The IDC study from April tells a story that should worry every mid-market accountancy practice, law firm and financial services business in the UK. Two-thirds of US audit firms have already embedded AI or are piloting it. They are past the adoption question. Now they are asking: how do we govern it? How do we validate it? How do we stay compliant? This is not a US problem anymore. The FRC's guidance on auditor use of automated tools, the ICO's deepening scrutiny of algorithmic decision-making under UK GDPR, and the emerging shape of the EU AI Act all signal that UK regulators will ask the same control questions—and soon. If your firm is still wrestling with whether to adopt AI, you are already behind. If you have adopted it without a governance framework, you are exposed.

This shift from adoption to control is the inevitable maturity curve for any technology in regulated industries. First comes experimentation and deployment. Then comes the hard work: proving the AI system does what you say it does, documenting how it handles edge cases, showing that outputs are accurate enough for professional use, and building an audit trail that regulators can inspect. The profession is realizing that 'we use AI to speed up work' is not a sufficient answer. The real questions are: which decisions can AI make alone? Which require human review? How do you know when the AI is wrong? What happens when it is? For accountants, lawyers and compliance officers, these are not technical questions—they are professional responsibility questions. And they sit at the heart of FRC ISA UK 220, SRA Code Part A, and PRA SS1/23 on third-party service providers.

Trovix's position on this is unambiguous: control frameworks must come before, not after, broad deployment. Too many firms have adopted general-purpose LLM interfaces—Microsoft Copilot, ChatGPT, Claude—without thinking through the specific risks in audit, due diligence or regulatory reporting. Those tools are good for drafting and ideation. They are not audit-grade. A better approach isolates the AI task, validates it against known data, documents its limits, and wraps it in a governance dashboard that shows you what the system has done, where it failed, and what human review actually occurred. Trovix Audit is built on this principle: it surfaces the control chain, not just the output. That is the difference between 'we use AI' and 'we use AI responsibly'.

For a mid-market practice right now, the practical move is this: audit your current AI use. If you are using general-purpose language models for client work, compliance reporting or advice, you need to document what they are used for, who is responsible for reviewing output, and how you know the output is accurate. Then build or acquire a governance layer—something that logs decisions, flags anomalies, and gives you evidence of control for the regulator. Trovix Audit does this for audit and accounting. You may not need to deploy more AI. You may need to control the AI you already have. That is what the US firms are learning. That is what the FRC and ICO will expect of you.

Source: Caseware