Compare AI pilot results across Tennessee locations fairly

Agree on the unit of work

Define when a case is complete and what the receiving team must have. A prepared draft and an accepted business update are different outcomes. For a Tennessee rollout, confirm that each location records completion the same way. Otherwise an apparent performance difference may reflect reporting conventions rather than the usefulness of the AI workflow.

Document the sample and its limits

Record the request types, time period, user experience, and exceptions included. Separate active work from waiting for approval or external information. NIST’s AI risk framework is a reference for measurement in context; our recommendation is to preserve those contextual differences instead of hiding them inside one regional score that appears more precise than the underlying evidence.

Reference: NIST: AI Risk Management Framework

Compare corrections by consequence

Distinguish missing source evidence, wrong record references, and minor formatting edits. A location handling more complex work may legitimately need more review. Inspect why the difference exists before changing the system or judging adoption. Include cases that staff completed manually so the scorecard does not improve merely by excluding the difficult work from the automated path.

Use the comparison to guide expansion

Identify which conditions support success and which require additional work. State whether a proposed expansion is supported by observed evidence or a forecast. Agentix can connect regional pilot measurement to an implementation roadmap. A useful comparison explains the next decision for each location rather than declaring a single winner or projecting unmeasured savings across the entire organization.

Reference: Agentix (publisher): Agentix services

Common questions