A checklist for adopting AI tools at work
Choose a workflow, prepare data, test the work and assign ownership. Use this checklist to decide what is ready to expand.
Buying licenses does not complete AI adoption. Before expanding use, a company needs to know which work it wants to improve, which information may enter the tool and how an acceptable delivery will be recognized. Without these answers, enthusiasm, learning and actual results are difficult to separate.
This checklist applies to agent-enabled editors, assistants and API integrations. Identify the model separately from the product. The voluntary NIST framework and OWASP’s excessive-agency reference help organize evaluation without replacing checks of the selected environment.
1. Is there a defined workflow and owner?
Choose frequent, bounded work, such as preparing an initial report or fixing a known class of defects. Identify who receives the delivery and what must be correct. Improving productivity is a broad objective, not a task that can be compared before and after the change.
Record the current method, including preparation and review time. The baseline need not be perfect, but it should represent actual work. If the process changes on every run, record that uncertainty before attributing a difference to AI. A meaningful comparison needs a reasonably understood starting point.
2. Are data and access ready?
Identify source material, information excluded from context and the authorized environment. Confirm the account, model and necessary connectors. Do not treat a personal configuration as the company standard without checking whether other team members receive the same settings and conditions.
Prepare synthetic or reduced examples for the trial. When real information is essential, record why it is needed and the applicable conditions. A tool available on a computer does not need every item the operator can access. Provide the material required by the task rather than everything conveniently nearby.
3. Does the trial include failure?
Test an ordinary request, a difficult one and an incomplete one. Include an unavailable service or an empty response. Observe whether the tool recognizes the issue, preserves valid work and explains the next step. A confident answer produced from missing information can be more operationally troublesome than an explicit error.
Ask someone who did not run the trial to review the result. They should understand the criteria and evidence without an enthusiastic presentation. For code, review changes and behavior. For text, check sources and claims. Record disagreement instead of hiding it behind an average score.
4. Does the calculation include review and rejected work?
Record observed expense, attempts, intervention time and rejected deliveries. Compare similar tasks. Faster first responses do not demonstrate lower total completion time. Nor should freed hours be treated as financial savings before the organization determines how that capacity will be used.
Define conditions for expansion: acceptable results, suitable cost and the ability to follow up failures. If a trial leaves one unanswered, record the gap and adjust the evaluation. Buying additional seats does not complete missing verification or make an unresolved dependency disappear.
5. Is there a routine after the first week?
Assign ownership for errors, model changes, usage and training needs. Keep reviewed instructions and examples accessible to the team. The coding-agent governance article expands on responsibilities and permitted effects; the cost guide helps track spending. Expand when the work can be repeated and explained, while keeping unproven assumptions visible.
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Tiago F SantiagoComments
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