Key Takeaway
Every organisation using AI needs a governance policy. It does not need to be complex — this practical template covers the five essential areas: approved tools, data classification, usage guidelines, accountability, and review processes.
Why Every Organisation Needs an AI Policy
Without a clear AI policy, employees either avoid AI out of uncertainty or use it recklessly with sensitive data. Both outcomes are bad. A policy does not restrict AI use — it enables confident, responsible adoption.
The best AI policies are short, clear, and focused on enabling rather than restricting. They answer the questions employees actually have: “Can I use this?” “What data can I share?” “Do I need to tell anyone?”
Section 1: Approved AI Tools
The following AI tools are approved for business use: [list tools].
Enterprise accounts have been provisioned for: [list].
Personal accounts on approved tools may be used for non-confidential work.
Tools not on this list require approval from [role] before use with any company data.
Keeping the approved list short (2–3 tools) reduces confusion and allows you to focus training and support resources effectively.
Section 2: Data Classification
Data classification determines what information can be shared with AI tools. A simple three-tier system works for most organisations:
- Tier 1 — Public: Information already publicly available. Can be used with any approved AI tool freely.
- Tier 2 — Internal: Internal documents, processes, and non-sensitive business data. Can be used with enterprise AI accounts that have data protection agreements in place.
- Tier 3 — Restricted: Personal data, financial records, trade secrets, legal privileged information. Must not be shared with external AI tools without explicit approval and appropriate safeguards.
Most day-to-day AI use falls into Tiers 1 and 2. Clear classification prevents both over-caution (avoiding AI entirely) and recklessness (pasting customer data into free AI tools).
Section 3: Usage Guidelines
• AI outputs must be reviewed by a human before being used in customer-facing, financial, or legal contexts.
• AI-generated content should be clearly attributed when used externally, where applicable.
• Employees should not rely on AI for factual accuracy without verification.
• AI should augment professional judgment, not replace it.
Usage guidelines should be practical and specific. Avoid abstract principles — focus on concrete scenarios employees actually encounter.
Section 4: Accountability
The most important governance principle: the person who uses AI is responsible for the output. AI does not shift accountability — it shifts methodology.
If a financial report contains an error because the analyst did not verify AI-generated figures, the analyst is accountable — not the AI tool. This should be stated explicitly in the policy to prevent “the AI told me to” becoming an excuse for poor quality work.
Managers should review AI-assisted work with the same rigour they apply to any other work product. The standard is the output quality, regardless of how it was produced.
Section 5: Regular Review
AI capabilities evolve rapidly. A policy written today may be outdated in six months. Build in a regular review cycle:
- Quarterly review of approved tools list
- Six-monthly review of data classification tiers
- Annual comprehensive policy review
- Ad-hoc reviews when significant new AI capabilities emerge
Assign a specific person or committee to own the review process. Without clear ownership, policies become stale and ignored.
Want to Go Deeper?
AI governance and responsible use are core topics in the AI for Corporate Teams course, with customisable policy templates included.
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