Most AI policies fail because they are either too vague or too restrictive. Here’s how I’d build one that protects the business, gives the team clarity, and still leaves room for useful experimentation.
AI use at work is already happening, whether leadership has written a policy or not. I’ve seen it with small teams and growing companies alike: someone uses ChatGPT to draft emails, another person uses an image tool for social media, and suddenly sensitive information is floating around in places nobody has really approved.
That is why I think a practical AI use policy matters. Not a legal-looking document that sits in a folder untouched, but a simple working agreement your team can actually understand and apply on a busy Tuesday.
If I were building one for a business in Toulouse, I would keep it short, specific, and tied to real tasks the team already does.
Start with the real goal
The first mistake I see is writing a policy focused only on risk. Yes, risk matters. But if the document reads like “don’t use AI,” people will either ignore it or hide their usage.
I prefer to start with a clear statement of purpose: why are we allowing AI at all?
Usually the answer is something like this: we want to save time on repetitive work, improve first drafts, and help the team move faster without compromising confidentiality, accuracy, or brand standards.
That framing changes everything. It tells the team AI is a tool, not a replacement for judgment.
Define what AI can and cannot be used for
This is where the policy becomes useful. I would split usage into three simple categories.
Allowed uses: brainstorming ideas, drafting outlines, summarising public information, rewriting internal notes, generating first versions of social posts, or creating simple visual concepts in Canva Pro.
Restricted uses: anything involving customer data, financial information, employee records, private contracts, pricing strategy, or unpublished business plans. These might require manager approval or approved tools only.
Prohibited uses: entering confidential data into public AI tools, publishing AI-generated content without human review, creating fake testimonials, impersonating staff, or using AI to make decisions about hiring, discipline, or legal matters without human oversight.
A team does not need twenty pages here. It needs examples that match daily work.
Be specific about data and confidentiality
This is the part I would make non-negotiable.
Your policy should explain exactly what cannot be pasted into AI tools: client names, contact details, invoices, health data, employee information, passwords, internal reports, and any private documents.
For local businesses in Occitanie, this matters even more because teams are often small and people wear multiple hats. The same person might handle marketing in the morning and customer support in the afternoon. Without a rule, it becomes very easy to drop sensitive information into an AI prompt “just to save time.”
I would include a simple test: if you would not post it publicly on your website, do not put it into a public AI tool.
If your team needs analytics summaries or reporting support, use privacy-conscious systems where possible. For example, if you want cleaner web reporting without invasive tracking, Fathom Analytics is a sensible option for businesses that care about GDPR-friendly measurement.
Assign human responsibility
AI should assist work, not own it.
A good policy makes one thing very clear: the person using AI remains responsible for the final output. That means checking facts, reviewing tone, verifying numbers, and making sure the result reflects the company’s standards.
I would write this plainly: AI can help create a draft, but a human must approve anything that goes to clients, the public, or leadership.
This sounds obvious, but it avoids a lot of future problems. Teams need to know they cannot say, “the tool wrote it” after a mistake.
Create a short approval process
Not every use case needs permission. But some absolutely do.
I recommend defining when approval is required. For example: using a new AI tool, uploading files, generating customer-facing legal or financial content, or automating part of a workflow.
A simple manager sign-off is often enough for a small business.
Let’s take a fictional example from Toulouse. Imagine La Boulangerie du Capitole has a five-person team. The owner is happy for staff to use AI to brainstorm Instagram captions and write first drafts for seasonal promotions. But if someone wants to upload customer order history into a tool to “predict demand,” that should need approval first. Same business, same team, different level of risk.
That is what a practical policy does: it draws a clean line between low-risk help and high-risk experimentation.
Keep a list of approved tools
This saves a surprising amount of confusion.
Your policy should name the tools your team is allowed to use, what they are for, and any limits. If you do not do this, people will choose random apps they found in a hurry.
I like to keep this list lightweight: tool name, approved purpose, owner, and whether sensitive data is allowed. If your team builds landing pages or campaign pages, you might approve Framer for web content production while still forbidding confidential data uploads into unrelated AI services.
The point is not to control everything. The point is to reduce chaos.
Train the team with examples, not jargon
A policy only works if people understand it. So I would avoid technical language wherever possible.
Instead, I would give examples:
- “Yes, you can use AI to rewrite a product description draft.”
- “No, you cannot paste a customer complaint thread into a public tool.”
- “Yes, you can ask for headline ideas.”
- “No, you cannot publish AI-written advice without checking it.”
For business owners around Toulouse, from Compans to Saint-Cyprien or over in Blagnac and Colomiers, this practical format works better than abstract rules because teams are busy and often not technical.
Review the policy every few months
AI changes fast. A policy written once and forgotten will age badly.
I would review it every quarter, even if only for 20 minutes. Ask: what tools are people actually using? Where did confusion happen? What needs clarification? Did any task become safe enough to allow, or risky enough to restrict?
That keeps the policy grounded in reality.
My simple rule
If you want your AI policy to work, make it usable. Keep it short, honest, and tied to real work. Protect confidential data, define responsibility, approve tools intentionally, and give the team room to benefit from AI without guessing where the boundaries are.
That is the version people will actually follow. And in my experience, that is far better than a perfect policy nobody reads.
