Data chart · · CreditUnionAI News
Three ways finance firms are measuring AI at work
| Measure | Share |
|---|---|
| Formal AI skill criteria | 42% |
| AI disclosure in reviews | 30% |
| Teams or roles tracking AI returns | 30% |
Keep three review questions separate
A leader can define a skill, collect evidence that it was used and still lack evidence that the work improved. Our editorial recommendation is to keep the job outcome, the employee's opportunity to learn and the accountable review decision visible in the same record.
Start with a work sample and the control checks appropriate to that role. Record access to approved tools, training and support before comparing ratings. Name the person who can challenge a recommendation or hold a decision when the evidence is incomplete. A prompt count alone is not a member-service result or a reliable basis for pay.
The EEOC's performance-evaluation guidance emphasizes communicated standards, consistent application and factual detail. NIST's voluntary AI RMF supports accountable risk management. These sources do not certify this suggested workflow or establish that a particular rating is fair.
Read the full CUAI reporting, then compare our employee-coaching scorecard. Verify the job evidence before changing a compensation process.
Sources and method
Reuters reporting, October 14. Original CUAI bars; no Reuters imagery or quotations reproduced. The chart is a cross-sectional survey report, not a time trend, causal result or sector benchmark. Source checked October 20, 2026.