What AI governance should look like for growing companies

Enterprise AI governance frameworks — the kind produced by large consulting firms and standards bodies — are designed for organisations with dedicated risk functions, established audit processes, and the capacity to absorb significant process overhead.

Growing companies rarely have any of those things. They are typically moving fast, with small teams, limited documentation, and a strong preference for doing over governing. The result is that many of them either adopt frameworks that are too heavy to sustain, or skip governance entirely and deal with the consequences later.

For a company at growth stage, practical AI governance starts with three things: an inventory of where AI is being used, clear ownership of each system (not just technical ownership — business accountability), and a simple decision framework for when human review is required before AI-generated output is acted upon.

This doesn't require a dedicated governance function. It requires someone to own it, a consistent process, and a commitment to updating it as the company's AI usage evolves.

The advantage of starting simply is that simple governance is sustainable. A one-page policy that people actually follow is more valuable than a hundred-page framework that sits on a shared drive.

As the company grows, the governance can grow with it. New risk categories get added. The review process gets more structured. Tooling gets introduced to automate what was previously manual. But the foundation — ownership, visibility, and human review thresholds — stays the same.

The companies that struggle most with AI governance are those that either never started or tried to implement an enterprise framework too early. The right level of governance is the level that the organisation can actually sustain.