The conversation about artificial intelligence in accounting and auditing is no longer about its ability to draft reports, analyse data, or save time. The defining shift now underway is the move from AI as an assistive tool to intelligent agents that execute procedures, connect data, monitor processes, and recommend decisions — and, within defined mandates, take some of them.
This shift opens significant opportunities for the profession, but it also raises professional and regulatory questions that cannot be left to technologists alone. The wider the authority granted to intelligent systems, the greater the need for professional judgement, governance, accountability, and the ability to verify both the integrity of the data and the logic of the decisions it produces. We are not merely entering a new phase of technology; we are witnessing a redefinition of the accountant’s and auditor’s role — and professional bodies carry a direct responsibility to lead this transformation.
Organisational Readiness Before Model Power
Some organisations assume that the success of an AI initiative hinges on selecting the most powerful model or the most advanced platform. Practical experience proves otherwise: many initiatives stall not because the technology is weak, but because the organisation itself is not ready.
An intelligent system does not inherently know what an organisation means by “recognised revenue,” “active customer,” or “abnormal expense.” Where these concepts are not standardised across departments and systems, the AI will produce contradictory answers — or analyses that appear precise while resting on flawed definitions.
This is where the accountant’s role comes to the fore: not as a user of the system, but as the custodian of the financial meaning on which it depends. Technology can process data, but it cannot, on its own, judge whether the data reflects the economic substance of a transaction, whether the accounting classification is appropriate, or whether the outcome complies with professional standards and policies.
From Reviewing Numbers to Reviewing Decision Logic
In the traditional environment, accountants and auditors verify the accuracy of entries, documents, balances, and disclosures. In an agentic environment, verification extends to how the system arrived at its result: the source of the data, the definitions and rules underpinning the analysis, the scope of authority granted, the logic of the recommendation, the actions executed within applications, the instances where the system departed from its rules, and the degree of human intervention required before sign-off.
Professional work thus moves from reviewing outputs to reviewing the entire decision system. When an intelligent agent classifies a transaction, proposes a provision, rejects a payment, or approves a vendor, the professional question is no longer only “Is the result correct?” but also “How was it reached? Was the authority appropriate? And who bears responsibility if the system gets it wrong?”
Governance Is a Condition for Scale, Not a Barrier to It
Governance is sometimes viewed as a constraint that slows adoption. In reality, it is the absence of governance that turns successful pilots into institutional risks at scale.
Every intelligent agent operating within an organisation must be treated as a technical and professional asset: with an accountable owner, defined authorities, a clear scope of work, a decision log, a review mechanism, and procedures for suspension and modification. A review at launch is not enough; continuous monitoring is essential, because a system’s behaviour changes as the data, policies, and underlying models change.
Accountants and auditors are well placed to help design practical controls, chief among them: separating recommendation from execution and approval; defining which transactions require human sign-off; documenting the impact of every action; monitoring exceptions; periodically testing output accuracy; and setting an acceptable risk level for each use case. An agent that offers analytical recommendations must not be governed in the same way as one authorised to modify vendor records or execute payments.
Never Automate a Broken Process
A common mistake is to use AI to accelerate a process that already suffers from poor design, conflicting responsibilities, or unreliable data. In that case, the system does not solve the problem — it amplifies it and propagates errors faster.
Automation must therefore be preceded by a review of the process itself: Are the rules clear? Is the data trustworthy? Are responsibilities defined? And can the decision be explained to management, the auditor, and the regulator?
Structured financial processes remain the most suitable starting point: invoice matching, variance analysis, expense review, detection of unusual transactions, and preparation of first-draft reports — always distinguishing between what can be executed automatically and what demands professional judgement that must never be fully delegated.
Professional Judgement Will Be Elevated, Not Eliminated
It might be assumed that the spread of AI will reduce the need for accountants and auditors. The opposite is true: the more automation expands, the greater the need for a professional who can interpret results, assess their reasonableness, and take responsibility for the final decision.
A system may flag an unusual pattern, but it cannot always distinguish between an error, fraud, and a legitimate exceptional transaction. It may propose an accounting treatment without grasping the contractual context, the economic substance, or local regulatory considerations. The professional’s role thus shifts from repetitive execution to evaluation, interpretation, oversight, and decision-making.
The Responsibility of Professional Bodies
The market is moving fast, while many professional and educational frameworks still treat AI as an assistive tool rather than a system that executes decisions inside organisations. Professional bodies must therefore adopt a proactive role across five tracks:
First, developing clear professional guidance on the use of intelligent agents, covering governance, responsibility, documentation, independence, confidentiality, and data quality.
Second, updating qualification and continuing professional development programmes to include understanding intelligent systems, evaluating their logic, and testing their controls — not merely operating their tools.
Third, providing practical models for classifying AI use cases by risk level and identifying the cases where human review is mandatory.
Fourth, convening professional dialogues that bring together accountants, auditors, technologists, regulators, and boards — because AI governance cannot be addressed from a single vantage point.
Fifth, supporting small and medium-sized practices, so that digital transformation does not widen the gap between them and larger firms.
Professional bodies must also lead the debate on questions that are no longer about the distant future: Who is responsible for a decision executed by an intelligent agent? Where is the line between professional recommendation and automated execution? How does the auditor obtain sufficient and appropriate evidence about system outputs? When is human intervention mandatory? And how can systems that change continuously be tested?
Redesigning the Work, Not Adding a Tool
The real value of AI will not be realised by layering a new tool on top of an old way of working. Requiring accountants to perform all their traditional tasks and then use AI tools on top of them produces no transformation — only more pressure and complexity.
What is required is a redesign of the work itself: Which tasks should the system take on? What remains under the accountant’s supervision? Where does the auditor intervene? And how is time redistributed from repetitive tasks to analysis, evaluation, and advisory work? This, in turn, demands a review of job descriptions, qualification pathways, competency requirements, and continuing education programmes.
Conclusion
Agentic AI does not eliminate the role of the accountant or the auditor; it changes its essence. The profession is moving from recording and reviewing events after the fact to participating in the design of systems that act and generate decisions continuously.
In this new environment, professional value will not be measured by the ability to use the tool, but by the ability to control it, evaluate it, interpret its results, and hold accountable those who designed it, delegated to it, and approved its decisions. If technology gives intelligent agents the capacity to act, it is accounting and auditing that must give that action its trust, discipline, and accountability.
