Someone on the team has tried an AI tool and the demonstration looks promising. Now the question is whether the business is ready to use it for real work. A subscription and an enthusiastic volunteer do not answer that question.
AI readiness means being able to test a specific workflow with suitable inputs, clear ownership, a baseline, human review, exception handling and acceptance criteria. Assess each condition separately. A small business can be ready for one bounded pilot while remaining unready for a wider rollout.
Photo by Jakub Żerdzicki on Unsplash.
Define the workflow your AI readiness check covers
Write the input, required output and person who uses it. “Help with admin” is too broad. “Prepare a draft summary from approved meeting notes for the project lead to check” is a task you can evaluate.
Complete each checklist item with Yes, Partly or No, then add evidence, an owner and the next action. Do not add the answers into a reassuring overall score. A serious data or review gap should remain visible.
The readiness checklist
Business need: We can explain the problem and why it deserves attention now.
Process: We know the trigger, steps, handoffs and common exceptions.
Baseline: We have examples of current work and a way to measure time, quality and rework.
Data: We know which inputs are needed, where they come from and whether they are suitable for the proposed tool.
Access: We can restrict access and actions to what the pilot needs.
Ownership: One person owns the pilot and can stop it.
Human review: A named reviewer has the knowledge, source information and time to check outputs.
Exceptions: We know how to route uncertain, incomplete or failed cases.
Adoption: The people doing the work understand the proposed change and can report problems.
Evaluation: We have acceptance criteria and stop conditions agreed before testing.
Recovery: We can return to the current process if the pilot fails.
Operating effort: We have allowed for checking, support, changed inputs and maintenance.
Separate gaps from stop conditions
A missing baseline can become an action to collect representative examples. An unclear permission to use the data is a reason to pause that use of the data. A reviewer with no available time means the proposed review control does not yet exist.
Agree which answers must be resolved before testing. Do not let a high expected benefit cancel out a missing safeguard.
What the checklist looks like in practice
In an illustrative consultancy, a project lead wants draft meeting actions. The task is clear, but the notes vary and nobody has agreed who checks the result. The first actions are to choose suitable meeting examples, define an action format and assign a reviewer.
The pilot can then test whether the draft captures the agreed actions accurately and reduces total preparation time after checking. It should not automatically distribute unreviewed commitments to clients.
Use the results to plan the next step
Group the actions into preparation, pilot design and later rollout work. Resolve preparation gaps before buying more tools. Keep the checklist with the pilot plan and revisit it when the use case changes.
The NIST AI Risk Management Framework is a voluntary resource for risk and trustworthiness considerations. This checklist is a practical discussion aid, not a certification or a legal compliance assessment.
AI readiness vs AI adoption: what changes?
Readiness asks whether the conditions for a useful test exist. Adoption asks whether people can use and maintain the changed workflow in real work. A pilot owner and approved test data may establish readiness, but ongoing support, review capacity and exception ownership still need to be settled before expansion.
Begin with our process-mapping guide if the current workflow is unclear. To turn the checklist into a manageable sequence of work, plan an AI implementation roadmap with Hili Consulting.
