What should an AI policy include?
An AI policy should cover permitted use cases, data privacy rules, quality control, human oversight, and accountability. It sets clear boundaries on which tools your team may use, how sensitive data is handled, who is responsible for reviewing AI output, and what happens when something goes wrong. A concise written policy protects your business and gives your team the confidence to work with AI responsibly.
Why most businesses need a written AI policy right now
You may already have staff using ChatGPT, Copilot or other AI tools in their daily work, often without a shared agreement on what is and isn't acceptable. That gap creates real risks: confidential client data pasted into a public model, AI-generated text published without a human check, or liability questions no one has answered. A clear policy closes those gaps before they become problems.
The core elements your policy should address
Permitted tools and use cases. Name the tools your team is allowed to use and for which tasks. Drafting emails is different from generating legal summaries or processing personal data. Be specific so people don't have to guess.
Data handling and privacy. Define what counts as sensitive or confidential data and state explicitly that it may not be entered into external AI systems unless those systems meet your privacy requirements. For businesses in the EU, this helps you meet the GDPR.
Human oversight and quality control. Require that all AI output is reviewed by a responsible person before it is shared externally or acted upon. State who is accountable for that check and what standard the output must meet.
Accuracy and source verification. AI tools can produce confident-sounding errors. Your policy should require fact-checking for any factual claim, especially in client-facing or legal contexts.
Accountability and what happens when things go wrong
Roles and responsibilities. Assign a named role (not a person's name, but a function) that owns the AI policy and keeps it up to date. AI tools evolve quickly; your policy should too.
Incident handling. Describe what your team should do if AI produces harmful, biased or incorrect output that has already been used. A simple escalation path is enough at this stage.
Training requirements. Specify whether team members need to complete any training before using AI tools in their work. At Stimpy we offer the Stimpy Academy for exactly this purpose: practical AI training that gives teams a shared baseline and the skills to apply AI responsibly.
Keeping your policy practical and usable
A policy no one reads helps no one. Keep it short, written in plain language, and easy to find. Review it every six months to a year, and whenever a major new tool enters your workflow. A one-page summary pinned in your team's main communication channel often does more than a lengthy document stored in a forgotten folder.
Follow-up questions
Yes, even a one-page document is enough to start. As soon as a team member uses an AI tool for work, questions about data, quality and accountability arise. A brief written policy gives everyone a shared answer and reduces the risk of costly mistakes.
Ownership belongs with a senior role that combines operational knowledge and some understanding of legal obligations, often a manager or operations lead. You don't need a legal team to start. A practical draft reviewed by your legal advisor is a solid approach for most small and mid-sized businesses.
Every six months to a year, and in any case whenever a significant new AI tool enters your workflow or relevant legislation changes. Assign one role the responsibility to trigger that review so it doesn't get skipped.