A convincing AI demonstration can get a project moving. Before your team relies on it, you need to see what happens with the records it will use, the decisions people must review and the systems it can change.
Agree that evidence before the final presentation. A useful acceptance plan gives the business owner and developer the same way to judge the work: which tasks must succeed, which actions need approval and what happens when the workflow cannot continue.
This is a practical purchase decision. It helps you commission an implementation, review a proposed first release and understand the responsibilities that continue after launch.
Describe the complete job
Start with a task someone can recognize. “Prepare a draft response to a service request using the current policy and the customer's approved account information” is specific enough to inspect. “An AI agent that handles operations” leaves too many decisions open.
Write down the trigger, the information the software can use, the proposed output and the destination. Identify every point at which it may create a record, send a message or change a business system. Name the person who can approve those actions.
The acceptance plan should follow that entire journey. A well-written draft can still be attached to the wrong account. A correct recommendation can still be sent before anyone approves it. Both belong in the review.
If the task itself is still uncertain, start with the first-project decision guide. Once the task is clear, AI implementation services connect the application, its information and the systems around it.
Choose cases that reflect the work
Ask the developer and the people doing the job to assemble a small, versioned set of representative cases. Include routine work, awkward exceptions and situations where proceeding would be the wrong result.
For each case, agree what a reviewer needs to observe. Depending on the task, that could include the correct account, a required field, a current source, an allowed system action or a clear request for missing information. Define which errors block release and which require a recorded follow-up decision.
NIST's voluntary AI Risk Management Framework supports testing against the intended use and deployment conditions, with documented measures and limitations. A public model benchmark can inform a technology choice; your acceptance cases still need to represent your application.
Where generated answers depend on business documents, use the more detailed knowledge-assistant evaluation guide for answer quality, sources and access. Then extend the review to what happens after the answer is produced.
Make approval visible in the workflow
“A human stays in control” needs a concrete interface and a concrete rule. Show the reviewer the action, destination and relevant details before approval. Specify what happens if those details change, the approval expires or the reviewer declines.
The connected system should enforce who may perform the action. OWASP's Excessive Agency guidance recommends limiting available functions and permissions, requiring human approval for high-impact actions and enforcing authorization outside the model's own judgment.
Planning example: An AI workflow drafts a reply to a customer request. The proposed first release allows a staff member to review and send that reply. Its acceptance cases include:
- A complete request produces a draft associated with the correct customer.
- Missing information produces a question or review task before the reply can proceed.
- A user outside the account's permitted scope cannot retrieve its information.
- A draft stays unsent until the designated reviewer approves the actual recipient and content.
- Changing the recipient after approval requires another review.
- A failed send produces an actionable status; recovery does not quietly create a duplicate message.
These are proposed checks for a fictional workflow. The implementation team would agree the exact rules, test environment and expected results for the real project.
Ask for an acceptance record you can use
A recorded demonstration is helpful. A short acceptance record makes the result easier to review again when something changes.
Ask for these fields with the first implementation proposal:
- Task and version: the application, model, configuration and integrations being reviewed.
- Case and input: the scenario, representative data and starting permissions.
- Expected behavior: the useful output, permitted action and required approval or stop.
- Observed result: what actually happened, with enough evidence to inspect it.
- Decision: accepted, needs repair or deferred, with the person making that decision.
- Follow-up: the unresolved issue, responsible person and next review point.
Use sanitized records during planning. The agreed data arrangements should determine when and how real information enters the project.
Butler Agency's owned software demonstration offers a simple example of making source handling and stop behavior visible. It uses fixed rules. An AI implementation needs its own evidence for the actual workflow you are buying.
Agree the operating handover
Before launch, identify who can pause the workflow, handle a failed task, inspect the relevant records and authorize a change. Agree what gets logged, who can see it and how long it is retained. Include the vendor accounts, licensed services and access your team will need to operate or transfer the system.
Plan which checks run again when the model, instructions, sources, tools or permissions change. OWASP's AI Agent Security guidance describes testing controls before production and after material changes. The practical question for your proposal is who performs that work and what would stop an updated release.
Bring one intended workflow, its current tools and a few sanitized examples. We can discuss the implementation, the evidence you need to accept it and the support arrangement that fits your team. Start a project conversation.