AI adoption and governance
What should an employee AI policy include?
Give people clear, usable boundaries for everyday AI use without pretending a policy can replace judgement.
State the purpose and scope
Explain why the business supports useful AI use, which people and systems are covered and how the policy relates to existing obligations. This matters because employee AI policy decisions rarely fail through a lack of possible technology. They fail when the business problem, operating context and responsibility for the outcome remain implicit. Bring evidence from the people doing the work, the systems supporting it and the leaders accountable for the result. Test assumptions about time, behaviour, data quality and implementation effort before treating them as facts. People follow boundaries more reliably when they understand the business reason behind them. Record the choice, the evidence still required and the person who will return with it. Keep the mechanism proportionate: the purpose is better judgement and follow-through, not additional ceremony.
Name approved and prohibited use
Provide examples of safe drafting, research or summarisation and examples that require permission or must not occur. This matters because employee AI policy decisions rarely fail through a lack of possible technology. They fail when the business problem, operating context and responsibility for the outcome remain implicit. Bring evidence from the people doing the work, the systems supporting it and the leaders accountable for the result. Test assumptions about time, behaviour, data quality and implementation effort before treating them as facts. Avoid language so broad that employees either stop experimenting or ignore it. Record the choice, the evidence still required and the person who will return with it. Bring specialist judgement into the decision where required while retaining business ownership of the outcome.
Protect information and rights
Set rules for personal, confidential, client, commercially sensitive and copyrighted material, including supplier terms and retention. This matters because employee AI policy decisions rarely fail through a lack of possible technology. They fail when the business problem, operating context and responsibility for the outcome remain implicit. Bring evidence from the people doing the work, the systems supporting it and the leaders accountable for the result. Test assumptions about time, behaviour, data quality and implementation effort before treating them as facts. Where the answer depends on law, contract or regulation, route the question to qualified specialists. Record the choice, the evidence still required and the person who will return with it. Keep the mechanism proportionate: the purpose is better judgement and follow-through, not additional ceremony.
Require meaningful human review
Describe what users must check for accuracy, bias, completeness, confidentiality and suitability before relying on an output. This matters because employee AI policy decisions rarely fail through a lack of possible technology. They fail when the business problem, operating context and responsibility for the outcome remain implicit. Bring evidence from the people doing the work, the systems supporting it and the leaders accountable for the result. Test assumptions about time, behaviour, data quality and implementation effort before treating them as facts. Responsibility does not transfer to the tool because the response looked confident. Record the choice, the evidence still required and the person who will return with it. Bring specialist judgement into the decision where required while retaining business ownership of the outcome.
Create disclosure and record rules
Clarify when AI use should be visible to customers, colleagues or decision-makers and what evidence must be retained. This matters because employee AI policy decisions rarely fail through a lack of possible technology. They fail when the business problem, operating context and responsibility for the outcome remain implicit. Bring evidence from the people doing the work, the systems supporting it and the leaders accountable for the result. Test assumptions about time, behaviour, data quality and implementation effort before treating them as facts. Focus disclosure on trust and consequence rather than labelling every minor assist. Record the choice, the evidence still required and the person who will return with it. Keep the mechanism proportionate: the purpose is better judgement and follow-through, not additional ceremony.
Keep the policy alive
Give employees a contact route, short training, regular examples and a process for updating approved tools and emerging risks. This matters because employee AI policy decisions rarely fail through a lack of possible technology. They fail when the business problem, operating context and responsibility for the outcome remain implicit. Bring evidence from the people doing the work, the systems supporting it and the leaders accountable for the result. Test assumptions about time, behaviour, data quality and implementation effort before treating them as facts. A policy stored in an intranet folder is documentation, not governance. Record the choice, the evidence still required and the person who will return with it. Bring specialist judgement into the decision where required while retaining business ownership of the outcome.
What to carry into the work
- People follow boundaries more reliably when they understand the business reason behind them.
- Avoid language so broad that employees either stop experimenting or ignore it.
- Where the answer depends on law, contract or regulation, route the question to qualified specialists.
- Responsibility does not transfer to the tool because the response looked confident.