Connect an AI use case to a business constraint, a baseline and someone responsible for the result.
An AI demonstration can show that a task is technically possible. It does not establish that the business should fund it, that people will use it or that the workflow will improve.
Define the operating problem before choosing a model or tool. Examples include slow document handling, expensive manual classification or the time required to prepare an accurate answer. Keep the business outcome separate from the technology.
An initiative needs someone who owns the operating result and can change the workflow. Technology can support the implementation, but it cannot independently decide the acceptable error rate, escalation process or trade-off between speed and quality.
Make the decision rights explicit: who approves the use case, who evaluates outputs and who can stop or change the pilot.
A useful model response may still add work if someone must repeatedly find the right input, repair the output and transfer it between systems. Evaluate the full process from the initial request to the accepted result.
Include the people who handle exceptions. Their experience often reveals whether the proposed workflow is practical and where the quality standard needs to be made more explicit.
A pilot should end with a decision supported by evidence. Expand when the workflow produces a useful result within its boundaries. Revise when the value is promising but the process is incomplete. Stop when the economics or risks do not support further work.
The goal is not to accumulate AI projects. It is to make an operating result better, with someone accountable for whether that happens.
Bring the constraint, the decision or the transition. We can work out whether a focused executive mandate is the right next step.
