Rethinking Internal Controls in the Age of AI
Internal Controls and AI
For many finance teams, internal controls have been built on familiar foundations: segregation of duties, approval workflows, audit trails and independent review. Those fundamentals still matter. They have helped organisations create order, accountability and confidence in high-risk financial processes.
But AI is changing the context in which those controls operate, and it is now a critical to align internal controls and AI to boost efficiences and accuracy.
Finance teams are already seeing AI support ar eas such as expense categorisation, transaction analysis, forecasting and decision support. The opportunity is clear: faster processing, better access to information and more capacity for higher-value work.
The risk is that existing controls may not always be designed for the speed, confidence and complexity that AI introduces.
The question for finance leaders is no longer simply: Can AI improve the process?
It is: Can we trust the output, understand the risk and remain accountable for the decision?
Internal controls and AI are becoming more dynamic
Traditional controls are often designed around human actions and human timescales.
A transaction is processed. Someone reviews it. Another person approves it. An audit trail records what happened.
AI can compress these timelines. Activities that once took hours can happen in minutes. Reviews that once relied on manual sampling may become more automated. Processes that once moved step by step may become less linear.
That creates opportunity, but it also changes where assurance needs to sit.
Finance leaders may need to move from asking:
“How do we control the person?”
to asking:
“How do we validate the output?”
That shift matters. If AI is categorising, analysing, recommending or summarising information, the control environment needs to test not only whether a process was followed, but whether the result is reliable enough to use.
This may mean looking again at:
- where AI is influencing decisions
- which outputs require human review
- how accuracy is checked over time
- what evidence supports the output
- who remains accountable for the f inal decision
Trust should be earned, not assumed
One of the challenges with internal controls and AI is that it can sound confident even when the output is incomplete or inaccurate. A forecast can look credible. A recommendation can feel logical. A report can appear finished. But far from it in some cases.
That does not mean AI should be avoided. It means finance teams need the same professional scepticism they already apply to assumptions, reconciliations and material judgements.
Trust needs to be built through validation, not presentation.
For finance leaders, this means being clear about the level of assurance required before an AI-supported output is used. Some outputs may only need a light check. Others, particularly those linked to reporting, compliance, commercial decisions or payment approvals, may need stronger review.
The organisations that gain value from AI will not necessarily be the ones that move fastest. They are more likely to be the ones that create the right balance between innovation, oversight and accountability.
The fraud landscape is changing too
AI is not only changing internal finance processes, it is also increasing risk of external fraud and cyber attacks.
Deepfake voices, synthetic video and more sophisticated phishing techniques are beginning to challenge traditional verification methods. Processes that once relied on recognising a voice, checking an email address or validating a signature may need additional assurance.
This is not a reason to become fearful of technology though, we just need to stay curious about how risk is evolving.
Finance controls may need to become more explicit about what is trusted, what is verified and what requires escalation. That could include clearer controls for payment approvals, supplier changes, sensitive requests and unusual activity.
It’s important to avoid just adding unnecessary bureaucracy, and instead create just enough governance to protect the organisation without slowing progress unnecessarily.
The human role in AI is more important than ever
AI can support analysis, identify patterns and suggest actions. But accountability remains a human responsibility.
This is an important distinction for finance leaders.
As automation takes on more routine activity, the value of human judgement becomes clearer. People still need to challenge assumptions, understand business context, interpret exceptions and make decisions where risk, ethics or commercial judgement are involved.
That means internal controls should not be reduced to system configuration alone. They need to bring together:
- technology that supports better information
- processes that define how decisions are made
- governance that clarifies accountability
- people with the confidence and capability to challenge outputs
This is where AI adoption becomes a change challenge, not just a technology change.
What finance leaders should consider now
A practical starting point is to review where AI is already influencing finance work and where it may do so next. Finance leaders may want to ask:
- Where is AI being used in financial processes today? Include formal systems, embedded tools and informal use by teams.
- Which outputs affect decisions, reporting or payments? Prioritise the areas where errors, omissions or manipulation would carry the greatest risk.
- Where does human review still add value? Be clear about where judgement, challenge or escalation is needed.
- How is performance monitored over time? AI-enabled processes should not be reviewed only at implementation. Accuracy, consistency and exception handling need ongoing attention.
- Who owns the control? Accountability should remain clear, particularly where responsibility sits across finance, technology, risk and operations.
These questions help make AI governance practical. They move the conversation away from abstract risk and towards clear decisions about control, assurance and accountability.
Looking ahead in AI
The fundamentals of good governance have not disappeared. Clear ownership, independent review, strong decision-making and appropriate controls remain as important as ever. What has changed is the environment in which those controls operate.
AI can help finance teams work faster and make better use of information. But speed without assurance can create new risks, confidence without challenge can create false certainty, and automation without accountability can weaken control.
Organisations that use AI well will not simply be those with more technology, they will be those that understand where AI adds value, where judgement is still needed, and how to govern both with confidence.
Reviewing how AI, automation or technology change is affecting your controls, governance or business readiness? Speak to one of our experts to bring clarity to your next big decision.
