Agentic AI in corporate finance and financial reporting - Business Today Middle East
It’s more than likely that you’ve heard the phrase ‘agentic AI’ being bandied around this year. It seems like it’s the buzzword of 2026. But what does it mean?It’s perhaps best to compare it to the generative. . .
It’s more than likely that you’ve heard the phrase ‘agentic AI’ being bandied around this year. It seems like it’s the buzzword of 2026. But what does it mean?It’s perhaps best to compare it to the generative chatbot-style AI we’re all now used to, like ChatGPT or Claude. These agents rely heavily on guidance: they respond to a prompt, take an action and await further instruction. Agentic AI, however, is more like a competent worker. You provide a task, and tools, and it will go off and produce what is asked of it, using everything at its disposal. So what does such an advanced tool mean for corporate finance?Conventional automation follows a fixed set of rules. If a condition is met, it performs a pre-defined action. It’s fast and reliable, but can’t handle anything its designer did not anticipate. It cannot reason; it merely executes. Agentic AI is different. You give it a goal (rather than an instruction), and it works out the steps, takes the actions itself, and works across systems to do so. So in finance, for example, an agent can pull data from the ledger, reconcile it, investigate what does not match, draft the analysis and assemble the output, chaining the whole sequence together. Automation follows rules. Generative AI answers questions. Agentic AI pursues goals and takes action. From answering to acting – this is what makes it significant for finance. From days to hoursIn a traditional finance function, month-end reporting is a manual assembly job. Someone pulls the numbers from the accounting system, reconciles them, chases the items that do not tie, drops them into a template, writes the commentary, and builds the board pack slide by slide. It takes days, consuming senior time that could be spent interpreting the results, rather than producing them. But an agentic AI system approaches it as a goal. It pulls live data from the ledger, reconciles it against the financial model, flags and investigates any exceptions, drafts variance commentary, and assembles the reporting pack end-to-end. A human then reviews and signs it off. This is not theoretical. We run exactly this across our own entities. Our own close and board reporting went from three days of work to about four hours. On one client engagement, we cut the monthly close by a full week. The output is the same board-grade pack. All that really changes is that people spend their time checking judgement, not assembling data. Where agentic AI creates gainsFaster reporting is the most visible gain and easiest to measure, but on its own, it is the least valuable. Put simply, producing the same style of backward-looking reports faster is a modest win. Stronger control and better forecasting are where it gets interesting. Agentic systems apply checks consistently and never rest, which clearly strengthens control. And because they can update a forecast continuously as new data arrives, forecasting shifts from a quarterly event to alivingview. This is hard to do, unless you have invested time in building a proper set of processes and have clean underlying data. But the greatest value sits in better decision-making. Speed, control and forecasting are inputs. The return comes when leadership acts on them. When reporting reaches you in hours rather than weeks, the numbers are still current enough to change what you do. When the forecast is live, you can see a cash or margin problem emerge, while there is still time to respond. The risksOf course, like many aspects of AI, there are risks. Accuracy is the first. The danger is not the obvious error, which gets caught. It is the plausible figure that is wrong, produced with total confidence and no warning. The control is a human who knows the business well enough to notice when a number does not feel or look right. We are still living in an era where hybrid models are the way forward: AI-driven work, with human oversight and checking. Data security widens because an (AI) agent reaching across your ledger, banking and reporting systems has broad access. Permissions, therefore, must be tight, controlled and secure. Auditability is specific to reporting and often overlooked. If an agent produced a number, you must be able to show how. An auditor or regulator will not accept that the system generated it. Workings must still be traceable. Accountability ties it all together. Someone still signs the accounts, and answers to the board. That responsibility cannot move to a machine; however capable it becomes. Human oversight must remain wherever a number becomes official, wherever money moves, and wherever a judgement is required, rather than a calculation. The rule we hold to is straightforward: A named person is accountable for every number that leaves the function. Change is comingIn an agentic AI finance scenario, the CFO role will move from overseeing production of finance to directing it. That will translate to less time on assembly, and more on judgement, capital allocation and big decisions that move the needle. The CFO also picks up a new responsibility: owning accountability for AI production. For accountants, agentic AI shifts their role from preparing the numbers to reviewing and interpreting them. The value is no longer in building the reconciliation, rather, it lies in spotting what it reveals – and knowing what to do about it. That is a move up the skill ladder, so those who invest time in learning about the business will be able to add the most value. Much of the entry-level work, the manual preparation and data assembly, is exactly what AI agents do well. And that work has traditionally been how juniors learned the trade. So we will see finance teams seeking to develop judgement in their people earlier and more deliberately, because the old apprenticeship of doing the manual work is shrinking. Like many aspects of technology, agentic AI is a double edged sword. On the one hand, it ushers in a new era of unprecedented efficiency, but on the other, it heightens risk, creates issues around non-deterministic actions and increases the possibility of fraud and regulatory non-compliance. The next few years will be very interesting, as we move to embrace the clear efficiencies of agentic AI in finance, while balancing the risks and vulnerabilities.