By Flow State Leadership
Before building an AI roadmap, define the work you want to improve, who owns the result and what evidence would justify adoption. Start with a bounded test, not a tool purchase. Leadership’s job is to set direction, notice what the test reveals and adapt without handing accountability to the system.
General educational guidance. Illustrations are hypothetical, not client results.
What business problem should AI solve?
Describe the work and its constraint: speed, accuracy, access to information or capacity. Record the current workflow and its failure points. Compare an AI test with a simpler process change; AI may not be the best next step.
For a hypothetical support team, the problem might be slow access to approved answers, not slow writing. Test draft responses against a small set of non-sensitive questions. Compare factual correctness, review time and usefulness with the current approach. These are proposed measures, not reported results.
Who owns the output and the risk?
Name a business owner and a human reviewer. Specify which outputs require approval and which decisions the system must not make. A fluent answer is not evidence that it is correct; check claims against authoritative material.
Before providing data, review access controls, retention, vendor terms and organizational policy with the appropriate specialists. Keep sensitive data out of unapproved tools. Legal, security and privacy requirements depend on the context; this guide is not a compliance assessment.
What would make you continue, change or stop?
Write the acceptance criteria before the trial: the tasks in scope, quality requirements, review burden, cost and unacceptable failure modes. Include a way to disable the test and return to the existing workflow.
In the support example, an invented policy is grounds to pause and investigate even if drafts arrive faster. If reviewers spend more time correcting than they save, narrow the task or stop. Set a review date rather than allowing a pilot to become permanent by default.
How will the people doing the work learn?
Ask the team where the change helps and where it introduces extra work. Give people practice in checking outputs and a clear route for reporting problems. Explain what changes in their responsibilities and what does not.
Evaluate the whole workflow, not just generated text. An AI draft that looks impressive in a demo may still create rework downstream. Review unexpected failures alongside useful outputs and adapt the plan to the evidence.
Where does leadership guidance end?
A leadership conversation can clarify priorities, ownership and adoption decisions. It does not replace technical implementation, independent security review or advice from qualified legal and regulatory specialists.
Take a short brief into the next discussion: the business problem, baseline, approved data, accountable owner, test boundaries and stop conditions. Missing answers are a reason to clarify the scope, not to promise a rollout.