A Red Hat survey showing that 87% of UK IT leaders have deployed agentic AI systems while only 25% have strong governance in place is not a technology problem. It is a regulatory problem. For law firms bound by the SRA Code, insurers subject to PRA SS1/23 and FCA Consumer Duty PS22/9, and accountancy practices under FRC ISA UK, this governance gap is not a gap at all—it is a breach waiting to happen. When 48% of firms cannot even tell you where their data is being processed, you are not looking at a deployment lag. You are looking at firms that have handed decision-making and client data to systems they cannot supervise, audit, or explain to a regulator.

This is what happens when AI adoption outpaces governance architecture. The industry has been chasing the productivity gains of agentic AI—systems like Claude, GPT-4 and others that make decisions with minimal human intervention—without first building the frameworks needed to govern them. Vendors have sold speed. Firms have bought it. But compliance does not move at the speed of innovation. The EU AI Act now defines high-risk AI. The ICO has hardened its stance on algorithmic accountability under UK GDPR. Lloyd's Blueprint Two expects insurers to demonstrate active governance of AI-driven underwriting. Meanwhile, three-quarters of UK IT decision-makers are running these systems blind.

Trovix's position on this is clear: agentic AI in regulated businesses demands visibility before velocity. Tools like Harvey and Legora solve specific document problems well, but they sit atop an invisible foundation. You cannot govern what you cannot see. That is why Trovix Audit exists—not to slow deployment, but to make governance real. It gives you continuous visibility into where AI is running, what decisions it is making, and whether those decisions comply with your own risk appetite and regulatory obligations. The difference between firms that will pass a regulatory review and those that will not is not the AI they chose. It is whether they can prove they chose it responsibly.

If you run a mid-market law firm, insurance underwriting team, financial advisory practice or accountancy firm, the actions are straightforward. First: map where agentic AI is already running—chances are it is in places you have not formally audited. Second: establish what data is feeding it. If you cannot answer that question in ten minutes, your governance is broken. Third: implement active oversight of AI decision-making before you scale it further. The regulator will ask three questions: Where is it running? What is it deciding? Can you override it? If you cannot answer those clearly, you need to stop and build that framework now. The penalty for doing this wrong is not a slow deployment. It is enforcement action.

Source: Computer Weekly