How to Choose an AI Agent Use Case

A scoring framework for selecting AI agent opportunities based on business value, feasibility, data access, risk and measurability.

Start with a workflow, not an agent idea

Describe the job in operational terms: who initiates it, what information is received, which systems are used, what decision is made and what outcome completes the task.

Look for useful variability

Agents are most valuable where inputs vary and interpretation matters. Highly predictable steps may be better handled by traditional automation.

Score business value

Estimate frequency, staff effort, delay, customer impact, error cost and capacity constraints.

Check data and integration feasibility

Identify the information the agent needs and the systems it must access. Confirm APIs, permissions, data quality and authentication.

Rate consequence and reversibility

Ask what happens if the agent is wrong. Strong first use cases usually have bounded consequences and an easy escalation path.

Define the human boundary

Specify what the agent can do independently, when it should ask for confirmation and when it must escalate.

Choose measurable success criteria

Use task completion, cycle time, handling time, escalation rate, error rate, adoption or another operational measure.

Use a simple selection score

Score candidates across value, frequency, variability, data readiness, integration feasibility, measurability and risk.