Part 2: Why embracing AI governance and setting up the right approval mechanisms is the right approach

Governance is not a dirty word, it's an enabler to a better outcome

In the first part of this series, I explored the rise of Shadow AI, the unsanctioned AI usage that often signals unmet needs rather than deliberate risk taking. I argued that ignoring it is a strategic blind spot, and that organisations should treat it as a clue to where innovation is already happening. Now, I want to shift the focus to how governance, when designed for enablement rather than control, can turn Shadow AI from a liability into a strategic advantage.

AI governance is often misunderstood, it’s not a brake pedal, it’s a steering wheel. The goal isn’t to slow down innovation, but to guide it in a direction that aligns with organisational values, risk appetite, and strategic priorities. If Shadow AI is the symptom, governance is the response, not through restriction, but through enablement.

The most effective governance models are not heavy handed, in fact they’re lightweight, adaptive, and designed to support experimentation. Approval mechanisms should be fast, transparent, and user led, if someone wants to use a new AI tool, the process should help them understand the risks, document the use case, and connect with others doing similar work, it should feel like a partnership, not a permission slip.

Trust is the currency of AI adoption, without it, people will either disengage or go underground, governance builds trust by making AI usage visible, auditable, and explainable. It creates a shared language for discussing risk, ethics, and impact. It also helps leaders make informed decisions about where to invest, where to intervene, and where to let things run.

There’s a forward thinking opportunity here, governance can be a catalyst for scale! By removing ambiguity, it accelerates adoption, teams know what’s allowed, what’s