Workplace AI Feature Evaluation Framework

Workplace AI Feature Evaluation Framework

Decision and baseline

The evaluation begins with one work decision. It names the task, users, inputs, outputs, current process, quality measure, total time, downstream authority, error consequence, and reason the feature might improve the workflow.

The baseline includes review and correction, not just draft time.

Data and access boundary

Record every content source the feature can use, the test user's permissions, connected apps, retention, logging, administrative controls, sharing paths, and excluded data.

Permissions determine possible access. They do not prove that every accessible item is appropriate context.

Representative test

The test set should include common cases, difficult cases, missing evidence, conflicting evidence, stale evidence, sensitive data, accessibility cases, and tasks the feature should decline.

Measure output correctness, completeness, source traceability, total correction, user comprehension, access behavior, harmful failure, support effort, and cost.

Decision state

Approve only for the bounded task and authority tested. Narrow when value exists but errors require a smaller scope or stronger review. Pause for a correctable control gap. Reject when the task does not improve, sources are not traceable, access is unsafe, or total cost exceeds the benefit.

NIST AI RMF can help structure governance, mapping, measurement, and management. It does not certify a vendor feature.