AI discovery
AI use-case discovery starts with work that has a clear decision, owner, input, consequence, and possible improvement path. The objective is to identify opportunities that can be tested honestly inside a defined service or process.
Map the task, decision, person doing it, information used, and condition that ends the work. Ask whether AI is assisting, recommending, classifying, summarising, or taking an action.
The AI and machine-learning services route provides an internal path when discovery needs technical evaluation.
Identify data sources, quality limits, access boundaries, sensitive content, freshness needs, and what a wrong output would cause.
Name the service owner, data owner, user, support route, human checkpoint, exception path, and evidence needed to accept the result. A use case without an owner is an idea, not a delivery candidate.
Compare the value question, data readiness, workflow fit, integration effort, control needs, user change, support, and reversibility.
Define the evaluation question, reviewer, acceptance condition, failure response, and next decision. Record what discovery did not establish.
Repetition is only one signal. The work also needs a clear outcome, suitable data, an accountable owner, and a safe way to review errors.
Bring the work, decision, or data question that needs an evidence-led AI assessment.
Discuss AI use-case discovery