AI and Audit - a Tool or Trap?
It’s hard to ignore the noise around artificial intelligence (AI) right now. Whether it's being used to draft emails, automate processes, or simply create fun AI generated toy dolls of ourselves, AI has found its way into the audit profession — and it looks like it’s here to stay.
But rather than simply fall into the shiny new object syndrome, it’s important we cut through the hype and take an objective look at what AI can realistically offer UK audit firms… and where it might create more problems than it solves.
At Apex, we’re fans of anything that makes audit better, smarter, and less painful. But we also work with firms after the fact, helping them understand cold file reviews points where things didn’t quite go to plan. So, we’ve seen both the promise and the pitfalls of emerging technology in action — especially when firms aren’t clear on what they’re using or why.
Here’s our take — warts and all.
The Pros: How AI (and data analytics) can help audit teams
First, a quick clarification
Not everything labelled “AI” in audit is truly artificial intelligence. In fact, in practice, what we’re seeing on files is that many tools fall under data analytics — rule-based techniques that analyse large datasets quickly and efficiently, but without the self-learning element that is typically associated with AI.
Both have value — and both come with risks — but it’s worth knowing what you're dealing with. That understanding is essential for compliance, documentation, and staff training.
With that in mind, here’s where these tools really generate benefit:
1. Data handling to the max
Whether it’s AI or data analytics, these tools can process and sort through large volumes of transactions at lightning speed. That means faster identification of unusual journal entries, duplicate payments, or deviations from expected trends.
This doesn’t just improve efficiency — it can genuinely enhance audit quality. Done right, it allows teams to focus their energy on risk areas, rather than wading through the routine.
2. Improved consistency
Automated tools (whether AI-driven or not) don’t get tired or distracted. This can lead to more consistent risk assessments, sample selections, and analysis — reducing the human error that creeps in when audit teams are under pressure.
3. Time back for human judgment
Ironically, one of the strongest arguments for technology is that it gives teams more space to do the thinking. By automating the heavy lifting — matching invoices, scanning for anomalies — auditors can spend more time applying professional scepticism and understanding what’s really going on.
4. Potential for better audit trails
Some tools log their actions automatically, creating a clearer record of what was tested and how. For firms that struggle with documentation, this could be a huge help — but only if the team still explains how those outputs were interpreted and used as evidence. This is currently one of the key weaknesses we’re seeing on our file reviews when using AI tools.
The Pitfalls: Where things can go wrong
1. Lack of transparency (especially with true AI)
With data analytics, you typically know what you asked the tool to do. But with AI — especially machine learning models — the logic behind the outputs isn’t always visible or easy to explain. If the tool flags (or doesn’t flag) something, can your team explain why? Regulators will expect teams to understand and justify the procedures performed. Relying on a tool without understanding its mechanics is a quick route to regulatory trouble.
2. Over-reliance and false confidence
We’re starting to see this in cold file reviews — tools are used, and teams assume the job’s done. But tech doesn’t replace judgment. Anomalies flagged (or not flagged) by a tool still require analysis and interpretation. It’s easy to let the machine take over — and that’s a risk in itself. This is where the ‘stand back’ approach is useful. Do the outputs make sense?
3. Documentation gaps
Whether it’s AI or data analytics, too often we see files where the team used a tool but didn’t explain what it did, why it was appropriate, or how the outputs influenced the audit response. Regulators need clarity — and so do reviewers. If you can’t walk someone through it or you have to explain it in a debrief meeting, it probably isn’t well documented.
4. Regulatory uncertainty
Let’s be honest — technology is moving faster than the standards. There’s limited formal guidance on how AI fits within ISAs or how much you can rely on automated evidence. That leaves firms, especially smaller ones, navigating grey areas with limited support. The key is to err on the side of caution and focus on audit fundamentals: risk, evidence, and documentation.
So… should UK auditors be using AI and analytics?
Used properly, these tools can enhance audit quality and free up teams to focus on higher-risk areas. But they’re not a magic wand — and they don’t replace professional judgement or effective documentation.
Before rolling out a new tool, firms should consider:
Are we using AI or traditional data analytics — and do we know the difference?
Are staff properly trained to use and interpret the results?
Can we explain and document what the tool did — and why it was appropriate?
Have we documented our conclusions clearly, especially where the technology influenced key decisions?
And most importantly: have we considered whether this tool is the right fit for the risk, the client, and the audit approach? Because if the answer to that last one is “not sure”… it’s worth taking a step back. This is never going to be a one size fits all approach.
What next?
At Apex, we’re not here to sell the latest tech or pretend to be AI experts — but we are seeing more examples of these tools being used in practice, both well and not so well. We help firms step back, reflect on how they’ve applied tools in their audit files, and offer clear, pragmatic feedback on whether the evidence stacks up. If you want an honest, practical perspective — with an eye on regulatory expectations — we’re here to help.