- within Food, Drugs, Healthcare and Life Sciences topic(s)
AI adoption rarely starts with a formal strategy. It starts with a team testing a new tool. An employee using an AI assistant. A vendor adding AI functionality to an existing platform. Or a department discovering a faster way to summarise, classify or analyse information.
That is not necessarily a problem. It often reflects exactly what companies want: innovation, curiosity and efficiency. The problem begins when AI adoption moves faster than the organisation's ability to understand what is actually happening.
Management may know which AI tools have been officially approved. But does it know which tools employees are really using? What information is being entered? Whether confidential, personal or health data is involved? Whether AI outputs are influencing business decisions? And who remains accountable for those decisions?
This is why Shadow AI is not simply an IT issue. It is a governance warning sign.
You cannot govern what you cannot see
An organisation cannot meaningfully assess AI risk without basic visibility. A practical starting point is therefore not another lengthy compliance programme. It is establishing a reliable picture of:
- which AI systems and tools are being used;
- for which business purposes;
- which data they process;
- who owns the use case;
- which uses require approval or additional controls; and
- how material use cases will be monitored after deployment.
This normally requires a few core governance components: an AI policy, an intake process, an AI inventory, clear responsibilities, proportionate approval paths, appropriate controls and evidence of key decisions. But the objective should not be bureaucracy. A governance process that is too slow or complicated may simply drive AI use further underground.
The better question is: How much governance does this particular use case actually require?
Using an AI tool to help draft non-confidential internal text is not the same as using AI with patient data or allowing its output to influence significant customer, employee or clinical decisions.
Good governance distinguishes between them.
The first step is not to ban AI
Companies will continue to experiment with AI. Trying to eliminate that behaviour entirely is unlikely to be the most effective strategy. The more useful approach is to bring actual AI use into a framework that creates visibility, accountability and proportionate control without removing the benefits the technology is intended to create.
For leadership, the starting question is therefore simple:
Do you know which AI tools are actually being used in your organisation, including the ones nobody formally approved?
If the answer is no, that is where AI governance should begin.
The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.
