How to Choose an Agentic AI Workflow Use Case
A useful agentic workflow candidate combines a valuable outcome, costly variation, accessible evidence, bounded authority and a result the business can verify.
Define the outcome and present baseline
State what completes the work and who benefits. Measure current volume, cycle time, effort, rework, error, escalation and business outcome. An automation claim has no meaning without a comparable starting point.
Find variation that consumes judgement
Look for work where people repeatedly search across sources, interpret unstructured material, reconcile conflicting evidence or choose among several permitted routes. High volume alone favours conventional automation when the path is stable.
Test whether the evidence is available
List the information a competent employee uses, where it resides, who owns it and whether it is current. A workflow is not ready if critical judgement depends on undocumented tacit knowledge, unreliable identities or inaccessible systems.
Bound the action surface
Specify what the agent may read, recommend, create or change. Separate reversible actions from actions involving money, eligibility, safety, customer commitments or legal consequences. Start with the smallest useful authority.
Check whether a simpler design is sufficient
Compare retrieval, a single AI task, deterministic workflow automation and a single agent before proposing multiple agents. Use the least complex design that handles the real variation.
Assess value after operating costs
Include integration, data repair, evaluation, model use, supervision, monitoring, incident response and change maintenance. Count value from better completion, reduced rework, faster decisions or lower risk, not only labour time removed.
Choose a pilot with learnable boundaries
Select one route, population and outcome. The pilot should expose real exceptions without placing material customers, assets or decisions at uncontrolled risk. Define the evidence that would justify expansion, revision or stopping.