These ai-enabled internal mobility survey questions help you see what mobility KPIs won’t show: whether employees and managers trust AI matching, feel treated fairly, understand what data is used, and feel safe exploring internal moves. You get early warning signals, sharper conversations in 1:1s, and clear next actions for HR, managers, and governance teams.

Survey questions

Use a 1–5 Likert scale for all closed statements: 1 = Strongly disagree, 5 = Strongly agree. Keep one “Not applicable” option for items that won’t fit every role.

If you want a broader internal baseline, you can pair this with your existing internal mobility survey and then add the AI-specific blocks below for pilot groups.

Closed questions (Likert scale 1–5)

Overall 0–10 ratings (NPS-style)

Open-ended questions (Open text)

Key takeaways

Definition & scope

This survey measures how employees and managers experience AI-supported internal mobility (interne Mobilität), including AI matching, explainability, perceived fairness, psychological safety, and Datenschutz expectations. It fits pilot groups and later rollouts in DACH/EU organizations, supporting decisions on tool governance, manager enablement, data controls, skills frameworks, and how mobility conversations are handled.

How to run ai-enabled internal mobility survey questions in a pilot (timing + sampling)

Run these ai-enabled internal mobility survey questions right after the first real exposure to AI-supported moves, not after the launch email. A good trigger is “employees received suggestions for ≥2 weeks” or “a talent review used AI suggestions once.” In DACH contexts, align the survey plan with Betriebsrat and Datenschutz stakeholders early, because trust issues show up faster when employees suspect hidden evaluation. If you already operate an internal talent marketplace, align the survey cadence with marketplace waves and role family expansions; the guide on talent marketplaces is a practical reference for thinking in waves, not big-bang rollouts.

Keep your first pilot sample tight: 1–3 business units, one region, and a clear use case (roles, projects, gigs, or mentorship). Then run the survey in 2 waves: Wave 1 after the first matching experience; Wave 2 after you have changed at least 1–2 things (explanations, permissions, or manager scripts). That second wave is where you see whether trust and perceived fairness can actually move. If your participation drops below 60 % in a pilot, treat that as a signal too: people may not feel safe, or they think feedback won’t change anything.

Analyzing ai-enabled internal mobility survey questions: what “good” looks like by dimension

The fastest way to get value from ai-enabled internal mobility survey questions is to score by dimension, not by individual item. You want to know whether you have a “transparency problem” (people don’t understand or control data), a “fairness problem” (perceived bias or group gaps), or a “safety problem” (fear of backlash). Pair that with outcome items (Q37–Q42, Q73–Q78) to avoid local optimization. For example: you can raise perceived transparency, but if relevance stays low, employees still won’t use the tool.

Score each dimension as an average of its items (1–5). Then look at (1) level, (2) spread across teams, and (3) gaps across groups (remote vs. office, part-time vs. full-time, location, job family). If you see a gap ≥0,5, don’t debate intent; treat it like a product defect until proven otherwise. To make fixes stick, connect analysis to your skills data quality work. Most “bad matching” complaints are really stale skills, vague role requirements, or missing preference signals. Your underlying skill architecture matters; the skill management guide is a useful checklist for where skill data typically breaks (self-report bias, outdated profiles, inconsistent role definitions).

Interventions that raise trust, fairness, and usability (without freezing mobility)

When scores drop, avoid the reflex to “turn off AI.” Employees and managers usually ask for clearer boundaries, not less technology. The most effective fixes are boring: better explanations, better data hygiene, and better manager conversations. Start with Q7/Q15 (transparency) and Q25/Q26 (safety), because those two areas often unlock usage. If you want one rule: any AI suggestion that could influence a person’s career must be explainable in plain language, and discussable without fear.

Managers need help here. Give them scripts for how to talk about AI suggestions, what to say when suggestions are wrong, and how to encourage internal moves without punishing curiosity. You can embed that into your manager enablement work; the practical AI training for managers playbook is a good reference for what managers need to do differently in 1:1s, reviews, and decisions when AI is present. For operational follow-through, a talent platform like Sprad Growth can help automate survey sends, reminders and follow-up tasks, so actions don’t die in spreadsheets.

Blueprints: choose the right survey length for pilots, pulses, and follow-ups

You don’t need to ask all items every time. Use the full bank once, then move to a short pulse that tracks the same dimensions. This also makes works council alignment easier: fewer questions, clearer purpose, faster action. A practical pattern is “deep dive once, pulse twice.” Use the deep dive to learn what’s broken; use the pulses to confirm your fixes improved trust and perceived fairness.

DACH governance for AI-supported interne Mobilität (Betriebsrat + Datenschutz in practice)

In DACH, perceived legitimacy matters as much as model quality. Employees will ask: “Who sees this?” “Can this hurt me?” “Is this a hidden performance signal?” Answer those questions upfront, in writing, with the Betriebsrat involved. Don’t hide AI behind vague wording. If AI matching influences who gets seen for roles or projects, employees will assume it influences careers. Your job is to define boundaries and make them enforceable: permissions, retention, human override, and escalation routes. If you already run structured employee listening, reuse your governance patterns from employee survey templates (anonymity thresholds, reporting rules, closed-loop commitments) and apply them to AI mobility feedback.

Make governance visible in the survey itself. Add one short preface in the survey tool: what the AI uses, what it does not use, how long data is retained, and who can see what. Then ensure your reporting cannot deanonymize people. A common approach is: no slicing below n ≥10, no manager gets a “team of 3” view, and open text is reviewed for identifiers before sharing. Also, define a clear escalation path for suspected unfairness: employees should know whether to go to HRBP, DPO, or a joint HR–Betriebsrat channel, and what response time to expect (≤7 days for a first response is a good standard for non-urgent concerns).

Scoring & thresholds

Use a 1–5 agreement scale (1 = Strongly disagree, 5 = Strongly agree). Treat scores as averages per dimension (see tables above) and track changes between waves. Use these thresholds: Avg <3,0 = critical; 3,0–3,9 = needs improvement; ≥4,0 = strong. Turn results into decisions by mapping low dimensions to fixes: training (low understanding), product/workflow changes (low transparency or relevance), governance audits (low fairness), manager coaching (low psychological safety), and permissions/retention updates (low privacy trust).

Follow-up & responsibilities

Assign follow-up like an operations process, not a “nice to have.” Route signals by topic: managers own team-level psychological safety and mobility support (Q25–Q30, Q67–Q72); HR owns process clarity, communication, and capability building; IT/HRIS owns tool changes; DPO and works council partners own privacy and co-determination topics. Set response times: ≤24 h for any comment that signals retaliation fear or serious misconduct; ≤7 days to acknowledge and outline next steps for trust/fairness issues; ≤30 days to publish a concrete action plan with owners and deadlines.

Fairness & bias checks

Check results by relevant groups: location, job family, seniority level, remote vs. office, part-time vs. full-time, and (where legally and ethically appropriate) demographic categories. Your goal is not to “prove no bias.” Your goal is to detect patterns early and fix them. Use two lenses: perception gaps (survey differences) and opportunity gaps (who gets suggested what, who gets contacted, who moves).

Common patterns and responses: (1) Remote employees rate relevance lower (Q13–Q18) and fairness lower (Q19–Q24): review whether roles are coded with unnecessary location constraints; fix role data and manager habits within ≤45 days. (2) Part-time employees report lower safety (Q25–Q30) and lower access (Q38): audit whether managers block moves for “availability” assumptions; add an explicit fairness rule and manager coaching within ≤30 days. (3) Employees understand less than managers (Q1–Q6 vs. Q43–Q48): simplify explanations and stop using technical language; publish an FAQ within ≤14 days and repeat training.

Examples / use cases

Use case 1: Low transparency, decent relevance. You see Q13–Q18 around 3,8, but Q7–Q12 at 3,1 and R3 at 5,9. Decision: don’t retrain managers first; fix explanations and controls. Action: add “skills matched” and “missing skills” labels to each suggestion, plus a one-click “edit profile” path. After 60 days, you re-run the pulse and R3 increases to ≥7,0 while relevance stays stable.

Use case 2: Safety is the blocker, not the tool. Relevance and transparency are fine (≥3,8), but Q25/Q26 are at 3,0 and open comments describe fear of backlash. Decision: treat this as a leadership behavior issue. Action: HRBPs run manager clinics with a clear standard: employees can explore internal moves without punishment; backfill planning is a leader responsibility. You track improvement by re-running Q25–Q30 in ≤8 weeks and looking for ≥0,3 uplift.

Use case 3: Fairness concerns in one location. Overall fairness looks okay, but one site shows Q19–Q24 at 3,1 and a group gap ≥0,6. Decision: pause scaling in that location. Action: run a focused audit on data quality and role availability; check whether roles/projects are posted equally and whether local managers use the tool. You document mitigations, involve the Betriebsrat, and only resume rollout after the next wave shows fairness ≥3,7.

Implementation & updates

Implement in phases: pilot, rollout, and continuous updates. Start with one pilot area, keep the survey short enough to act fast, and treat wave-to-wave changes as part of your AI governance. Train managers before the first results arrive, so they don’t become defensive when feedback is critical. Then review the question bank at least 1x per year: remove items that no longer drive decisions, add items for new use cases (gigs, mentorship, succession), and update thresholds if your baseline shifts.

Track a small set of KPIs so you can connect perception to outcomes: participation rate, average dimension scores, group gaps, % of actions delivered on time, and internal mobility outcomes (e.g., internal fill rate or time-to-staff) as a separate layer. If you already run structured talent reviews, align actions with your calibration routines; a consistent calibration process reduces subjective overrides and improves trust in decisions. The talent calibration guide is a good model for roles, evidence standards, and decision logs that pair well with AI-assisted suggestions.

Conclusion

AI-supported internal mobility can speed up matching and surface hidden opportunities, but only if people trust the process. These ai-enabled internal mobility survey questions give you three things you can act on fast: early warning signals when transparency or psychological safety breaks, clearer conversations between employees and managers about growth moves, and a shared set of priorities for improving data, governance, and workflows.

Start small and concrete: pick one pilot area, implement blueprint (a) and (b) in your survey tool, and agree on owners for follow-up before you send the first invitation. Then commit to publishing results and changes within ≤30 days. When employees see that feedback changes explanations, permissions, and manager behavior, adoption becomes a byproduct of trust.

FAQ

How often should you run this survey?

For pilots, run Wave 1 about 10–14 days after employees first receive AI suggestions, then a short pulse after you ship fixes (usually 6–8 weeks later). After rollout, run a combined pulse every 6–8 weeks for the first 6 months, then switch to a 6–12 month follow-up. If your AI matching rules or data sources change materially, run an extra pulse within ≤30 days.

What should you do when scores are very low (e.g., Avg <3,0)?

Don’t ask managers to “explain it better” and hope it improves. Treat Avg <3,0 as a stop-and-fix signal. Pause scaling, identify the failing dimension (transparency, fairness, safety, privacy), and implement one visible change within ≤45 days. Publish what changed. Then re-run only the core pulse items for that dimension. If scores stay low after 2 waves, revisit the use case or governance.

How do you handle critical comments about bias or retaliation?

Separate two paths: (1) aggregated learning for process improvement and (2) individual risk handling. For any comment suggesting retaliation, discrimination, or misuse of data, respond within ≤24 h through your established employee relations channel. For bias concerns, acknowledge within ≤7 days, explain the review steps, and share the mitigation plan. Keep anonymity intact and redact identifiers before sharing comments.

How do you align with the Betriebsrat and GDPR expectations?

Bring the Betriebsrat in before the pilot survey, not after complaints. Share the survey purpose, the exact items, anonymity rules (e.g., n ≥10), and who can see results. Explain data flows, retention, and access rights for AI matching and survey data, and document them in a policy or Dienstvereinbarung. For GDPR context, reference the official General Data Protection Regulation (GDPR) text when aligning roles and responsibilities.

How should you update the question bank over time?

Review annually and after major scope changes (new data sources, new matching logic, expansion from roles to gigs). Keep trend items stable (trust, fairness, safety, privacy) so you can compare year over year. Retire items that don’t drive decisions, and add items when new risks appear (e.g., new visibility rules, new manager workflows). Document every change and keep a simple version history so results remain interpretable.