The Governance Gap: Why AI Policy Lags Behind Deployment
As organisations rush to integrate AI systems into their operations, a troubling pattern has emerged: the governance frameworks meant to guide responsible use consistently lag behind the pace of deployment. This isn't merely an inconvenience—it represents a systemic risk that demands serious attention.
The Speed Problem
The challenge begins with velocity. AI capabilities evolve faster than our institutional capacity to understand them. By the time a committee has drafted guidelines for one generation of tools, the next has already arrived, often with fundamentally different capabilities and risks.
This isn't a failure of effort or intention. It reflects a structural mismatch between how technology develops and how organisations make decisions. Technology moves in rapid iterations; governance moves through consultation, deliberation, and consensus-building.
What Falls Through the Gaps
When governance lags deployment, several things happen:
First, accountability becomes unclear. Without established frameworks, it's difficult to determine who is responsible when AI systems produce harmful outcomes. This ambiguity often means no one is held responsible.
Second, risk assessment becomes ad hoc. Teams make decisions about AI use based on incomplete information, often defaulting to whatever seems to work in the moment rather than what's appropriate for the context.
Third, trust erodes. When stakeholders—employees, customers, the public—see AI systems deployed without clear governance, they reasonably question whether anyone is ensuring these systems are safe and appropriate.
Closing the Gap
Addressing this challenge requires rethinking how we approach AI governance. Rather than trying to create comprehensive rules for every possible scenario, organisations need flexible frameworks that can adapt as technology evolves.
This means investing in governance capacity—people with both technical understanding and policy expertise who can evaluate new developments in real-time. It means creating feedback loops between those deploying AI and those responsible for oversight. And it means accepting that some degree of uncertainty is inevitable, while building systems robust enough to handle that uncertainty safely.
The governance gap won't close on its own. It requires deliberate effort, sustained investment, and a willingness to prioritise responsible use even when it slows down deployment. The alternative—continuing to deploy AI systems without adequate governance—is a risk we cannot afford to take.