AI governance can feel overwhelming. The frameworks designed by organisations like the OECD, NIST, and the European Union are comprehensive. They're also built for enterprises with dedicated compliance departments, legal teams, and six-figure budgets. If you're running a company of 20 to 200 people, you need something more practical.
Here are the five principles that matter most for SMEs, stripped of the complexity.
1. Visibility: Know what's happening
You can't govern what you can't see.
The first principle is understanding how AI is being used across your organisation. Not assumptions. Not policies that might or might not be followed. Actual visibility.
This means knowing which AI tools your team uses. ChatGPT, Claude, Perplexity, Gemini, and the others that seem to launch every week. It means understanding how frequently they're used, which teams rely on them most, and what types of information are being shared. Usage patterns change over time, so you need ongoing visibility rather than a one-off audit.
For enterprises, this involves complex data loss prevention systems and security operations centres. For SMEs, it can be as simple as browser-based monitoring that shows you AI usage across your team.
According to IBM research, over a third of employees share sensitive work information with AI tools without permission. That's probably happening in your organisation right now. Visibility is the first step to doing something about it.
Start with a simple audit. Ask your team (anonymously if needed) what AI tools they use and why. You'll likely be surprised by the answers. Then implement ongoing monitoring so you're not relying on self-reporting.
2. Transparency: Make the rules clear
People can't follow policies they don't know exist.
Transparency in AI governance means being explicit about what's allowed, what's not, and why. It also means being open about how you're monitoring AI usage.
Picture a new marketing hire on their first week. They want to use ChatGPT to draft some social posts. Is that allowed? What about uploading a competitor's brochure for analysis? Or pasting in last quarter's sales figures to help write a report? Without clear guidance, they'll either guess wrong or ask five different people and get five different answers.
A transparent approach gives them a one-page policy they can actually read. It lists approved tools, explains what data should never go into AI platforms, and describes how the company monitors usage. When policies change, people hear about it in team meetings rather than discovering it when something gets blocked.
Most shadow AI isn't malicious. People use AI tools because they want to work faster, and they don't realise they're creating risk. When you're transparent about the risks and the rules, compliance improves dramatically.
Your first step: write that one-page policy. Keep it simple. List the approved tools. List the data types that should never be shared. Explain that you monitor AI usage for security purposes. Then actually talk about it.
3. Accountability: Someone owns this
In many SMEs, AI governance is everyone's job and therefore no one's job.
Accountability means assigning clear ownership for AI-related decisions and risks. It doesn't require a dedicated Chief AI Officer. It does require someone being responsible.
You need a named person who owns AI policy decisions and updates them when new tools emerge or regulations change. You need a clear escalation path when AI-related incidents occur. And you need someone reviewing usage data regularly, spotting when patterns shift, and staying current on regulations relevant to your industry.
Without this, policies drift. Nobody updates them. Nobody reviews the monitoring data. Nobody notices when usage patterns change. Six months later, your governance exists only on paper.
Where to start
Assign AI governance to someone on your leadership team. In a smaller company, this might be the CEO, CTO, or head of operations. In a larger SME, it might be a dedicated compliance or IT role. The title matters less than the clarity. Pick someone, make it official, and give them time to actually do it.
4. Proportionality: Match response to risk
Not all AI usage carries the same risk. Your governance should reflect that.
The EU AI Act formalised this concept with its risk categories: minimal, limited, high, and unacceptable. But you don't need a regulatory framework to apply proportional thinking.
Think about it in three tiers. Low-risk usage, like brainstorming ideas or general research, needs minimal oversight. Let people work. Medium-risk usage involves company information that isn't confidential, like publicly available content or general business processes. Here you might require approved tools only. High-risk usage touches customer data, financial information, proprietary code, or legal documents. This needs strict protocols, possibly approval workflows.
If you treat all AI usage the same, you either over-govern (blocking harmless productivity gains) or under-govern (missing the actually risky behaviour). A blanket ban on AI treats a marketing brainstorm the same as uploading client contracts. That's not governance. That's just restriction.
To set your tiers, ask three questions. What data would be catastrophic to leak? What would be problematic but manageable? What doesn't really matter? Then match your policies and monitoring intensity to those answers.
5. Enablement: Governance that helps people work
This is where most AI governance frameworks fail SMEs.
Enterprise frameworks are often built around restriction and control. They assume dedicated teams to manage complexity and budgets to absorb friction. SMEs can't afford governance that slows people down.
Good governance provides approved AI tools rather than just banning unapproved ones. It makes the safe path also the easy path. It intervenes at the moment of risk rather than sending an email three days later. And it gives employees information and choice rather than just blocking them.
Your team uses AI because it makes them more productive. If your governance removes that productivity, people will find workarounds. You'll end up with shadow AI plus resentment plus less visibility. That's worse than where you started.
The alternative test
For every AI restriction you implement, ask yourself: what alternative am I providing? If you're telling people not to use ChatGPT with customer data, what should they use instead? If you're blocking file uploads to AI tools, how should they get help with document analysis? If the answer is "nothing," you haven't solved the problem. You've just moved it underground.
How these principles work together
These five principles aren't separate initiatives. They reinforce each other.
Visibility tells you what's happening. Transparency makes the rules clear. Accountability ensures someone acts on what you learn. Proportionality focuses your effort. Enablement makes compliance sustainable.
Miss any one of them, and the others become less effective. Block AI without providing alternatives (enablement), and you get shadow AI. Monitor usage without clear policies (transparency), and people feel surveilled. Set policies without review (accountability), and they become outdated.
Making it work for your business
The good news for SMEs: you don't need to replicate enterprise complexity.
What you need: a simple monitoring tool that shows you AI usage across your team (browser-based options can be deployed in minutes without IT infrastructure), a one-page policy that explains what's allowed and what's not (keep it readable), an owner who reviews AI governance quarterly (at minimum) and updates policies as needed, risk categories that match your specific business (what's high-risk for a law firm is different from what's high-risk for a marketing agency), and approved alternatives so people can use AI productively within your guidelines.
The regulatory context
A brief note on compliance: depending on your industry and location, AI governance isn't just good practice. It may be legally required.
The EU AI Act takes full effect in August 2026 and applies to any company processing EU citizen data, regardless of where you're based. Various data protection regulations (GDPR, CCPA, Australia's Privacy Act) have implications for how AI tools handle personal information.
Professional services firms face additional obligations. Client confidentiality requirements in legal and financial services don't disappear when data goes into an AI tool.
Getting basic governance in place now makes compliance easier later.
Start where you are
AI governance doesn't require perfection from day one.
Start with visibility. Understand how your team actually uses AI. Then build out policies, accountability, and enablement based on what you learn.
The organisations handling AI well aren't the ones with the most restrictive policies. They're the ones with the clearest understanding of what's happening and the simplest frameworks for managing it.