather than all of them. Strategic forecasting has plenty of data but no fixed rules. Statutory tax filing has rules but carries a high consequence for a single error. Payments to new beneficiaries combine both problems. None of these is off limits forever, but they make weak first moves because success is hard to define and failure is expensive. When people ask which finance process to automate first, the honest answer is usually the dull one with the longest queue and the most patient reviewer.
3. Building a process scoring matrix: the four factors
The matrix scores each candidate from one to five on four factors. Volume measures how many transactions or cases run through the process each month and how much staff time they absorb. Rules measures how much of the decision can be written down: a high score means an experienced clerk could explain the logic on a single page, with a short list of known exceptions. Data quality measures whether the inputs are complete, consistent and reachable by the agent through the ERP or a governed data layer.
Risk is scored the other way round, so that a high score means low consequence. Ask what happens if the agent makes a wrong call and nobody notices for a week. If the answer is a misposted entry that the next reconciliation will catch, risk scores well. If it is a payment to the wrong supplier, it scores poorly unless a person approves before release. Score risk after you have designed the control, not before, because a well placed human approval can turn an uncomfortable process into an acceptable one.
Weighting is where teams overthink. Equal weights work for a first pass. If your organisation has just been through a difficult audit or a control failure, give risk double weight. If capacity is the pressing issue, because the team is losing people faster than it can hire, give volume extra weight. Resist adding more factors: once strategic alignment, executive sponsorship and innovation value join the list, the matrix becomes a negotiation rather than an assessment, and the loudest sponsor wins again.
4. Scoring data quality and risk without wishful thinking
Data quality is the factor most often scored on optimism. Process owners describe the data as it should be, not as it is. A proper data quality assessment means pulling a recent month of real transactions and checking them: how many invoices lack a valid purchase order reference, how many supplier records share bank details, how many customer payments arrive with no remittance advice at all. Working through an ERP data readiness checklist before scoring turns a guess into evidence, and it often reorders the shortlist in ways nobody expected.
Risk deserves the same discipline. Map where the agent would act, where it would only recommend and where a person must approve. Then decide the escalation thresholds for exceptions, by value, by counterparty and by type of mismatch, before anyone writes a line of configuration. A process with sensible thresholds and a clear audit trail can score well on risk even when the amounts are large. A process where nobody can say who signs off an exception should score low until that question is answered, whatever the vendor demonstration suggests.
5. A worked example: four candidates, one clear winner
Take a distribution group with entities in Dubai and Riyadh, running one ERP for the operating companies and spreadsheets for the holding entity. The finance director shortlists four candidates: invoice checking in accounts payable, intercompany reconciliation, cash flow forecasting and month-end accruals. The team scores each in a short working session, using a sample of last month's transactions rather than memory, with the controller, an AP supervisor and the ERP administrator in the room to challenge any score that sounds too generous.
Invoice checking scores high on volume and rules, because the three-way match logic is already documented, and reasonably well on risk because payments still pass an approval step. Data quality is the weak spot: some Riyadh suppliers are duplicated. Intercompany scores well on rules but low on volume and data, because the holding entity sits outside the ERP. Forecasting scores poorly on rules, and accruals score moderately everywhere. The group starts with AP invoice verification, makes a supplier master clean-up a precondition, and parks intercompany. The matrix did not pick the biggest headline saving; it picked the process most likely to work in its first quarter.
6. Processes that look attractive but should wait
Some candidates score badly for good reasons, and it helps to say so plainly. The full month-end close is a frequent request, but it is a chain of processes rather than one, and handing it to agents as a first project means solving data, sequencing and approvals at the same time. Pick one link in the chain instead, such as bank reconciliation or accrual preparation, and let the close improve as each link is automated. Board reporting and strategic forecasting belong later, once trust in agent output exists.
Be wary too of processes chosen because a tool is already licensed. A capability bought for one purpose often gets pointed at whatever process its sponsor owns, regardless of how it scores. The opposite mistake is just as common: rejecting a strong candidate because it seems too simple to impress. A high-volume, rules-heavy process that frees a team from repetitive checking is a perfectly good finance ai pilot. Impressive can come second, once the operating model around agents has been proven in daily use.
7. Turning the top score into a governed pilot
A score is a starting point, not a business case. Once a process comes out on top as your first finance AI use case, write a one-page scope: the transactions in and out of scope, the systems the agent will read from and write to, the decisions it may take alone, the ones it must escalate and the measures you will report each week. Agree the baseline before go-live, using the same sample you scored with, so that improvement is visible against real figures rather than recollection.
Governance should be designed into the pilot from the first day. At AITHENTIC, every agent has a defined role, decision rights, escalation logic and a full audit trail, and people approve exceptions; that pattern is worth copying whoever builds your agents. Accounts payable is a common first choice because it scores well on all four factors, and it is where AITHENTIC's published result of 70%+ touchless accounts payable with AED 1M+ leakage saved sits. If you want an outside view of where to begin, a short AI assessment can test your shortlist before you commit budget.
Key challenges
- Every process owner believes their process is the obvious first candidate, so selection turns political.
- Data quality is scored on how the data should look rather than on a real sample of transactions.
- Risk is judged before controls are designed, which rules out processes that would be safe with a human approval step.
- Large multi-step processes such as the full month-end close are chosen as first projects and stall.
- Tools that are already licensed drive the choice instead of the process score.
The framework
Use a four-factor scoring model run as a short, evidence-based exercise. Each candidate process is scored from one to five on volume, rules, data quality and risk, where a high risk score means low consequence once controls are in place. Scores rest on a recent month of real transactions and are reviewed together by the process owner, the controller and an IT or data lead. Weights start equal and change only for a documented reason, such as recent control findings or capacity pressure. The top-scoring process is then checked for dependencies, scoped on one page with decision rights and escalation thresholds, and baselined before the pilot begins. The matrix is rerun after each pilot so the next choice uses updated evidence.
How to implement it
- Shortlist four to six candidate processes with their owners, keeping only those with a clear start, end and exception list.
- Pull a recent month of real transactions for each candidate and test volume, rule coverage and data completeness against it.
- Design approvals and escalation thresholds for each candidate, then score all four factors from one to five.
- Check the top-scoring process for dependencies such as supplier master clean-up or data held outside the ERP, and resolve them first.
- Write a one-page scope covering systems, decision rights, escalations and weekly measures, and record the baseline before go-live.
- Rerun the matrix after the pilot to choose the next process on fresh evidence.
Frequently asked questions
What is the best first finance process to automate with AI?
For most teams, the best first finance AI use case is a high-volume, rules-based process with reachable data and a control point before cash moves. Accounts payable invoice verification, bank reconciliation and cash application usually fit. The right answer for your organisation depends on how those factors score against your own transaction data, not on what worked somewhere else.
How do you prioritise AI use cases in finance?
Score each candidate process from one to five on volume, rules, data quality and risk, using a recent month of real transactions as evidence. Add the scores, weighting risk more heavily if controls are under scrutiny. Then check the top candidate for hidden dependencies, such as poor master data, before committing. Keep the factors few so the exercise stays an assessment.
Should month-end close be the first AI agent project?
Usually not. Month-end close is a chain of separate processes with different data sources, owners and approvals, so treating it as one first project spreads effort too thin. A better approach is to automate a single link, such as bank reconciliation or accruals, prove it works, then add the next. The close shortens as each link becomes reliable.
What data do you need before starting a finance AI pilot?
You need complete, consistent and accessible inputs for the chosen process: clean master data, transaction history the agent can read, and the documents it will check, such as invoices, purchase orders or remittances. Test this on a real sample before scoring. If key data sits in spreadsheets outside the ERP, bring it into a governed data layer first or choose a different first finance AI use case.
