The best first AI project is usually a task whose value and failure can both be measured. For a finance leader, that means identifying a recurring problem, establishing what it costs today and deciding which errors the business can tolerate before choosing a tool.
“Improve month-end close” is too broad to commission. “Prepare supporting evidence for the reconciliation exceptions that delay close” gives a team something to investigate. It names an activity, leaves the accounting judgement with the responsible person and creates a way to compare the proposed change with the current process.
Singapore's adoption figures make this distinction useful. In IMDA's 2025 Digital Economy Report, 40% of approximately 500 surveyed AI-using firms reported using AI in finance or accounting. This was a May–June 2025 sample of existing users, rather than all Singapore businesses. It shows finance is already an application area. It does not identify which workflow will repay investment in your organisation.
Start where the work accumulates
Ask a process owner to walk through a recent completed cycle. Follow the work from receipt to resolution: which documents arrived, what someone checked, where it waited and what caused it to return. Examine ordinary cases alongside difficult ones. A demonstration built from unusually tidy records will miss much of the operating burden.
Distinguish elapsed time from handling time. An invoice may wait three days for a missing purchase order while requiring only five minutes of staff attention. Faster extraction will not remove those three days. Supplier discipline, a clearer purchasing process or an earlier reminder may have greater value.
Record where uncertainty enters. AI may help interpret varied documents, assemble evidence or draft an explanation. Rules and conventional software are often appropriate for exact calculations, known identifiers and enforceable approval limits. A useful design can combine these approaches: extract information from a document, validate it against authoritative records, apply matching rules and send unresolved cases to a person.
The question to investigate is whether interpretation is the bottleneck. If most delay comes from unavailable data or an absent approver, fix that dependency before paying to automate it.
Compare a few candidates on the same basis
Choose three or four workflows and complete the same evidence sheet for each. Do not let a compelling demonstration substitute for the comparison.
| Candidate | Potential contribution from AI | A simpler alternative to test | Outcome to measure |
|---|---|---|---|
| Invoice intake | Extract fields from varied documents and flag missing evidence | Standard submission form or existing accounting-system feature | Staff minutes per correctly prepared invoice |
| Reconciliation exceptions | Assemble relevant records and propose explanations | Better identifiers, matching rules and data-quality checks | Time to resolve an exception correctly |
| Management-report preparation | Draft commentary tied to approved figures | Standard report templates and automated data refresh | Review effort, unsupported statements and reporting lead time |
| Supplier-evidence collection | Classify documents and identify missing items | Clear evidence requests, reminders and an owner | Complete evidence packs received by the deadline |
These are candidates for investigation, not established savings opportunities. The right answer depends on the existing systems, document mix and cost of a mistake. Check what your current software already includes. Purchasing a second tool can add an integration and support burden to a problem already partly solved.
For a regional business, narrow the first comparison to a defined entity and process. A Singapore pilot using English documents and one accounting system will say little about a rollout with different document formats, local working practices or access arrangements unless those conditions are represented in the test.
Apply the conditions that a high score cannot outweigh
A weighted score can make an attractive project look precise while concealing a serious gap. Use a small number of conditions first.
There must be an accountable process owner who can change the workflow. The team must have a lawful, approved way to obtain representative data. It must be possible to judge the output against a reliable reference. Someone must own exceptions, and the business must be able to continue when the system is unavailable.
If one condition is absent, record the work needed to resolve it. A valuable project can wait while access or ownership is established. Treat that as a dependency rather than improving the score with optimistic assumptions.
For systems that take actions, define their permitted authority early. Singapore's Model AI Governance Framework for Agentic AI discusses human accountability, approval checkpoints, testing and controlled tool access. This is guidance, not a universal legal approval for a proposed use. It provides practical questions for scoping a pilot that can prepare an action without being allowed to execute it.
A selection worksheet for the decision meeting
Complete this with the process owner and a person who performs the work. Use a recent operating period that includes the conditions you expect the pilot to handle. The discovery pack reconciles opening backlog and new arrivals to completions, authorised withdrawals and closing backlog. Keep rejected, transferred and unfinished work visible so an attractive accepted subset cannot determine the ranking.
| Field | What to record |
|---|---|
| Process boundary | The event that starts the task, the accepted end state and the entity involved |
| Current workload | Offered cases, completions, withdrawals and backlog; transferred work, handling times and peaks |
| Current quality | Corrections, missed exceptions and other consequential errors, with definitions |
| Main constraint | Interpretation, missing information, coordination, system access or approval |
| Proposed change | The precise step the system will perform and the work people retain |
| Existing alternative | Process repair, current software capability, deterministic automation or another supplier |
| Evidence available | Representative records, expected answers, reviewer and access approval |
| Authority | What the system may read, prepare, change or commit; required approval points |
| Operating cost | Implementation, integration, licences, human review, maintenance and support |
| Value | Capacity released, spending actually avoided, or another separately justified benefit |
| Success and stop conditions | Agreed quality, effort and cost thresholds; errors that require suspension |
| Accountable owner | Person accepting the result and person owning ongoing operation |
Then compare the candidates in a short decision table: expected value, confidence in the evidence, effort to resolve dependencies and consequence of failure. Explain the ranking in words. The purpose is to expose judgement, rather than hide it inside a numerical total.
Fund the next question you need answered
The first approval should buy evidence proportionate to the uncertainty. If the main unknown is extraction quality, test representative documents. If it is integration, prove the required read and write operations in a controlled environment. If the economics depend on reducing review time, measure that time with the intended users.
Agree the decision before the test starts: proceed within the same scope, change the design, or stop. Include the cost of operating the process after the experiment. The automation cost worksheet separates useful staff capacity from savings that actually reduce spending.
Bring a completed selection sheet for each candidate to the next investment discussion. Approve the smallest test that can resolve the leading candidate's largest uncertainty. A decision to use an existing feature, repair the process or abandon an uneconomic proposal is a useful outcome of the same exercise.
Read our editorial principles for evidence, contribution and correction standards.