An AI use case template describes a business problem and the complete human–technology workflow before a team chooses a product or automates a task. It connects the current baseline, intended outcome, users, affected people, data, AI role, human decision, boundaries, evidence and stop conditions.
Use the builder above for a first brief. Then validate every statement with workflow owners and affected teams. “Use AI to improve productivity” is not a testable use case because it does not identify the work, change or evidence.
1. Purpose of an AI use case template
The brief lets decision-makers compare AI with a manual change, conventional automation, another product or doing nothing. Its output should support a decision to explore, redesign, assess or stop—not a preselected purchase.
Separate expected benefit from vendor capability. State the measurable organizational outcome and who benefits. Faster generation may not improve the full process when review, correction or downstream work increases.
2. Describe the current workflow and baseline
Map the trigger, people, systems, information, decisions, actions, exceptions and completion point. Measure current volume, time, quality, cost, rework and incidents using a defined period. Preserve uncertainty rather than inventing precision.
NIST’s AI RMF Map guidance emphasizes intended purpose, beneficial uses and deployment context. Apply that principle to the whole workflow, not only the model.
| Element | Weak | Decision-ready |
|---|---|---|
| Problem | Support is slow | Reviewed replies average 18 minutes for defined ticket types |
| Outcome | Save time | Reduce total handling time within named quality limits |
| AI role | Answer customers | Draft a reply; an agent verifies and publishes |
| Stop rule | If it fails | Pause after restricted-data exposure or incorrect action |
3. Define the AI role and human decision
Choose whether the system drafts, searches, summarizes, classifies, recommends or acts. Describe what the human sees, what remains their decision and how corrections reach the record. Limit autonomy to what evidence and risk tolerance support.
Name users, administrators, reviewers and affected people. Consider those who do not operate the tool but experience its output. Document accessibility, language, contestability and communication needs.
4. Set data and action boundaries
List permitted and prohibited information using existing classifications. Map sources, destinations, retention, integrations and permissions. Do not assume data is anonymous when remaining details identify a person or remain confidential.
Define forbidden outcomes and actions. Link the brief to the privacy checklist, risk assessment and oversight design.
Map dependencies and failure paths
Identify upstream sources, model or service dependencies, connected systems and every destination that may rely on output. Ask what happens when the service is unavailable, a source is stale, permission changes or the provider updates the model. Describe a safe fallback and the owner who can activate it.
Consider how people might extend the workflow after launch: copying output into another system, connecting an unofficial tool or skipping review under time pressure. Reasonable misuse belongs in the use-case boundary even when it violates the intended procedure.
5. Turn assumptions into testable requirements
For each expected benefit or control, record evidence, owner and threshold. Create representative normal, difficult and failure cases, including languages, missing context, conflicting sources and reasonable misuse.
Compare with the baseline through the structured pilot. Measure full workflow time and quality, not output speed alone.
Estimate cost and organizational change
Include subscriptions, usage, integration, administration, review, correction, training, support and exit. State which teams or responsibilities change and whether the proposed benefit depends on unrealistically perfect adoption. Use ranges where volume or price remains uncertain.
Define the minimum evidence required before procurement and the separate evidence needed before production. A convincing demonstration may justify exploration, but it cannot establish performance on the organization’s cases.
6. Decide whether AI is suitable
Compare benefit, risk, feasibility, cost, change, dependency and alternatives. A valid decision may be conventional software, better documentation or process redesign. Record explore, pilot, redesign or stop, with owner, conditions and reassessment triggers.
Do not write the use case around a product already purchased. That reverses the decision and hides alternatives.
7. Use-case examples
Support draft
An approved service drafts replies for selected tickets. An agent verifies facts and sends; account changes remain outside scope.
Meeting summary
The system proposes actions and the organizer corrects and assigns them. Sensitive meeting categories are excluded.
Invoice extraction
The system extracts fields; validation and a reviewer handle exceptions before posting. It does not authorize payment.
Common use-case mistakes
- Starting from a vendor feature list.
- Omitting baseline and measurable outcome.
- Calling the AI role “assist” without defining it.
- Ignoring affected people and downstream records.
- Leaving data, autonomy and stop rules implicit.
- Testing only easy examples.
AI use case template FAQ
This AI use case template should remain short enough to review and precise enough to test.
How long should the brief be?
Often one to three pages plus linked evidence.
Who writes it?
The workflow owner coordinates users, affected teams and specialists.
Should a vendor help?
It can explain capability, but the organization owns context and boundaries.
When is it updated?
After material changes to purpose, users, data, AI role or provider.
Methodology and limitations
ScoutChoice designed this AI use case template around problem, baseline, outcome, actors, information, AI role, human control and stop conditions. The builder runs locally.
This is general information, not legal, security, privacy or compliance advice.