AI tools have moved from the lab to the laptop. Your team may already be pasting customer data into chatbots, using AI to generate documents, or trialling “agents” that can act on your systems. The productivity upside is huge – but so is the risk of sleepwalking into a compliance mess.
The core challenge is the “black box” nature of modern AI. Models learn from vast datasets and produce answers through statistical patterns rather than explicit rules. They can be astonishingly helpful, yet the path from input to output is often opaque.
For regulators and customers, opacity is a problem. If a decision affects someone’s rights, money or reputation, they are entitled to ask: how was this decided? Under regimes like GDPR and emerging AI-specific rules, organisations remain responsible for what automated systems do with personal data. “The model did it” is not a defence.
So what can a pragmatic SME do?
First, be honest about where you’re already using AI. Make a list of tools and use cases: chat assistants, AI features in SaaS platforms, internal experiments. Note what data goes in, what comes out, and how outputs are used. This inventory is your starting point for risk assessment.
Second, classify AI use cases by impact. An AI that drafts marketing copy is a very different risk to one that helps decide who gets a loan or how a complaint is handled. Focus your governance energy where harm could be real: things that influence employment decisions, credit, pricing, access to services, or anything sensitive about health, children or vulnerable people.
For higher-risk uses, build in three safeguards: transparency, human oversight and auditability.
Transparency means being clear with users – both staff and customers – when AI is in the loop. If an AI helps draft a response, that’s fine, but don’t let it send messages autonomously that look like they came from a human. If customers interact with a bot, label it as such and make it easy to reach a person when needed.
Human oversight is about keeping a competent person in the loop for important decisions. That might mean requiring a human to approve AI-generated recommendations or limiting AI to triage and summarisation rather than final judgments. The more critical the decision, the tighter the oversight.
Auditability is often the hardest. You may not be able to explain the inner maths of a model, but you can log inputs, outputs and key prompts. If challenged, you should be able to show what information the AI had, what it produced, and how a human used it. Avoid “fire and forget” scripts that act without trace.
Data minimisation still applies. Don’t feed more personal data into AI tools than is genuinely needed. Anonymise or pseudonymise where possible. Be cautious with sensitive categories of data, and think twice before sending anything to consumer-grade tools where you don’t control where the data goes.
Contracts matter too. When you buy software with AI features, check where data is processed, how long it’s retained, and whether it’s used to train models beyond your account. Make sure your data processing agreements reflect reality, not marketing gloss.
You don’t need a full-time ethics board, but you do need someone clearly accountable for AI governance – often the same person who owns data protection or information security. Give them the authority to say no to risky use cases or demand additional guardrails.
Finally, don’t let fear freeze you. The “black box” nature of AI is real, but so is the risk of falling behind competitors who use it confidently and responsibly. The goal is not to eliminate all risk – that’s impossible – but to understand it, document it and make conscious choices.
In 2025 and beyond, regulators are playing catch-up with technology. SMEs that build good habits now – transparency, oversight, logging, and thoughtful use of data – will find it much easier to adapt as new rules arrive. Those who treat AI as a magic wand and ignore the black box risk may find the bill arrives later, with interest.
