Prepare your organisation for enterprise AI.

Bring the workflow owner, a source owner and the people responsible for technology and risk into the same discussion. Work through one specific use case. Broad answers about “AI transformation” will not resolve the decisions needed to build a useful first release.

For each item, identify the supporting record and the person who accepts it. Leave unresolved items unchecked and assign a follow-up action. The checklist runs in your browser and does not send your answers to Aliph. Use your browser’s print dialog to save a working copy.

Describe the task, intended users, inputs and outputs. Write down what the system should not do. Agree how the result will be reviewed and what happens when a source is missing or an answer is uncertain.

Choose a scope small enough to evaluate with representative tasks. Include realistic exceptions from the current process rather than testing only the ideal path. Keep a baseline of the time, effort or quality of today’s workflow.

Record which sources may be used, who owns them and how access is checked. Define the requirements for model requests, stored content, logs and backups. Identify the people responsible for maintaining the application and responding to failures.

Confirm the deployment arrangement against those requirements. Prepare a handover that includes evaluation results, known limitations, support procedures and the conditions for making a change.

For the source-readiness item, an answer could name the approved policy library, its accountable owner, the user groups allowed to search it and the process for withdrawing an outdated document. A statement such as “our documents are available” leaves these decisions unresolved.

For evaluation, record representative questions, the expected supporting sources, who reviews the answers and the result that would prevent release. Keep those records alongside the checklist so the next conversation can focus on evidence rather than recollection.

Prepare a short brief before the discussion. Include the intended task, current process, example inputs, desired output and known constraints. Invite people who can resolve source ownership, approve the workflow and maintain the implementation. If a decision-maker cannot attend, make the follow-up route explicit.

Walk through one representative request from start to finish. Ask what information is available, which permission is checked, who reviews the result and what happens when the answer cannot be supported. Capture decisions in the brief and assign unresolved questions to an owner. The goal is a shared implementation scope.

End by separating decisions that are ready from questions that prevent a useful evaluation. A source-selection task may be small but essential. A broad discussion about future features can wait until the initial task has enough definition to assess.

As the work progresses, collect the records a reviewer will need: the use-case brief, source register, access model, evaluation cases, results and operating responsibilities. Include known limitations and the fallback process. Link these records from the checklist so checked items remain understandable after the meeting.

Use a small number of clear acceptance decisions. Confirm that the intended task is useful, the evaluated scope is appropriate and the responsible teams understand the support and change process. If an item is accepted with a limitation, state the limitation and who owns the remaining work.

The next release may add another source, language, tool or user group. Record which of these changes require another review and which evaluation cases should be repeated. A readiness checklist becomes more useful when it supports a continuing decision process, rather than disappearing after the first approval.

A low completion count can be useful: it shows which decisions need attention before development. A complete checklist still needs review of the quality of its answers. Use it as a conversation tool, not a numerical risk assessment.

For practical help, explore Aliph AI services. For broader lifecycle risk considerations, refer to the voluntary NIST AI Risk Management Framework.