The Financial Reporting Council’s recent review of AI use in audit has raised eyebrows across the finance sector. Even the UK’s most sophisticated audit firms – the Big Four alongside BDO and Forvis Mazars – are deploying AI-powered tools without formally measuring their impact on audit quality.
But before we rush to judgment, let’s consider what this really reveals. These aren’t firms stumbling in the dark; they’re pioneers navigating genuinely uncharted territory. The FRC’s findings highlight a universal challenge facing finance leaders today: how do you innovate responsibly when the playbook is still being written?
What the FRC Review Actually Tells Us
The review found that while firms track AI usage for practical purposes like licensing and rollout, only one unnamed firm had established key performance indicators to monitor these tools’ contribution to audit quality. From KPMG’s AI transaction scoring that scans millions of entries to flag anomalies, to Deloitte’s use of AI for reviewing board minutes and complex contract analysis, the technology is embedded across audit processes.
The reality is that measuring something new is a genuine challenge. How do you create meaningful KPIs for tools that are still evolving? How do you benchmark performance when traditional metrics might not capture AI’s true value? These firms are still searching for the answers.
When AI tools process vast amounts of data in ways that weren’t possible before, traditional audit quality metrics may no longer be sufficient.
Why AI Governance Matters in the First Place
Before diving into the opportunities this creates, it’s worth asking, “If AI tools are helping auditors do their work, why do they need special governance at all?”
The answer lies in how AI works differently from traditional audit software. A standard spreadsheet or database query will give you the same result every time you run it. AI tools, particularly those using machine learning, can behave less predictably. They might flag different transactions as suspicious depending on the data they’ve been trained on, or miss patterns that a human auditor would spot.
There’s also the question of bias. AI systems learn from historical data, which means they can make the same mistakes without anyone realising. In audit, this could mean consistently missing certain types of fraud or unfairly targeting particular kinds of transactions.
Perhaps most importantly, AI tools often work as “black boxes” – they produce results without clearly explaining how they reached their conclusions. When a client or regulator asks, “Why did you focus on these transactions?” simply answering “because the AI told us to” isn’t good enough. Professional credibility requires being able to explain and justify audit decisions.
Proper governance ensures AI tools are reliable, fair and explainable, which protects both audit quality and professional reputation.
The Competitive Advantage Hiding in Plain Sight
Here’s the innovation paradox. Governance isn’t here to halt the progress AI has helped finance team make; it’s there to make sure things are done properly so sustainable innovation is possible. The FRC recognises this too. Alongside their review findings, they’ve published their first-ever guidance on AI in audit, developed collaboratively with industry experts to support rather than constrain innovation.
This guidance takes a ‘sophisticated approach’, acknowledging that explainability requirements vary depending on context and focusing on certain documentation rather than blanket rules. Importantly, it’s not introducing new regulatory requirements; it’s offering a roadmap for doing AI governance well.
The firms that embrace this guidance and solve the measurement puzzle first will have a significant competitive advantage across three critical areas:
1 – Performance metrics that matter
Developing meaningful KPIs for AI effectiveness isn’t an academic exercise; it’s the foundation of demonstrating ROI to stakeholders and building the business case for further investment. The firms that can prove their AI tools genuinely improve audit quality will be able to price their services accordingly and win client confidence.
2 – Explainability that builds trust
As AI becomes more sophisticated, being able to articulate how these tools reach their conclusions becomes crucial for both regulatory compliance and client relationships. The ability to explain AI decision-making in plain terms will differentiate firms in an increasingly crowded market.
3 – Governance frameworks that scale
Perhaps most importantly, structured oversight that enhances rather than restricts innovation allows firms to deploy AI confidently at scale. Robust governance can remove the uncertainty that causes hesitation.
The FRC’s focus on this area signals that regulatory expectations are becoming clearer. But rather than approaching this as a compliance burden, the regulator is working alongside industry to make AI adoption easier.
The Skills That Are In Demand
This governance challenge is creating demand for a specific type of professional. Not necessarily deep technical AI expertise, but someone more strategic, like being able to think deeply about challenges while maintaining an innovative mindset.
As we mentioned recently, AI won’t replace finance teams, but it might replace those who aren’t using it. So, forward-thinking finance leaders are looking for people who can translate technical AI capabilities into meaningful business performance metrics. These are professionals who understand both the potential and the pitfalls of new technology, and who can design measurement frameworks that capture value without stifling creativity.
Equally important is the ability to build explainability standards that satisfy multiple audiences – regulators who need assurance, clients who need confidence and internal teams who need clarity. This requires a rare combination of technical understanding and communication skills.
Perhaps most importantly, there’s growing demand for professionals who can build governance frameworks that scale with AI adoption. These frameworks need to be robust enough to satisfy regulators and flexible enough to accommodate rapid technological change. It’s a balancing act that calls for vision and discipline.
The FRC’s guidance illustrates this sophistication perfectly – it acknowledges that appropriate explainability varies by context and that AI explanations can be approximate rather than requiring complete transparency. This nuanced approach means firms need professionals who can navigate regulatory frameworks that are principles-based rather than prescriptive and who understand that AI governance is about appropriate transparency for specific use cases, not a one-size-fits-all.
What This Means for Finance Leaders
For CFOs and finance directors, the FRC review offers a preview of what’s to come. AI governance is becoming a board-level responsibility. Building teams that can navigate innovation responsibly is no longer optional.
This means hiring people who think differently about new challenges rather than simply applying old solutions to new problems. It means creating cultures where asking questions enhances rather than threatens innovation. And it means recognising that the most valuable professionals are often those who combine technical understanding with strategic thinking.
Looking Forward
The industry-developed guidance is about building capability together rather than pointing fingers. AI in finance has moved beyond experimentation and has become business-critical. And like all critical infrastructure, it requires proper measurement, governance and oversight.
At Core3, we’re seeing increasing demand for professionals who understand this transition. Our clients aren’t looking for people who choose between being progressive or being careful – they want those rare professionals who know how to be both. They’re building teams that can embrace innovation while building the frameworks that make that innovation sustainable.
If you’d like to build a finance team that can move fast and govern well, then let’s talk, our AI specialist can guide you on how to prep your team and business to optimise AI integration.
