AI Use-Case Discovery Workshops for Enterprises

Describe the task before naming the technology

“Use AI in finance” is a theme, not a use case. “Draft management commentary from approved monthly figures for a finance analyst to review” is a task that can be examined. It has a user, inputs, an output and a human decision point.

Ask participants to bring examples of recurring work, not a wish list of products. For each example, identify where time, quality or service problems occur. Sometimes a clearer process, better data or a conventional automation is the more appropriate answer.

Select a useful discovery group

Include people who perform the work, a process owner, someone who can judge the output and a person who understands relevant access or risk constraints. A discovery workshop can fail when ideas are scored by people who cannot verify how the process works.

Before the session, collect a brief description of the current task, its frequency, input sources and common errors. Treat estimates as estimates. A participant’s recollection of time spent can suggest what to measure, but it is not yet a reliable productivity baseline.

Use a consistent use-case card

Prioritize with evidence and a confidence note

Compare business relevance, feasibility, data readiness, ability to evaluate output and consequences of error. A simple low/medium/high assessment can be useful when accompanied by a reason and an owner. Do not turn a subjective workshop score into a forecast of financial return.

For example, an internal summary task may be easy to evaluate against its source but depend on restricted information. A public-information research task may have fewer access barriers but require careful source checking. The ranking can change once those dependencies are tested.

Example: separate three similar ideas

A fictional HR team proposes drafting onboarding instructions, screening job applicants and summarizing employee complaints. These all involve text, but they have very different data and decision consequences. A discovery process should not group them as one “HR automation” opportunity.

An onboarding draft based on approved policy can be reviewed for accuracy. Decisions about applicants or sensitive employee information demand specialist assessment and stronger safeguards. The workshop should identify those boundaries and route unresolved cases to the appropriate owners, not approve them because the model can generate an answer.

Finish with a small, testable next step

For a shortlisted task, specify a safe sample, an authorized environment, a reviewer and pass/fail criteria. Define what the trial will not do: for example, no automatic sending, no production system changes and no consequential decision without approval. Name the person who will decide whether the evidence is sufficient.

The workshop output is a shortlist and investigation plan. Deployment, integration, governance controls and operational measurement are separate work. A high ranking on the shortlist is permission to investigate under agreed conditions, not evidence that a solution is ready.

Bring your business challenge to AISFY

An effective enquiry describes the function, the recurring work and the decision you want the workshop to support. Include known data or policy constraints. The proposed scope can then define the participant mix, preparation and useful working outputs without promising implementation before feasibility is understood.

Frequently asked questions