A finance team wants to test an assistant that extracts invoice details and prepares a draft record. Staff currently enter the information manually, resolve missing purchase-order references, and send exceptions to another team. A demonstration suggests that extraction could save time, but the team still needs to know whether those savings survive checking, correction, and integration with the finance system.
An AI pilot is a bounded trial designed to answer that kind of decision. It should establish whether a proposed application is useful enough, reliable enough, and practical enough to justify a next step. That step may be a limited deployment, further investigation, a narrower use case, or stopping the proposal.
The pilot does not need to reproduce every production condition. It does need to identify the assumptions most likely to change the decision and collect credible evidence about them. Its scope, evaluation method, and limits should be agreed before results arrive.
1. State the Decision and the Intended Benefit
Begin with the work and the proposed change. For the finance team, the goal might be to reduce the effort required to produce a correct draft invoice record. The assistant would extract fields, while staff would review them and the existing finance system would enforce payment controls. Automatic payment would remain outside this pilot’s scope.
Define who will use the result, who could be affected by errors, and which parts of the workflow are included. A pilot involving invoice preparation should not quietly expand into supplier approval or payment authorisation because the model can generate those recommendations too.
Assign a business owner who can decide whether the benefit is worthwhile and an operational owner who understands the work. Technical, security, privacy, and relevant risk specialists should help establish constraints. Their involvement should resolve specific questions, such as permitted data use and access to the finance system, rather than add an identical approval process to every experiment.
2. Establish the Baseline and the Alternatives
Measure the existing process before comparing it with the assistant. Include entry time, review time, correction rates, exception handling, and delays between teams. Agree what counts as a correct draft so that reviewers apply the same standard to both methods.
The comparison should include a plausible alternative where one exists. Better import templates, conventional document extraction, or improved supplier instructions might solve part of the problem more cheaply. A useful pilot can reveal that a combination of simpler changes and AI assistance is preferable to a larger replacement.
Do not assume that every minute removed from entry work becomes a financial saving. The team may use released capacity to reduce a backlog or improve review rather than reduce staffing. State the intended benefit in terms the organisation can later verify.
The baseline also helps separate novelty from lasting usefulness. Early enthusiasm may increase attention and effort, while unfamiliarity may initially slow users down. Record those conditions so the team can interpret changes in performance over the trial.
3. Choose a Bounded but Representative Scope
A first trial might cover invoices from a defined set of suppliers and require review before any record is accepted. Choose the scope because it contains important features of the intended work: different layouts, multi-page documents, missing references, and credit notes, where relevant.
A narrow pilot can be valuable when it answers a specific uncertainty. It becomes misleading when its results are presented as evidence for conditions it excluded. Strong performance on standard invoices from established suppliers does not establish readiness for every language, document quality, or transaction type.
Document exclusions and determine how excluded cases are recognised. If staff must read every invoice to decide whether it qualifies, that screening effort belongs in the cost of the proposed process. If the system cannot reliably identify an unsupported case, its scope restriction may be difficult to enforce in practice.
4. Prepare Evaluation Data and Acceptance Criteria
Use examples that represent the agreed scope, with permission to process them in the chosen environment. Finance specialists should establish the expected fields and record genuine ambiguity. An invoice that cannot be resolved without consulting a buyer should not be treated as if the correct answer were obvious from the document alone.
Keep development examples separate from the final evaluation set. The team can use development cases to improve prompts and handling rules, then assess the resulting version on held-back cases. Check for duplicates or closely related records across the sets, since these can make the final assessment less independent than it appears.
Choose measures that match the decision. Field correctness matters, but a minor formatting difference and an incorrect payment amount have different consequences. Measure the errors that affect review effort, financial exposure, or downstream handling. Record whether staff detect and correct them before acceptance.
Sample size should reflect the uncertainty and the importance of the decision. A small trial can reveal obvious defects but cannot establish that rare serious failures are absent. Report counts as well as percentages and use suitable statistical advice when estimates will support consequential decisions. There is no universal number of invoices that makes every pilot conclusive.
5. Test the Human Workflow and the Necessary Integration
The review interface should let staff compare extracted fields with the relevant source. It should make missing or unresolved information visible and allow correction without awkward workarounds. Measure whether reviewers catch consequential mistakes and how much effort that requires.
Reviewers need clear authority. If an invoice is suspicious or incomplete, they should be able to reject the proposal, obtain further information, or return it to the existing process. The trial should also check apparently routine outputs; reviewing only cases the model marks uncertain can miss confident errors.
Test the integrations that could invalidate the business case. The finance system may require valid supplier identifiers, current purchase-order data, or a supported import format. The assistant may need to wait for a nightly update. These constraints affect whether the proposed time saving is available when staff need it.
The legacy integration guide explains data validation, transaction handling, and recovery in more detail. A pilot can test a limited path through those concerns without attempting a complete enterprise rollout.
6. Run the Trial in Stages
Begin with offline evaluation where appropriate. Process authorised historical examples without affecting live records, inspect failures, and resolve major defects. If the results justify continuing, a shadow stage can generate proposals alongside the current process without making changes.
A supervised live stage then tests how people actually use the assistant. Keep the population and permissions bounded, maintain a fallback, and record which version is running. Significant changes during the trial should be documented and may require a fresh comparison rather than mixing results from materially different systems.
Choose duration around the variation that matters. Month-end activity may be important for invoice work, while a seasonal forecast needs a different evaluation approach. A longer pilot does not guarantee that drift or rare events will appear. Historical replay and deliberately constructed failure cases can supplement live observation, provided the report distinguishes them from naturally observed results.
Where practical, compare similar work or allocate cases between the current and assisted process using a defensible method. Differences in supplier mix, document difficulty, or reviewer experience can otherwise explain an apparent improvement. If a controlled comparison is impractical, state that limitation and avoid stronger causal claims than the evidence supports.
7. Measure the Complete Cost and Define Intervention Rules
Track model or service usage, document processing, integration effort, reviewer time, correction, training, and support. Count retries and failed attempts as well as accepted records. Cost per correctly accepted draft is more useful than a price per model call when the goal is to improve completed work.
Also examine where work moves. Faster entry may increase time spent resolving supplier mismatches. Staff may process more records while another team receives more exceptions. The business case needs the combined effect, with one-off implementation costs distinguished from recurring operating effort.
Before launch, specify conditions that pause the trial or block expansion. Examples include an unauthorised disclosure, records accepted without required review, unacceptable critical-field errors, or a backlog beyond available support capacity. The thresholds should reflect the use case and the team’s ability to detect harm.
Assign someone who can restrict access, stop writes, and organise recovery. NIST’s AI Risk Management Framework includes managing risks after deployment and establishing mechanisms to supersede or deactivate systems that produce outcomes inconsistent with intended use. For this pilot, that principle needs a concrete procedure for outstanding drafts and any records already accepted.
8. Write the Decision the Evidence Supports
The final report should connect the original question to the findings. State which invoices were included, what baseline was used, which system version was evaluated, and what improved. Report important errors, reviewer effort, recurring cost, integration limits, and conditions that were not tested.
Separate evidence from proposals for fixing the remaining gaps. If the assistant missed critical details, an untested plan to improve the prompt does not count as a demonstrated improvement. The next decision may be to test that change, narrow the scope, or continue using the existing process.
Production approval needs named ownership, support arrangements, monitoring, and a workable fallback. Retaining operational control becomes especially important when growing volume makes manual recovery harder. A successful trial at limited volume should not automatically authorise a larger level of dependence.
A well-run pilot leaves the organisation with a clearer decision and a record that others can inspect. It shows where the application is useful, the conditions under which that finding holds, and what would need to change before broader use. That evidence gives both expansion and restraint a practical basis.
