AI Enablement Survey Questions: How Employees Experience AI Training, Tools and Policies

This AI enablement survey helps you move beyond “we rolled out a tool” and see how people truly experience AI. With the right AI enablement survey questions you spot confusion, fears and gaps early – and can adjust training, tools and policies before frustration or compliance risks grow.

Survey questions

The question bank below is intentionally broad. You won’t use every item in every survey. Later blueprints show how to combine these AI enablement survey questions into short pulses or deeper baseline surveys. Combine results with your existing AI training for employees and skills work.

Likert-scale questions (1–5)

Scale: 1 = Strongly disagree, 2 = Disagree, 3 = Neither, 4 = Agree, 5 = Strongly agree.

Q1. I know which AI tools (e.g. ChatGPT, Copilot, Atlas AI) are officially available in our company. [Audience: All employees]
Q2. I understand what the main AI tools can and cannot do in my role. [Audience: All employees]
Q3. I know where to find our internal guidance, FAQs or playbooks on using AI. [Audience: All employees]
Q4. I understand which AI tools are experimental and which are approved for daily work. [Audience: All employees]
Q5. I know whom to contact if I am unsure whether an AI use case is allowed. [Audience: All employees]
Q6. I understand the basic terms we use for AI (e.g. prompt, training data, hallucination). [Audience: All employees]
Q7. I understand the main goals of our AI enablement program. [Audience: All employees]
Q8. I have received enough training to use the company’s AI tools safely and productively. [Audience: All employees]
Q9. I feel confident using AI for core tasks in my role (not only for experiments). [Audience: All employees]
Q10. I can judge when AI output is good enough versus when I need to double‑check or redo work. [Audience: All employees]
Q11. I know how to write effective prompts for the AI tools I use. [Audience: All employees]
Q12. I feel comfortable experimenting with AI features without fear of “breaking” something. [Audience: All employees]
Q13. I know which skills I still need to develop to use AI more effectively in my role. [Audience: All employees]
Q14. I receive feedback or coaching on how I use AI in my work. [Audience: All employees; Managers as givers]
Q15. AI features are well integrated into my daily tools (e.g. Office, HR platforms, ticketing systems). [Audience: All employees]
Q16. I can access AI tools (accounts, licenses, VPN, devices) without technical obstacles. [Audience: All employees]
Q17. AI tools fit naturally into my existing workflows instead of creating extra steps. [Audience: All employees]
Q18. When AI suggestions are wrong or unhelpful, I can easily correct or override them. [Audience: All employees]
Q19. I rarely have to switch between many tools or tabs to use AI in my work. [Audience: All employees]
Q20. Our team has defined a few concrete AI use cases for our main processes. [Audience: All employees; Managers as owners]
Q21. IT provides timely support when we face issues with AI tools or integrations. [Audience: All employees; HR/IT as owners]
Q22. I understand the rules for which data I may or may not put into AI tools. [Audience: All employees]
Q23. I trust that personal data is handled according to GDPR when we use AI tools. [Audience: All employees]
Q24. Our AI policies (e.g. Dienstvereinbarung, acceptable use) are clear and easy to apply. [Audience: All employees]
Q25. I feel safe to report an AI‑related mistake or incident without fear of punishment (psychologische Sicherheit). [Audience: All employees]
Q26. I know how AI‑generated content must be reviewed before we send it to customers or candidates. [Audience: All employees]
Q27. I believe AI outputs are checked regularly for bias and fairness in important decisions. [Audience: All employees; HR/IT as owners]
Q28. I trust that logs and monitoring of AI use are used for improvement, not for surveillance. [Audience: All employees]
Q29. My manager talks about AI use in our team (e.g. risks, good practices, expectations). [Audience: All employees; Managers as owners]
Q30. My manager encourages me to test AI on real tasks and share what works. [Audience: All employees; Managers as owners]
Q31. HR provides clear guidance on AI in HR‑related processes (recruiting, performance, learning). [Audience: All employees; HR/IT as owners]
Q32. I know where to book or request more AI training or coaching if I need it. [Audience: All employees]
Q33. When I raise AI‑related questions, HR or IT respond with helpful, practical answers. [Audience: All employees; HR/IT as owners]
Q34. As a manager, I feel prepared to answer my team’s questions about AI. [Audience: Managers]
Q35. As HR/IT, I have the resources and mandate to support AI enablement in the business. [Audience: HR/IT]
Q36. Using AI tools saves me time on repetitive tasks. [Audience: All employees]
Q37. Using AI improves the quality of my work outputs (e.g. structure, language, insights). [Audience: All employees]
Q38. Since using AI, I feel less stressed by routine documentation and admin work. [Audience: All employees]
Q39. AI helps me focus more on high‑value, human parts of my job. [Audience: All employees]
Q40. AI‑supported decisions in my area feel transparent and explainable. [Audience: All employees]
Q41. I see a clear link between AI use and better outcomes for customers, candidates or colleagues. [Audience: All employees]
Q42. In my team, it is okay to admit when an AI‑based result was wrong. [Audience: All employees]
Q43. People in my team share tips and prompts for using AI more effectively. [Audience: All employees]
Q44. I feel that AI is a helpful addition to our work, not a threat to my job. [Audience: All employees]
Q45. I feel informed and included in AI‑related changes, not “surprised” by new tools. [Audience: All employees]
Q46. I believe our company cares about the human impact of AI (jobs, workload, wellbeing). [Audience: All employees]
Q47. Our culture supports experimentation with AI within clear boundaries. [Audience: All employees]
Q48. Overall, I am satisfied with the AI tools we currently provide. [Audience: All employees]
Q49. Overall, I am satisfied with the AI training and learning formats we offer. [Audience: All employees]
Q50. Overall, I am satisfied with the clarity of our AI policies and guidelines. [Audience: All employees]
Q51. I see a clear plan for how my role will evolve with AI in the next 1–3 years. [Audience: All employees]
Q52. I know which AI‑related skills will matter most for my career here. [Audience: All employees]
Q53. I would like more concrete examples of AI use in my specific function or profession. [Audience: All employees]

0–10 rating questions

Scale: 0 = not at all / very negative, 10 = extremely / very positive.

R1. Overall, how confident do you feel using AI tools for your daily work? (0–10) [Audience: All employees]
R2. How clear are our rules and policies for using AI (including Datenschutz)? (0–10) [Audience: All employees]
R3. How positive is the impact of AI tools on your personal productivity so far? (0–10) [Audience: All employees]
R4. How likely are you to recommend our AI enablement (tools, training, policies) to a colleague? (0–10) [Audience: All employees]

Open-ended questions

O1. In which tasks or projects would you like more AI support or training? [Audience: All employees]
O2. What is your biggest concern or fear about using AI at work? [Audience: All employees]
O3. Which AI tools or features work best for you today, and why? [Audience: All employees]
O4. Which part of our AI tools, training or policies is most confusing or unclear for you? [Audience: All employees]
O5. Describe one situation where AI clearly helped you (e.g. time saved, better result, less stress). [Audience: All employees]
O6. Describe one situation where AI created extra work, risk or frustration. [Audience: All employees]
O7. If you are a manager: what support do you need to guide your team’s AI use better? [Audience: Managers]
O8. If you work in HR or IT: what makes it hardest to support employees on AI topics today? [Audience: HR/IT]
O9. Which AI use case should we pilot next in your area, and what would success look like? [Audience: All employees]
O10. What is one thing the company should start doing to help you use AI more effectively? [Audience: All employees]
O11. What is one thing the company should stop doing or change about its current AI approach? [Audience: All employees]
O12. What is one thing we should definitely continue because it works well for AI enablement here? [Audience: All employees]

Decision table

Area / question block	Trigger (score)	Recommended action	Owner	Due by
Awareness & Understanding (Q1–Q7)	Avg <3.0 or ≥30% “1–2”	Run short info sessions; update AI FAQ; add links in intranet and MS Teams.	HR / Comms	Within 14 days after results
Skills & Confidence (Q8–Q14, R1)	Avg <3.0 or R1 <6.0	Offer role‑based labs; pair novices with AI champions; integrate into development plans.	L&D / HRBPs	Training plan within 30 days
Tools & Workflows (Q15–Q21)	Avg <3.0 or ≥30% negative comments	Map top friction points; adjust configurations; improve SSO; offer “ AI in your workflow” clinics.	IT / Digital Workplace	Action plan within 30 days
Governance, Safety & Trust (Q22–Q28, R2)	Avg <3.0 or R2 <7.0	Clarify rules in simple language; run Datenschutz Q&A; update Dienstvereinbarung summary.	Legal / Data Protection Officer / HR	Clarifications communicated in 21 days
Manager & HR Support (Q29–Q35)	Avg <3.0 in any business unit	Launch manager/HR enablement sessions; provide prompt libraries and FAQ scripts.	HR / People Development	First sessions within 21 days
Impact on Work (Q36–Q41, R3)	Avg <3.5 or R3 <6.0	Identify high‑value use cases per function; adjust training to real workflows.	AI Steering Group / Function Heads	Refined use‑case list in 30 days
Culture & Change (Q42–Q47)	Avg <3.0 or strong fear signals (Q44)	Address job‑security concerns; communicate vision; highlight safe‑to‑fail principles.	Executive team / HR	Company update within 30 days
Overall Satisfaction & Needs (Q48–Q53, R4)	R4 ≤6.0 or ≥3 weak areas	Prioritise top 2–3 themes; link to roadmap; share what will change and when.	AI Program Lead / HR	Roadmap update in 45 days
Open comments (O2, O6, serious incidents)	Critical risk, legal/privacy flags	Review anonymously; if risk is concrete, inform DPO, Betriebsrat and affected managers.	HR / DPO / Works Council	Initial review ≤24 h, action plan ≤7 days

Key takeaways

Use the survey to see where AI feels confusing, risky or useless.
Translate low scores into specific training, tool or policy changes.
Give managers concrete numbers and questions for better AI conversations.
Respect GDPR and Betriebsrat rules: anonymity, data minimisation, clear purpose.
Repeat short pulses to track if AI enablement really improves over time.

Definition & scope

This survey measures how employees, managers and HR/IT experience AI enablement: awareness of tools, skills and confidence, workflow fit, governance, support, impact and culture. It can run company‑wide or for specific pilot groups. Results support decisions on training roadmaps, AI skills matrices, tool configuration, governance updates and change management – especially in coordination with the Betriebsrat and data protection officers.

Survey blueprints: how to use these AI enablement survey questions

You rarely need all questions at once. The blueprints below help you build focused surveys that fit your timing and audience. Combine them with your existing AI enablement in HR strategy and role‑based training plans.

Blueprint	Purpose & timing	Audience	Recommended items
(a) Company‑wide AI baseline (20–25 items)	Map current state before/after first big AI training or tool rollout.	All employees, managers, HR/IT	Q1–Q7, Q8–Q11, Q15–Q18, Q22–Q25, Q29–Q33, Q36–Q39, Q42–Q44, Q48–Q50, R1–R4, O1–O4
(b) Short post‑launch pulse (10–12 items)	2–6 weeks after launching Copilot or another key AI tool.	Users of the new tool	Q1, Q3, Q8–Q10, Q15–Q18, Q22–Q24, Q36–Q37, R1, R3, O3, O5–O6
(c) Manager‑focused pulse (10–12 items)	Check if managers feel able to coach AI use and handle risks.	People managers	Q2, Q5, Q8–Q9, Q13–Q14, Q20, Q29–Q30, Q34, Q42, Q44, R1, O7
(d) AI champions / power‑user survey (15–18 items)	Understand advanced needs and best practices from heavy users.	Selected AI champions / early adopters	Q2–Q4, Q9–Q11, Q15–Q21, Q27, Q36–Q41, Q47–Q53, R1–R3, O3, O9–O12

Scoring & thresholds for your AI enablement survey questions

Most items use the 1–5 scale (Strongly disagree to Strongly agree). For analysis, treat scores <3.0 as critical, 3.0–3.9 as “needs improvement” and ≥4.0 as healthy. The 0–10 items give you simple KPIs you can track over time (e.g. R1 “confidence using AI” or R4 “AI enablement NPS”).

If any block (e.g. Skills & Confidence Q8–Q14) averages <3.0, run follow‑up sessions with that group within 14 days.
Scores 3.0–3.9 trigger improvement plans: extra training modules, clearer policy examples or workflow tweaks in the next 30 days.
Scores ≥4.0 mark strengths. Ask high‑scoring teams to share prompts, demos or simple “how we use AI” videos.
Track R1–R4 quarterly to see if confidence, clarity, productivity impact and satisfaction are moving up or stalling.
Document your thresholds (e.g. “Avg <3.0 = mandatory action”) in your AI governance/AI KPI overview.

Follow-up & responsibilities

Survey results only help if clear owners act quickly. Define in advance who handles which signals and by when. A talent platform like Sprad Growth can support by automating survey sends, reminders and follow‑up tasks across managers and HR.

HR consolidates results by business unit, highlights 3–5 priority themes and shares a short summary deck (within 10–14 days).
Managers receive their team results and run a 30–60 minute follow‑up meeting to discuss scores and ideas (within 7–14 days).
AI program lead, HR, IT and Legal review cross‑company patterns (e.g. governance scores, tool friction) and update the AI roadmap (within 30 days).
Severe data protection or safety concerns trigger immediate escalation to DPO and, where required, Betriebsrat (acknowledge ≤24 h, agreed plan ≤7 days).
HR monitors if agreed actions are completed (e.g. workshops delivered, guidelines updated) and reports completion rates in quarterly steering meetings.

Fairness & bias checks

AI enablement must be fair. That means not only checking AI models for bias, but also checking whether some groups feel less supported or more at risk. Break down results by location, function, job family, gender and remote vs. office – always respecting anonymity thresholds agreed with the Betriebsrat.

Set a minimum of ≥5 responses per subgroup before you show results to protect anonymity and meet GDPR/data‑minimisation rules.
If non‑technical teams show much lower confidence (Q8–Q11, R1) than IT or product, design tailored examples and “no jargon” training for them.
If one location has lower trust in data protection (Q22–Q25) than others, review how you communicated the Dienstvereinbarung and DPIA there.
Compare psychological safety items (Q25, Q42) across groups; low scores in any subgroup should lead to targeted dialogue and manager coaching.
When AI‑supported HR decisions are in scope, link this survey with your performance review survey questions to spot fairness issues early.

Examples / use cases

Example 1 – Low confidence despite many trainings

A medium‑sized services company had already run several AI keynotes and hands‑on sessions. The baseline survey showed high awareness (Q1–Q4 >4.0) but low confidence (Q8–Q11 ≈2.7) and weak impact on work (Q36–Q39 ≈3.0). Many comments said, “Nice demos, but not for my role.”

HR and the AI program lead used this insight to redesign training into role‑based labs. They built simple AI skills matrices per function using their existing skill matrix templates, then created three concrete workflows per role. After 3 months, a short pulse survey showed confidence (R1) up by 2 points and more staff reported real time savings.

Example 2 – Governance fears block adoption

In another organisation, Copilot had been technically rolled out, but usage stayed low. The pulse survey revealed good scores for tools and workflows (Q15–Q18 ≈4.0) but weak trust and safety (Q22–Q25 ≈2.8). Comments mentioned fear of GDPR breaches, unclear rules and “Big Brother” monitoring.

Legal, the DPO and HR created a one‑page “Do/Don’t” for AI use, simplified the Dienstvereinbarung summary and ran short Datenschutz Q&A sessions. They clarified logging, retention periods and that survey results would never be used for individual sanctions. A second pulse after 8 weeks showed trust scores above 3.8 and Copilot usage doubled.

Example 3 – Managers feel lost in AI coaching

In a DACH manufacturing company, individual contributors felt curious about AI, but manager‑only questions (Q34, O7) showed many leaders felt unprepared. Comments said, “My team asks things I can’t answer” and “I’m afraid of promising too much.”

HR set up an “AI for managers” track based on their AI training for HR teams and a 6‑week manager cohort program. They practised prompt reviews, risk scenarios and how to embed AI topics into 1:1s. In the next pulse, manager confidence (Q34, R1 for managers) rose to ≥4.0 and teams reported more structured AI experiments.

Implementation & updates

Think of this survey as part of your AI enablement cycle, not a one‑off project. Align timing with major AI milestones: before big rollouts (baseline), 4–8 weeks after (pulse), then annually for a deeper review. In DACH, involve the Betriebsrat early, clarify the legal basis (e.g. consent vs. legitimate interest), data minimisation, retention periods and who can see which results.

Pilot the survey with a small, mixed group (e.g. HR, IT, one business unit) to test clarity and length; adjust Qs before company‑wide rollout.
Agree anonymity rules, data retention (e.g. delete raw data after 12–24 months) and purpose limitation with DPO and Betriebsrat, and document them in your Dienstvereinbarung.
Combine survey insights with your AI training programs for companies roadmap and your skill gap analyses.
Train managers to discuss results in regular 1:1s and team meetings; link AI development goals to existing IDP or performance processes.
Review the question set annually: remove items that are always “green”, add new items for new tools or AI governance topics (e.g. EU AI Act).

Practical KPIs you can track over time include: survey participation rate (aim ≥60–70%), average scores per block, share of teams below the action threshold (<3.0), R1–R4 trends, and action completion rates after each survey wave. You can also connect this to your talent dashboards or AI capability views, especially if you already use skill management software to track AI‑related skills.

Conclusion

AI enablement is less about the latest tool and more about whether people feel informed, safe and capable when using AI. This survey gives you an honest picture: where awareness is missing, where skills are thin, where governance feels scary and where AI already helps. You catch issues months earlier than you would through informal complaints.

The second benefit is better conversations. Clear AI enablement survey questions give managers and HR a neutral starting point to talk about fears, productivity and fairness instead of assumptions. That improves psychological safety, especially when AI mistakes or near‑misses happen. And third, you get hard numbers that help prioritise: which training to build next, which policy paragraph to simplify, which tool integration to fix first.

Concrete next steps: choose a pilot area, assemble a short baseline survey using one of the blueprints, and set owners plus deadlines for follow‑up. Load the survey into your existing engagement platform or an HR tool and prepare simple communication that explains purpose, anonymity and how results will be used. Once the first cycle is complete, adjust training, tools and governance – and plan your next pulse to see if AI enablement is truly moving in the right direction.

Frequently asked questions

How often should we run this AI enablement survey?
Most organisations start with a full baseline once per year and add shorter pulses after major events, for example 4–8 weeks after a Copilot rollout or a new AI policy. If AI adoption is a strategic priority, quarterly pulses with 8–12 items keep you close to reality without overloading staff. Keep the cadence predictable and always communicate what changed since the last survey.

What should we do if some teams have very low scores?
Low scores (avg <3.0) are early warning signals, not reasons to blame individuals. Start with a follow‑up conversation: ask what is behind the numbers and which 1–2 actions would help most. Then adjust training, workflows or communication and agree a clear timeline. If scores stay low over several cycles, escalate to the AI steering group or leadership and re‑check whether your overall AI strategy fits this team’s reality.

How do we handle very critical or sensitive comments?
Set up a clear triage: HR or a small trusted group reviews open text answers, anonymises them for wider sharing and flags potential incidents (e.g. data leakage, harassment, extreme stress). For real risk cases, involve DPO, Legal and the Betriebsrat. Communicate back in general terms (“We heard concerns about X, here is what we will do”) so employees see that critical feedback leads to action, not punishment.

How do we involve managers and employees so they trust the survey?
Before launching, explain why you run the survey, how data is protected, who sees what and how long data will be kept. Clarify that results are used to improve tools, training and policies, not to control individual performance. In DACH, share your concept with the Betriebsrat and, if needed, adapt the Dienstvereinbarung. Train managers to present results neutrally, invite discussion and agree 1–3 realistic actions together with their teams.

Where can we find benchmarks or inspiration for AI enablement?
Public benchmarks for AI adoption change quickly, but high‑level data from sources like the McKinsey State of AI report can frame discussions with leadership. For concrete practice, combine this survey with your internal AI training programs, AI workshops and skills matrices to build your own baseline. Over time, your best benchmark is yourself: compare scores, participation and impact after each wave and refine your AI enablement roadmap accordingly.

Jürgen Ulbrich

CEO & Co-Founder of Sprad

Jürgen Ulbrich has more than a decade of experience in developing and leading high-performing teams and companies. As an expert in employee referral programs as well as feedback and performance processes, Jürgen has helped over 100 organizations optimize their talent acquisition and development strategies.