Companies are still figuring out how to measure AI in performance, but a pattern is emerging - they’re moving from “Did you use AI?” to “Did AI materially improve your performance and outcomes?”
Below is how leading companies can think about structuring performance review questions and metrics.
1) AI usage & adoption (baseline behavior)
These are the most basic and increasingly table stakes questions.
Example questions
“How have you incorporated AI tools into your daily workflow?”
“What % of your work is augmented by AI?”
“Which tools do you regularly use, and for what tasks?”
“Where did you choose not to use AI, and why?”
How it’s measured
Tool usage frequency
% of tasks supported by AI
Breadth of use cases (writing, analysis, coding, etc.)
But companies are increasingly moving away from pure usage tracking because it’s easy to game and doesn’t correlate directly with performance (Reworked.co).
2) Productivity & efficiency gains
Efficiency gains are becoming the centerpiece of AI evaluation.
Example questions
“What measurable productivity gains did AI enable?”
“How much time did AI save on key workflows?”
“What output volume or speed improvements did you achieve?”
“What work did AI allow you to take on that you couldn’t before?”
How it’s measured
Time saved (hours/week)
Output increase (e.g., reports, code, deals closed)
Cycle time reduction
Cost savings or capacity unlocked
Companies are explicitly asking employees to quantify impact and tie AI use to business outcomes (Built In).
3) Business impact & outcomes
This is where performance reviews are heading: AI as a lever for business results.
Example questions
“How did your use of AI improve team or company outcomes?”
“What revenue, customer, or operational impact resulted?”
“Can you point to a project where AI changed the result?”
How it’s measured
Revenue influence/ pipeline acceleration
Customer satisfaction improvements
Quality improvements (accuracy, fewer errors)
Strategic impact (new initiatives enabled)
This aligns with pressure to prove ROI on AI investments, not just adoption (Built In).
4) Quality, judgment & responsible use (human + AI collaboration)
Companies are realizing AI usage without judgment can hurt performance.
Example questions
“How did you validate AI outputs before using them?”
“Where did you override or improve AI-generated work?”
“How do you ensure accuracy, ethics, and data privacy?”
“What mistakes did you catch or prevent?”
How it’s measured
Error rates / rework
Quality of final outputs
Evidence of critical thinking
Responsible AI behaviors (bias awareness, compliance)
The shift is toward evaluating judgment and capability, not just tool usage (reworked.co).
5) Innovation & AI-driven problem solving
Top performers are differentiated by how creatively they use AI.
Example questions
“What new ways have you applied AI in your role?”
“What workflows have you redesigned using AI?”
“Have you shared or scaled AI best practices across the team?”
How it’s measured
New use cases introduced
Process improvements driven
Internal influence (teaching others)
Some companies explicitly track whether employees propose new AI use cases or improvements (reworked.co).
6) Skill development & AI fluency
Companies are now measuring AI literacy.
Example questions
“What AI skills have you developed this year?”
“How effectively can you prompt, refine, and iterate with AI?”
“Do you understand limitations like hallucinations or bias?”
How it’s measured
Skill assessments (prompting, evaluation, tool selection)
Training completion
Demonstrated proficiency in real work
There’s a growing push to assess actual AI understanding, not just usage (Business Insider).
7) Collaboration with AI
Some companies are experimenting with human-AI teaming metrics.
Example questions
“How effectively do you collaborate with AI to enhance outcomes?”
“What portion of your deliverables are AI-assisted vs. human-created?”
“How do you sequence AI vs. human work?”
How it’s measured
“AI-assisted output” ratios (e.g., code, content)
Efficiency of human-AI workflows
Dependency vs. augmentation balance
In some orgs, even metrics like “AI-generated contribution” or “tokens per employee” are being explored (New York Post).