These ai performance review survey questions help you measure how people experience AI-generated drafts, summaries, and suggestions in performance reviews—separately for employees and for Führungskräfte. You’ll spot trust, fairness, and privacy issues early, and you’ll get clear thresholds that tell you when to pause, train, or tighten guardrails.

Survey questions

Use a 5-point Likert scale for the statements (1 = Strongly disagree, 5 = Strongly agree). Numbering below is for analysis and follow-up: E = Employees, M = Managers, S = Shared.

Employees (E1–E36) — Awareness & transparency

Employees (E7–E12) — Quality & usefulness of AI-assisted feedback

Employees (E13–E18) — Fairness & bias perceptions

Employees (E19–E24) — Psychological safety & trust

Employees (E25–E30) — Data protection, privacy & consent

Employees (E31–E36) — Overall impact & preference

Managers (M1–M42) — Onboarding & training

Managers (M7–M12) — Workflow & time impact

Managers (M13–M18) — Quality of drafts & summaries

Managers (M19–M24) — Judgment, oversight & human accountability

Managers (M25–M30) — Governance & guardrails

Managers (M31–M36) — Fairness, consistency & calibration support

Managers (M37–M42) — Overall confidence & willingness to continue

Shared (S1–S6) — Experience across both audiences

Overall ratings (0–10)

Open-ended questions (12 total)

Key takeaways

Definition & scope

This survey measures how employees and managers experience AI use in performance reviews: usefulness, quality, fairness, psychological safety, and GDPR-aligned guardrails. Use it for teams in AI-assisted review pilots (direct reports and their Führungskraft). Results support decisions on training, transparency, calibration, and whether to expand, pause, or restrict AI features.

Survey blueprints for ai performance review survey questions

Run different versions depending on timing and audience. Keep your deep-dive short enough to finish in ≤8 minutes. If you already run a classic post-cycle survey, align wording with your existing performance review survey questions so trends stay comparable even when AI changes the workflow.

Use this simple flow: pick blueprint → load items → set anonymity rules → send within 3–10 days after reviews → publish results and actions within 21 days.

Scoring & thresholds for ai performance review survey questions

Use the 5-point scale for statements and 0–10 for overall ratings. Keep decisions tied to thresholds so you don’t overreact to anecdotes. If your review forms are being updated, align this survey with your performance review templates so managers see the same rubric language in both places.

Define three bands: Low (Avg <3,0), Medium (3,0–3,9), High (≥4,0). For ratings, treat <6/10 as a stop-and-fix signal in pilots.

Turn scores into decisions with a 4-step routine: (1) compute averages per dimension, (2) check dispersion and “Disagree” rates, (3) compare groups, (4) map to actions in the decision table.

Follow-up & responsibilities

AI in reviews fails when nobody owns the messy parts: corrections, escalations, and governance. Set routing rules up front and keep response times tight. If you already run calibration meetings, align follow-up with your talent calibration guide so AI doesn’t become a backdoor to inconsistent standards.

Use these response times as defaults in pilots: ≤24 h for severe privacy concerns, ≤7 days to acknowledge low trust scores, ≤21 days to publish actions.

Fairness & bias checks

Don’t treat fairness as a single average score. Break results down by relevant groups and compare both perception and process signals. If fairness concerns rise, use your internal bias playbooks and align language checks with performance review biases so managers can recognise patterns in wording and evidence quality.

Use practical red flags: group gap ≥0,4, or ≥15 pp difference in favorable rates, or repeated open-text mentions of “generic,” “copy-paste,” “unfair,” “privacy.”

Examples / use cases

Use the survey to make small, testable decisions. Treat these as concrete scenarios you can replicate in your environment, not as promised outcomes.

Scenario 1: Employees distrust AI because disclosure is unclear

You see E1–E6 Avg 2,8 and comments like “I only noticed AI wording after the meeting.” HR decides to add disclosure at the point of use: the review form shows “AI-assisted draft used: yes/no” plus a one-paragraph explanation. Managers get a 60-second script for the Mitarbeitergespräch. You re-run the combined pulse (Blueprint C) in 60 days to confirm trust moved upward.

Scenario 2: Managers save time, but employees feel feedback is generic

M7 and M8 score high (≥4,0), but E7–E12 score low (Avg <3,0) and E33 rises. The decision: keep AI for structuring, but require evidence. Each review section must include 2 proof points (project, metric, observable behaviour). HR shares “good vs bad” examples and updates prompts. In the next cycle, you spot-check 10% of reviews for specificity before they go to employees.

Scenario 3: Fairness concerns cluster in one group

E16 is stable overall, but one location shows a gap of 0,5 points and higher E14. HR and the local Betriebsrat align on a short review: check whether local teams used different prompts, rubrics, or data sources. You run a calibration refresher, tighten rubric anchors, and re-brief managers on bias patterns. You then compare the same group slice in the follow-up trend survey (Blueprint D) to see if the gap narrowed.

Implementation & updates

Start small, then scale. In DACH, involve the Betriebsrat early when AI touches performance processes, monitoring questions, or decision support. Keep the survey clearly separate from individual outcomes: responses must not be used to adjust someone’s rating or pay, or you will kill trust and participation. A talent platform like Sprad Growth can help automate survey sends, reminders, and follow-up tasks, while keeping actions and deadlines visible for owners.

Use this rollout rhythm: pilot (6–10 weeks) → first review cycle → survey + fixes (≤30 days) → scale to next area → trend check at 6–12 months. Pair rollout with role-based training, using resources like AI training for employees, AI training for managers, and AI training for HR teams so people learn the same guardrails and editing standards.

If you need a broader enablement roadmap, connect this survey to your wider AI governance and skills work. A structured program like AI training programs for companies and an internal skills baseline (for example a simple AI skills matrix) make your results easier to interpret: you can separate “tool issues” from “skill gaps” and plan fixes faster.

Done well, this survey gives you three practical benefits: you detect trust and privacy issues early, you improve the quality of review conversations (not only the text), and you get clearer priorities for training and guardrails. Next, pick one pilot area, load the relevant blueprint into your survey tool, and name owners for transparency, privacy, and calibration follow-up. After the first cycle, publish what you changed and when—then re-check trends 6–12 months later to confirm AI is helping, not quietly damaging fairness.

FAQ

How often should we run these ai performance review survey questions?

If you’re piloting, run it after each AI-assisted review cycle for the first 2 cycles. That gives you fast feedback while people still remember what happened. After that, a deep-dive 1× per year plus a short pulse after major AI feature changes works well. Keep at least 6–8 core items stable so you can track trust and fairness trends over time.

What should we do if scores are very low (Avg <3,0) or comments are harsh?

Start with containment and clarity, not defensiveness. Acknowledge results within ≤7 days and say what will happen next. Use focus groups to find root causes: is it disclosure, generic wording, privacy fear, or manager behaviour? Then pick 3 fixes max with owners and deadlines. Close the loop publicly within ≤21 days. If privacy concerns are severe, route to DPO/IT with response ≤24 h.

How do we keep this survey from feeling like monitoring or performance control (especially in DACH)?

Be explicit about purpose and separation: survey responses must not influence ratings, pay, or individual outcomes. Aggregate results and apply anonymity thresholds (for example, no reporting for groups smaller than 5–7). Align with the Betriebsrat early and document guardrails in plain language. If you need framing, the EDPB guidance on automated decision-making and profiling is a helpful reference point: EDPB Guidelines.

Should we tell employees when AI was used in their review?

Yes—if you want trust. Lack of transparency tends to inflate fairness concerns even when the output quality is fine. Keep it simple: say where AI was used (drafting, summarising, calibration support), what data it did and did not use, and that the manager owns the final content. Add a correction right: employees can challenge wording and request edits without needing to “prove” the AI was wrong.

How do we keep the question bank current as tools and policies change?

Run an annual review with HR, a few managers, People Analytics, and (where applicable) the Betriebsrat. Keep your core trend items stable (trust, fairness, safety, privacy), and rotate a small set of “feature-specific” questions based on what changed (new summaries, new rating suggestions, new data sources). Pilot new items with 1 team first, then scale once wording is unambiguous and action-ready.