What employees should tell you about AI at work
Tell your organisation which AI tools you use, what work and data they touch, where outputs need checking and what guidance would make your work safer. Do not paste client material or secrets into the feedback form.
The source question for this draft came from: AI governance guidance for UK SMEs. For smaller regulated or professional-services teams, the point is practical: you need enough candour to govern real AI use, not a policy that only describes approved tools on paper.
The best answer is honest evidence of use, risk, and support needed
Ask staff which AI tools help with their work, which they use unofficially and where they are unsure about data, accuracy or client impact. Record the tools, affected workflows, sensitive data risks and training gaps.
A good response might say: “We use AI to summarise meeting notes and draft internal documents, but people are unsure whether client data can be pasted into public tools. We need approved tools, examples of safe use, and a simple review process for anything client-facing.”
Find the AI use your policy is currently missing
What leaders should listen for
Look for patterns in where employees experiment, where they are blocked and where they make judgement calls without guidance. Compare those reports with the written policy.
- Shadow AI: teams using personal accounts or unapproved tools because approved options are slow or unclear.
- Data uncertainty: staff unsure what client, employee, or commercial data can be entered into AI systems.
- Quality concerns: outputs that look polished but need checking for facts, context, bias, or regulatory fit.
- Workflow pressure: managers expecting AI productivity gains without changing review, workload, or accountability.
- Training gaps: employees wanting practical examples rather than abstract policy language.
How to turn feedback into governance
Group responses into approved, risky, banned and unclear use cases. Assign an owner to each unclear workflow to decide whether it needs a tool change, training, a review step or prohibition.
Give staff a safe route to report existing use and unanswered questions. Otherwise, feedback may describe only approved practice and leave unofficial use unreported.
Create an AI governance loop that staff will actually use
The minimum evidence to keep
Keep a lightweight record of what was reported, what decision was made, and who owns the follow-up. For example: “marketing uses AI for first-draft blog outlines; no client data entered; human review required before publication; owner: marketing lead; next review: quarterly.”
Use the record to follow up on reported use and check whether AI saves work, shifts risk or adds review work elsewhere.
A practical question set for SMEs
Ask five follow-up questions if the answers lack detail. Which AI tools are you using? What data do you enter? What work does AI influence before a client, customer, or colleague sees it? What are you unsure about? What would make safe use easier?
Explain that the questions are intended to improve tools and guidance, not to catch people out. Use the answers to identify where staff need approved options, training or review support.
Build practical AI controls into day-to-day workflows
Conclusion
Collect reports of actual AI use, including useful workflows, risks and gaps in the policy. Assign follow-up owners so the reports lead to decisions about tools, guidance and review.
Want to find the practical AI opportunity?
If this topic is part of a real workflow decision, we can help you identify where AI will save time, where it creates risk, and what needs governance before rollout.