You know you should be governing AI usage. But between running the business and keeping clients happy, implementing a "comprehensive AI governance framework" sounds like a project you'll never get to. Good news: you don't need the enterprise playbook. Here's a practical approach that works for growing businesses.
Enterprise companies throw consultants and million-dollar platforms at this problem. You don't have that luxury, and honestly, you don't need it. What works for a 10,000-person bank is overkill for a 50-person agency. The principles matter, but the implementation needs to fit your reality.
Here's a framework you can actually use.
Step 1: Get visibility before policies
Most governance advice starts with "create an AI usage policy." That's backwards. You can't write sensible rules for something you don't understand. Start by seeing what's actually happening.
Ask yourself: Which AI tools are your team using? How often? For what kinds of work? Which teams are power users, and which haven't adopted AI at all?
If you can't answer these questions, that's your starting point. Before any policies or training, get visibility. Even a simple survey helps, though automated tools give you ongoing insight rather than a one-time snapshot.
This isn't about catching people doing something wrong. It's about understanding your organisation's AI reality so you can make informed decisions. You might discover your sales team is using AI heavily for proposals (great, help them do it better) while your ops team hasn't touched it (worth exploring why).
Step 2: Understand your baseline
Once you have visibility, spend a few weeks just observing. Don't jump to conclusions or immediate action. Let the data tell you what's normal for your organisation.
You're looking for patterns:
Which platforms dominate? If everyone's on ChatGPT, that's where your guidance should focus first. If you're seeing five different tools, you might want to standardise, or you might learn that different teams have different needs.
What types of work involve AI? Drafting client emails is different from analysing confidential financials. Understanding the use cases helps you calibrate your response.
When does usage spike? Maybe it's proposal season, or maybe Mondays are heavy because people are catching up. Patterns reveal how AI fits into your workflow.
The point is context. A hundred AI interactions per day might be concerning for a five-person team or completely normal for a fifty-person one. Your baseline is your baseline. Don't compare yourself to industry averages that may not apply.
Step 3: Focus on what actually matters
Here's where most governance approaches go wrong: they try to control everything. That's exhausting, breeds resentment, and misses the point.
Not all AI usage carries the same risk. Someone using ChatGPT to brainstorm blog post ideas is different from someone pasting a client's financial statements into Claude. Your governance effort should reflect that.
Think about it in tiers:
Low concern: General research, brainstorming, drafting public-facing content, learning and experimentation. This is AI working as intended. Don't create friction here.
Worth watching: Client names mentioned in prompts, internal project details, competitive information. Not necessarily wrong, but worth understanding patterns.
Needs attention: Financial data, personal information, credentials and passwords, confidential documents. This is where you want active awareness and clear guidance.
The goal isn't to eliminate all risk. It's to focus your limited attention on what matters most. Let the low-concern stuff flow. Watch the middle tier for patterns. Put your governance energy into the high-stakes scenarios.
Step 4: Set principles, not rules
Detailed rules sound good but fail in practice. "Never paste more than 500 words into an AI tool" is specific but useless, and no one will follow it anyway.
Principles work better because they guide judgement rather than trying to anticipate every scenario. Here's an example set:
Treat AI like a smart stranger. Would you share this information with a knowledgeable person you just met at a conference? If not, don't share it with AI.
Strip identifying details when possible. "How should I handle a client who's unhappy about pricing?" is fine. Including the client's name adds risk without adding value.
Don't paste credentials. Ever. This one can be a rule. API keys, passwords, access tokens: never. There's no scenario where this makes sense.
When in doubt, ask. Create a low-friction way for people to check. A Slack channel, a quick email, whatever fits your culture. Make asking easy, not embarrassing.
Four principles are easier to remember than forty rules. Train to the principles, give examples, and trust your team to apply judgement. They're professionals. Treat them like it.
Step 5: Create coaching moments, not punishments
This is important and where most governance goes wrong: the point isn't to catch and punish. It's to help people get better.
When your visibility shows someone doing something risky, the response matters. "Sarah pasted client financials into ChatGPT, let's write her up" creates fear. People will just hide their AI usage, which makes everything worse.
Better approach: "Hey Sarah, I noticed you were working on the Henderson financials with AI. Totally get it, those projections are complex. Quick thought: if you strip out the company name and identifying numbers, you get the same analytical help without the exposure. Want me to show you how I'd approach it?"
That's a coaching moment. Sarah learns something useful, feels supported rather than surveilled, and will probably share the tip with colleagues. Win all around.
The visibility you've built isn't for punishment. It's for improvement. When people understand that, they're more likely to use AI openly (which you want) and ask questions when unsure (which you really want).
Step 6: Build your evidence base
At some point, someone will ask about your AI governance. Could be a client in due diligence. Could be an auditor. Could be your board or investors. Having an answer matters.
You don't need a hundred-page policy document. You need evidence that you've thought about this and are managing it actively. That means:
Documentation of your approach. A one-pager explaining your principles and how you monitor usage. Doesn't need to be fancy. Clarity beats polish.
Proof you have visibility. Analytics showing you know what's happening. "Our team makes approximately 800 AI interactions per week, primarily using ChatGPT and Claude for drafting and research" sounds very different from "we trust our employees."
Evidence of ongoing attention. Meeting notes from quarterly reviews. Records of training sessions. Examples of guidance you've provided. Anything showing this is active management, not a one-time checkbox.
Audit trail for sensitive situations. When high-risk scenarios occur, how they were handled. Not every interaction (that's overkill) but the ones that mattered.
Build this evidence as you go rather than scrambling when someone asks. Ten minutes of documentation weekly beats a panicked weekend before an audit.
Making it stick
The best governance framework is one that actually gets used. A few thoughts on making this sustainable:
Start small. You don't need everything on day one. Get visibility first. Add principles. Build the evidence base over time. Iterating beats perfecting.
Make it someone's job. Doesn't need to be full-time, but someone should own this. Check the dashboard weekly. Follow up on patterns. Keep documentation current. Unowned initiatives die.
Celebrate good usage. When someone uses AI cleverly for a project, share it. When a team's productivity jumps because they've adopted AI well, acknowledge it. Governance shouldn't only be about problems.
Review quarterly. AI tools change fast. Your team's usage evolves. What made sense six months ago might need updating. Build in a rhythm of review and adjustment.
The bottom line
AI governance for SMEs isn't about matching what enterprises do. It's about having appropriate visibility, sensible principles, and evidence you're paying attention. You can build this without a huge budget or dedicated team.
The companies that get this right will use AI more confidently, win clients who care about governance, and avoid the painful scramble when regulations tighten or incidents occur. That's worth a few hours of setup and a few minutes of ongoing attention each week.
Start with visibility. The rest follows from there.