Creating an AI Usage Policy — What to Allow and What to Prohibit

Your employees are already using AI. The problem is that you don't know it.

Without a Policy, AI Goes to Extremes

We see two situations play out.

On one side, employees are pasting customer data into ChatGPT to generate reports. Nobody told them to, but it's convenient. The company doesn't know.

On the other, a vague "don't use AI" atmosphere means nobody touches it. Competitors have cut customer response times in half with AI, but your team is stuck in old workflows.

Both cases stem from the same root cause: no policy exists.

A policy isn't a prohibition list. It's a boundary line that says "this far is free, beyond here get approval." Boundaries create freedom — people move confidently when they know where the edges are.

Three Tiers Is All You Need

Don't overcomplicate this. Divide AI usage into three zones.

Free zone: No approval needed. General work assistance, public information tasks, internal brainstorming.

Review zone: Team lead or designated approver required. Work involving customer data, externally published content, decision support.

Prohibited zone: Not allowed. Personal identifiers (SSN, medical records), raw contracts, unreleased financial data, trade secrets.

The boundaries between these zones differ by company. What matters is that boundaries exist and everyone knows them.

Data Classification Is the Core

Ultimately it's about "what can we feed into AI?"

Public data: Already on the web, general industry knowledge. Free to use with any AI.

Internal data: Company documents, meeting notes, process descriptions. Usable with enterprise AI plans (where data isn't used for training).

Sensitive data: Customer PII, contract terms, financial figures. Only with approval or on-premise/private AI.

Classified data: Unreleased M&A, core IP, legal dispute materials. No AI usage permitted.

This is a practical application of the principles covered in our security and privacy post.

The Policy Document: One Page

One A4 page, or one wiki page. If it's longer, nobody reads it.

What to include:

Purpose: "This policy enables safe and active AI usage." Make clear that restriction isn't the goal.

Approved tools list: AI tools the company officially endorses. Example: "ChatGPT Team, Claude Pro, Copilot are approved. Free tiers carry data training risks — don't use them for work."

Three-tier table: Free/Review/Prohibited zones with examples.

Violation procedure: Learning-oriented rather than punitive — "report → impact assessment → improvement."

Review cycle: Quarterly review. AI evolves fast, so the policy must stay alive.

Three Common Mistakes

Mistake 1: Starting too strict. "All AI use requires prior approval." Nobody will use it. Start with a wide free zone and narrow only if problems arise.

Mistake 2: Creating the policy but not training on it. Document distribution alone isn't enough. As we discussed in the training and onboarding post, a 30-minute explanation session is necessary.

Mistake 3: Allowing no exceptions. Real work often falls between zones. You need a channel for "when in doubt, ask." Questions themselves are data — when the same question repeats, refine the policy.

The Real Effect of a Policy

A good AI policy doesn't restrict usage — it accelerates it.

When "this much is okay" is clear, people experiment actively within those boundaries. The vague anxiety of "am I allowed to use this?" disappears.

For AI to become organizational culture, experimentation must feel safe. The policy is the fence that creates that safety.

Just as children play freely when there's a fence around the playground, employees use AI freely when there's a policy in place.