AI-Assisted Performance Review Skill
The uncomfortable review question of the decade: when a report ships twice the output with AI, what did they do? Volume stopped measuring effort; polish stopped measuring skill. Punishing AI use is as wrong as crediting the model's work to the human. This skill separates the signals — and gives managers the conversation, not just the theory.
What This Skill Produces
- A what-measures-whom analysis of the role's current evaluation criteria
- Rewritten criteria that measure the human: judgment, verification, outcomes, leverage
- Calibration rules for teams with uneven AI adoption
- Conversation scripts for the three hard cases
Required Inputs
Ask for (if not already provided):
- The role and current review criteria (the rubric, or how it really works)
- How AI shows up in the work — which tasks, how much of the output it drafts, what the tooling reality is
- The specific situation, if any: one person's review? team calibration? criteria rewrite?
- The org's AI stance — encouraged? tolerated? policy exists? (Reviews must not punish sanctioned behaviour)
Method
- Sort every criterion: human, tool, or hybrid. Walk the current rubric. Volume of drafts, formatting quality, speed to first version → now mostly tool signals (evaluating them evaluates prompt luck and subscription tier). Decision quality, stakeholder trust, error catch rate, what they chose to build → still human. Output quality overall → hybrid: credit belongs to the pair, and the review's job is to see the human's contribution inside it.
- Rewrite around the four durable human signals:
- Judgment — what they decided to do, what they declined, how they scoped; the quality of taste applied to AI output (what they kept, cut, and corrected)
- Verification — do errors get caught before shipping? A person whose AI-assisted work is reliably right is demonstrating skill; one who forwards unverified fluency is a risk wearing productivity's clothes
- Outcomes — did the work move what it was for (the metric, the decision, the customer), independent of how it was produced
- Leverage — do they make AI multiply the team (shared prompts, workflows, teaching) or only their own count
- Set the calibration rules for mixed adoption. In one team you'll have a 2×-output adopter and a careful non-adopter. Rules that keep it fair: evaluate against the role's outcomes, not each other's volume · where AI use is sanctioned, not adopting is a development conversation (not a values one) · where someone's edge is invisible verification labour, surface it explicitly before comparing. Never let the review become a proxy war about the tools.
- Demand evidence that sees the human. Volume anecdotes are out. In: a sample of shipped work walked backwards (what did the AI draft, what did you change, why) · error/rework history · decisions log · peer signals about trust and leverage. The walk-backwards exercise is the single highest-signal artifact — put it in the review prep.
- Script the three hard cases:
- The volume star with thin judgment — "Your output doubled; let's walk three pieces backwards" (the conversation is about the delta between draft and shipped)
- The careful sceptic being out-shipped — outcomes-first framing; adoption raised as growth, not deficiency; their verification strength named as a strength
- The launderer — unverified AI work shipped as their own, errors reaching others: this is a reliability conversation with the accountability rule from the org's AI policy, not an AI conversation
Output Format
AI-Era Review Guidance: [role/team]
Criteria audit
| Current criterion |
Measures |
Verdict |
|
human / tool / hybrid |
keep / rewrite / kill |
Rewritten criteria: [the judgment/verification/outcomes/leverage set, with observable definitions each]
Evidence to collect: [the walk-backwards sample protocol + the rest]
Calibration rules: [the mixed-adoption rules, as committee guidance]
The conversations: [scripts for the three hard cases, adapted to the situation given]
Quality Checks
Anti-Patterns
1---2name: ai-assisted-performance-review3description: Evaluate performance fairly when output is AI-assisted — what still measures the human, what now measures the tooling, and how to run the review conversation. Use when reviewing someone whose work is heavily AI-assisted, when output volume stopped meaning anything, when calibrating a team with uneven AI adoption, or when writing review criteria for the AI era. Produces review guidance: a what-measures-whom analysis, rewritten criteria, calibration rules for mixed-adoption teams, and conversation scripts. For the general review document use performance-review; for redesigning the role itself use role-redesign-for-ai.4---5
6# AI-Assisted Performance Review Skill
7
8The uncomfortable review question of the decade: when a report ships twice the output with AI, what did *they* do? Volume stopped measuring effort; polish stopped measuring skill. Punishing AI use is as wrong as crediting the model's work to the human. This skill separates the signals — and gives managers the conversation, not just the theory.
9
10## What This Skill Produces
11
12- A **what-measures-whom analysis** of the role's current evaluation criteria
13- **Rewritten criteria** that measure the human: judgment, verification, outcomes, leverage
14- **Calibration rules** for teams with uneven AI adoption
15- **Conversation scripts** for the three hard cases
16
17## Required Inputs
18
19Ask for (if not already provided):
20- **The role and current review criteria** (the rubric, or how it really works)
21- **How AI shows up in the work** — which tasks, how much of the output it drafts, what the tooling reality is
22- **The specific situation**, if any: one person's review? team calibration? criteria rewrite?
23- **The org's AI stance** — encouraged? tolerated? policy exists? (Reviews must not punish sanctioned behaviour)
24
25## Method
26
271. **Sort every criterion: human, tool, or hybrid.** Walk the current rubric. Volume of drafts, formatting quality, speed to first version → now mostly **tool** signals (evaluating them evaluates prompt luck and subscription tier). Decision quality, stakeholder trust, error catch rate, what they *chose* to build → still **human**. Output quality overall → **hybrid**: credit belongs to the pair, and the review's job is to see the human's contribution inside it.
282. **Rewrite around the four durable human signals:**
29 - **Judgment** — what they decided to do, what they declined, how they scoped; the quality of taste applied to AI output (what they kept, cut, and corrected)
30 - **Verification** — do errors get caught before shipping? A person whose AI-assisted work is *reliably right* is demonstrating skill; one who forwards unverified fluency is a risk wearing productivity's clothes
31 - **Outcomes** — did the work move what it was for (the metric, the decision, the customer), independent of how it was produced
32 - **Leverage** — do they make AI multiply the *team* (shared prompts, workflows, teaching) or only their own count
333. **Set the calibration rules for mixed adoption.** In one team you'll have a 2×-output adopter and a careful non-adopter. Rules that keep it fair: evaluate against the role's outcomes, not each other's volume · where AI use is sanctioned, *not* adopting is a development conversation (not a values one) · where someone's edge is invisible verification labour, surface it explicitly before comparing. Never let the review become a proxy war about the tools.
344. **Demand evidence that sees the human.** Volume anecdotes are out. In: a sample of shipped work walked backwards (what did the AI draft, what did you change, why) · error/rework history · decisions log · peer signals about trust and leverage. The walk-backwards exercise is the single highest-signal artifact — put it in the review prep.
355. **Script the three hard cases:**
36 - *The volume star with thin judgment* — "Your output doubled; let's walk three pieces backwards" (the conversation is about the delta between draft and shipped)
37 - *The careful sceptic being out-shipped* — outcomes-first framing; adoption raised as growth, not deficiency; their verification strength named as a strength
38 - *The launderer* — unverified AI work shipped as their own, errors reaching others: this is a *reliability* conversation with the accountability rule from the org's AI policy, not an AI conversation
39
40## Output Format
41
42### AI-Era Review Guidance: [role/team]
43
44**Criteria audit**
45| Current criterion | Measures | Verdict |
46|---|---|---|
47| | human / tool / hybrid | keep / rewrite / kill |
48
49**Rewritten criteria:** [the judgment/verification/outcomes/leverage set, with observable definitions each]
50
51**Evidence to collect:** [the walk-backwards sample protocol + the rest]
52
53**Calibration rules:** [the mixed-adoption rules, as committee guidance]
54
55**The conversations:** [scripts for the three hard cases, adapted to the situation given]
56
57## Quality Checks
58
59- [ ] Every current criterion has a human/tool/hybrid verdict — none skipped as "obviously fine"
60- [ ] New criteria are observable behaviours, not virtues ("catches errors before shipping" not "is diligent")
61- [ ] Verification labour is explicitly valued somewhere — the invisible work made visible
62- [ ] Calibration rules prevent both punishing adoption and punishing non-adoption
63- [ ] The launderer case routes to reliability/accountability, not to relitigating the AI policy
64
65## Anti-Patterns
66
67- [ ] Do not credit or blame the human for what the model did — walk the work backwards to find the human
68- [ ] Do not keep volume metrics "because they're objective" — they're objective measurements of the wrong thing now
69- [ ] Do not run calibration comparing raw output across uneven adopters — that's a tooling lottery, not a review
70- [ ] Do not treat AI scepticism as a performance problem where use is optional — outcomes are the bar, not enthusiasm
71- [ ] Do not have the accountability conversation without the org's policy in hand — improvised rules in a review are how grievances are born
Mohit Aggarwal