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Responsible AI implementation guide

AI pilot governance checklist

A useful pilot tests a defined business task under controlled conditions. This checklist gives leaders and technical teams a shared record of the data, decisions, safeguards and evidence needed before wider use.

By

1. Name the task, user and intended result

A broad goal such as use AI in customer service is difficult to test. Define one task, the people who perform it today and the result the organization expects. A pilot might draft an internal summary for an employee to review or classify incoming requests into an existing queue.

Write down what remains outside the pilot. Exclude sensitive data, automated decisions or external communications unless the organization has deliberately approved them. Name the executive sponsor, business owner, technical owner and the person who can stop the trial.

2. Classify the effect of a wrong result

Consider who may be affected and what happens if the system produces inaccurate, incomplete, biased or inappropriate output. Review legal, contractual, privacy, security, employment and safety concerns with the people responsible for those areas.

The NIST AI Risk Management Framework organizes AI risk work around govern, map, measure and manage. It can provide a common structure without prescribing a particular product.

Use the risk review to set pilot limits. A low-impact internal drafting aid can allow a different test design from a system that influences employment, credit, healthcare, safety or access to a service.

3. Approve the data path

  • List the data the pilot receives, creates, retrieves and stores.
  • Identify confidential, personal, regulated and customer-owned information.
  • Confirm that the organization has authority to use the data for this purpose.
  • Review vendor terms for retention, model training, subprocessors and deletion.
  • Limit access by role and keep test and production information separated.
  • Set retention and disposal rules for prompts, outputs, logs and evaluation records.
  • Plan how a person can correct or remove inappropriate source material.

Use representative test data when possible. If production data is necessary, approve the minimum required fields and document the controls protecting them.

4. Review the service and its dependencies

Record the model, application, hosting arrangement, integrations and administrative accounts involved. Review identity controls, encryption options, logging, service availability, usage limits, content filters and the provider's change process.

Understand what the application adds around the model. Retrieval sources, system instructions, tools and workflow automation can affect results as much as the model selection. Keep secrets out of prompts and source documents. Restrict external actions until they have explicit authorization and testing.

Prepare for service changes or loss. The business should know how to disable access, export necessary records and continue the underlying task manually.

5. Define human review and escalation

Name who reviews each output and what that review must check. A person who lacks the relevant subject knowledge may be unable to detect a convincing error.

  • Which outputs need approval before use or release?
  • What source material should the reviewer be able to inspect?
  • How should uncertainty, missing evidence or conflicting information be handled?
  • Which topics or actions require specialist review?
  • How can a user report a harmful, incorrect or unexpected result?
  • Who investigates incidents and decides whether the pilot continues?

Show users where AI contributes to the workflow. Train them on the approved use, data restrictions and escalation path before granting access.

6. Create an evaluation set and baseline

Collect a representative sample of tasks with expected outcomes or a reliable way to judge quality. Include routine cases, ambiguous inputs and cases that should be refused or escalated. Protect the evaluation set from being casually reused as prompt material.

Compare the pilot with the current process. Useful measures may include correction rate, time spent reviewing, completion time, consistency, escalation rate, user experience and operating cost. Choose measures that match the stated outcome.

Record model and configuration details with the results. Retest after meaningful changes to models, instructions, retrieval sources, tools or workflow.

7. Set operating limits and stop conditions

Limit the pilot by users, data, time, transaction volume and available actions. Set spending alerts and technical rate limits where the service supports them. Monitor access, errors, unusual use and vendor notices.

State the conditions that pause the pilot. Examples include unauthorized data exposure, an unacceptable error pattern, a control failure, a material vendor change or repeated use outside the approved scope. The stop procedure should revoke access, preserve relevant evidence and notify the named owners.

End with a decision

Review the evidence before expanding use.

A pilot can lead to adoption, another bounded test, redesign or a decision to stop. Record the reasons and remaining uncertainty.

  1. Compare results

    Review quality, risk, user effort, cost and operational findings against the baseline.

  2. Resolve open findings

    Assign controls, workflow changes and vendor questions before wider use.

  3. Approve the new scope

    Name the users, data, actions and owners allowed in the next stage.

  4. Set a review date

    Revisit performance, incidents, changes and whether the use still serves its purpose.

From idea to controlled test

Make the first implementation small enough to understand.

Wise IT can help select a practical use case, review readiness, design controls and lead a pilot with clear ownership and evaluation.

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