A practical guide for executives and senior managers who need to deploy AI without creating new risks. No technical jargon. No vendor sales material. Just what you need to know to make sound decisions.
Your staff are already using AI tools. Whether you’ve approved them or not, LLMs like ChatGPT and Claude are being used to draft emails, summarise documents, write code, and prepare reports. The question is not whether AI is entering your organisation — it already has. The question is whether it’s happening with governance, or without it.
The risks are real but manageable. Unmanaged, they include: confidential information being uploaded to third-party AI providers; AI-generated errors reaching clients; regulatory exposure from automated decisions made without required human oversight; and staff developing over-reliance on tools they don’t fully understand.
The upside is also real. Organisations that govern AI well — rather than prohibiting it or ignoring it — see genuine productivity gains, faster delivery, and more time for high-judgment work.
If you can’t answer these, you don’t yet have a handle on AI risk in your organisation.
Most organisations don't know. Shadow AI adoption is rampant. The starting point is an audit: survey staff, check browser usage data if your policy permits it, ask IT to flag AI-related domains in DNS or proxy logs. You need a real picture before you can govern anything.
Free-tier consumer AI products typically use inputs to train future models. If staff are pasting client data, financial information, or personal data into ChatGPT or similar tools, your data governance obligations are at risk. The default assumption should be: anything entered into an external AI tool is outside your control.
AI systems hallucinate. They produce confident, plausible-sounding errors. If your organisation has deployed AI in any workflow without a human review step for outputs that matter, you have unmanaged risk. 'AI checked it' is not a defensible position when something goes wrong.
Low-stakes decisions with easy correction paths (drafting a first-pass email) are different from high-stakes decisions that affect people's livelihoods, access to services, or legal rights. The latter require human oversight, appeal mechanisms, and in many jurisdictions, explicit regulatory compliance.
Not if — when. AI systems produce errors, behave unexpectedly, and sometimes fail in ways that cause real harm. Do you have a documented process for discovering, containing, assessing, and remediating an AI incident? Do your staff know how to report one? If not, you're flying blind.
Many AI vendor contracts assign liability for errors entirely to the customer. Before signing, your legal team needs to review: who owns the outputs, what the vendor's liability cap is, whether the vendor uses your data to train models, and what your exit rights are if the vendor degrades the service.
Good AI governance is not a 100-page policy document. It is a small number of working controls that people actually follow.