Forecasting, closing, cash flow, invoices, collection, reporting: AI is finally starting to have a real impact on corporate finance. For CFOs and finance professionals, the challenge is no longer to follow a fad, but to choose the right use cases, at the right pace, with the right safeguards.
For a long time, artificial intelligence was a rather theatrical topic in corporate finance. The rhetoric heralded a revolution, the applications remained scattered, and finance departments struggled to distinguish real value from marketing noise.
Today, the landscape is becoming clearer. AI is no longer a technological curiosity. It is entering processes, changing the way we work, speeding up certain processes and beginning to reshape the role of finance teams. The key is to know where AI really helps, where it embellishes existing processes without transforming them, and how to integrate it without weakening financial rigour.
Firstly, putting AI in its place
For years, the word «AI» has served as a big umbrella. It has been used to describe forecasting, automation, machine learning, software robots, conversational assistants and, more recently, co-pilots and agents. This vagueness has long hampered the debate.
For a finance department, however, the subject deserves a simple reading. Some AI bricks analyse and forecast. Others read, summarise and reformulate. Still others are beginning to link together several actions within a defined framework. In short, AI is no longer just about calculations. It also helps to read, sort, explain, document and recommend.
This change is extremely important. Corporate finance is not just about numerical models. It also relies on documents, controls, discrepancies to be commented on, flows to be secured and decisions to be prepared. And it is precisely in this area that AI becomes interesting.
Then look at where the value really appears
A finance department gains nothing by chasing technology per se. It wins when a tool improves a specific, visible and measurable process. On this point, AI is beginning to produce convincing results in a number of very concrete areas.
Performance management: the ripest ground
The first concerns performance management. In budgeting, forecasting and FP&A, AI speeds up the construction of scenarios, identifies weak signals, detects abnormal variations and prepares comments on variances. It does not replace the management controller. AI makes time available to them. It reduces the amount of mechanical work to make room for analysis.
Closing the accounts: focusing attention more effectively
The second area involves closing of accounts. Here, AI helps to identify atypical entries, classify anomalies, prioritise exceptions and focus efforts on the really sensitive points. Here again, the challenge is not just one of speed. It's about the quality of attention. When teams spend less time checking the obvious, they devote more energy to understanding what's important.
Accounts payable: a fast-growing source of value
The third area, which is often less valued but highly effective, concerns accounts payable. Reading invoices, extracting data, reconciling orders, receipts and invoices, detecting duplicates, managing exceptions: AI improves a high-volume, often costly and sometimes highly imperfect process. For a CFO, the decisive advantage of this type of project is that the gains can be seen very quickly.
Cash flow: greater visibility, more responsiveness
The fourth area, that of treasury, is also gaining in maturity. AI helps to read flows better, to refine certain cash forecasts, to highlight discrepancies, to summarise positions and to prepare trade-offs. It does not take decisions in place of the treasurer or the CFO. On the other hand, it does improve visibility, and therefore responsiveness.
Debt collection and accounts receivable: of immediate use
The fifth field of application concerns collection and accounts receivable. Here, AI makes it possible to prioritise reminders, better segment portfolios, identify payment behaviour and concentrate efforts where they will have the greatest effect. It's not spectacular. It's simply useful. And, in finance, usefulness is often better than a brilliant demonstration.
Reporting: a real comfort, but needs to be carefully monitored
Finally, the reporting is now attracting a great deal of attention. The tools already know how to write an initial comment, propose a summary, structure a note or answer questions formulated in natural language. The convenience is real. But there is also a danger. A well-written text does not always provide an accurate analysis. AI can produce a convincing formulation without guaranteeing the economic relevance of what it puts forward. This is why AI-generated reporting must remain a starting point, never a turnkey truth.
The different types of AI for finance and their benefits
At this point, the debate becomes more interesting. Because almost all publishers now display an «AI» layer. This mere presence says nothing about the real value.
For a CFO, there are three levels.
Firstly, the’Comfort AI It facilitates use, summarises, searches and rephrases. Then the’Performance AI It genuinely improves a process, for example by improving forecasting, anomaly detection or exception management. Finally’Orchestration AI It aims to link several actions together in a semi-autonomous way. This last category opens up interesting prospects, but it also demands the highest level of skill.
In practice, we shouldn't be asking «does this tool contain AI? But rather: »Whathat does AI really do, for what gain, with what data, under what control?
The benefits of AI in finance
| Need | What AI brings | Uses | Tools |
|---|---|---|---|
| Budget / forecast | Forecasts, scenarios, analysis of variance, trend detection | Forecasts, rolling forecasts, simulations, explanations of variances, summaries for the Executive Committee | Pigment, Anaplan, Vena, Jedox, Workday Adaptive Planning |
| Closing / accounting | Anomaly detection, reconciliation, exception prioritisation | Reviewing entries, justifying accounts, reporting unusual discrepancies | BlackLine, Workiva, Trintech, FloQast |
| Accounts payable | Document extraction, coding, matching, exception management | Invoice reading, order-receipt-invoice reconciliation, duplicate detection | Yooz, Esker, Libeo, Basware, Pennylane (SMEs) |
| Treasury / cash management | Cash forecasts, summaries, alerts, decision support | Forecast cash receipts and disbursements, analyse positions, detect discrepancies | Kyriba, Agicap, Diapason, Sage Cash Management |
| Accounts receivable / collection | Prioritisation, scoring, recommendations for action | Customer segmentation, prioritisation of reminders, late payment analysis | My DSO Manager, Upflow, Quadient (YayPay), Sidetrade |
| Reporting / financial communication | Assisted drafting, synthesis, natural language queries | Comments on reports, summary notes, committee materials | Microsoft Copilot (Excel/Power BI), ChatGPT Business |
| Internal control / audit / compliance | Research, sorting, identifying exceptions, documentary assistance | Preparing files, reviewing documents, helping with documentation | MindBridge, Caseware, HighBond (Diligent) |
Finance isn't losing its place, it's changing levels
One of the most persistent fantasies is that AI will eventually replace the finance function. This idea misses the mark. Finance does not derive its value from producing figures alone. It organises reliability, interpretation, consistency, discipline and trust. And these dimensions are becoming even more important as the tools become more powerful.
In this new landscape, the role of the CFO is not shrinking. It is redefined. It is becoming less focused on certain production tasks and more focused on the architecture of decisions, data quality, governance of tools, supervision of processes and securing information.
AI in corporate finance is no longer a gadget. It is already acting on specific, useful processes: forecasting, closing, invoicing, cash management, recovery, reporting and control. But it can only deliver on its promises within a demanding framework. You need clean data, structured workflows, real human supervision, clear governance and well-chosen use cases.
Basically, the real changeover is not just about technology. It's about the maturity of the finance function. The finance departments that make the most progress will not be those that adopt the most AI slogans. They will be the ones who choose the right uses, demand reliability, secure the data and keep their hands on what really counts: the quality of the information, the consistency of decisions and trust.
