Here we will analyze the unnatural but relevant link—if placed under control—between a Finance function anchored in its need for truth and the arrival of generative AI* at its service, whose strength is precisely to invent narratives but also figures. An irreconcilable paradox?
Beyond the figure that allows a decision to be made, the explainability of that figure is also key. When announcing to a BU manager overseeing several billion that they are not meeting their objectives and that, consequently, corrective action will be taken, one must be able to argue, document, and be sure of the sources. This is the notion of explainability.
And we regularly come across management controllers who spend one hour producing an analysis and another hour checking the calculation method and sources. How many meetings have ended after 5 minutes because no one was aligned on the figures?
It is in the DNA of Finance, and it also allows for direct action without hesitating over the numbers.
| Key takeaway: A figure without a source or documented method is worthless to a BU manager. |
Generative AI is a family of algorithms based on LLMs* that has the fascinating ability to generate natural language text that appears completely realistic. And for good reason: the major LLMs on the market have analyzed the entire global web and are capable, from a fragment of a sentence or a question, of predicting the most credible sequences of words relative to the context. These are not exactly the most relevant; they are the words that are statistically most probable according to its digital vision of the world. Therefore, it is not the most accurate result via logical reasoning that is produced, but the generation of the most probable.
Another important nuance: to give an even greater illusion of naturalness, the word sequences are not always those with the highest probability but are drawn at random from among the most probable. Why? To give an impression of naturalness. If you ask it the same question twice, it will phrase the answer in two different ways with small nuances. This moves even further away from a deterministic logic which, via documented deduction, leads to a stable result.
It is on this principle that ChatGPT, Claude, Mistral, or Gemini respond to you so fluidly, regardless of the context.
| Key takeaway: An LLM generates the most probable word, not the truest. |
An LLM alone has no computational logic. If it answers “2+2=” correctly, it is not because it calculated it, but because it has seen this answer millions of times in its training data. As soon as we move away from simple or highly documented calculations, an LLM alone can “hallucinate”: invent a result that appears real, without ever detecting the error, and sometimes build upon it with complete confidence.
This limitation is aggravated by a second problem: unexplainability. Even when an LLM is trained to break down its reasoning into more readable steps, these steps remain probabilities, never a logical and reproducible deduction that could be audited step-by-step. For your function, which thrives on traceability and argumentation before a BU manager, this is the number one point of vigilance.
The good news: this risk is identified, understood, and we already know how to put it under control; that is the whole subject of the rest of this article.
| Key takeaway: An LLM alone does not calculate and cannot justify its reasoning step-by-step. |
We can see that on paper, the raw combination of Finance and generative AI seems antinomical and explosive. Yet there is enormous potential if two essential rules are respected:
Nothing very new, in short. Let’s detail this approach.
| Key takeaway: Only apply AI where it makes sense, and always validate it. |
In Finance, you don’t just work with numbers. The decisions you are asked to make are certainly based on figures, but also on contextual information which can be narrative, graphic, video… Market context, competitor behavior, the socio-economic climate, evolving standards, your own internal rules from one division to another… these are all formats that this type of algorithm is relevant for linking, analyzing, and synthesizing. Specifically trained for Finance, AI agents* are excellent analysts that can absorb and contextualize a mass of information that you could not process otherwise.
| Key takeaway: Generative AI excels at analyzing context, not at calculating. |
While LLMs do not know how to calculate, they are the new translators and analysts between you and your complex tools.
Let’s take a concrete example: explaining an 8% variance on a cost line compared to the budget during a closing. Traditionally, a management controller manually crosses several sources—extractions from their EPM tool, comments sent back by subsidiaries, history from previous months—to write a reasoned variance comment. This work easily takes 30 to 45 minutes per significant line, repeated over dozens of lines each closing.
With a correctly configured AI agent, the mechanics change: the calculation tool (your EPM) produces the exact and verifiable variance: the deterministic data does not change hands. The AI agent then reads the subsidiary comments, cross-references them with the history, identifies recurring causes, and writes an initial structured draft of the comment. The management controller then spends their time no longer searching and writing, but verifying, challenging, and enriching, reducing the work to 10-15 minutes of quality control rather than 30-45 minutes of production.
The gain is therefore not in producing a different figure: the figure remains the same, produced by the same reliable calculation tool. But it frees up the management controller’s time from the most time-consuming and least analytical part of the work. Tools perform deterministic* calculations that can be 100% certain, and LLMs handle writing the context and transmitting the correct hypotheses to them. Each solution focuses on its strengths for a combination that multiplies the potential of your teams.
| Key takeaway: The figure remains the same; it is the context writing time that disappears. |
To reduce hallucinations in your future agents, a simple technique consists of breaking down the tasks assigned to them into sub-tasks, each assigned to a dedicated agent. This technique makes it easier to train these algorithms and reduces their hallucinations, as they are less exposed to contexts they do not know.
Thus, one could have a single agent to whom all questions are asked—documentary research, P&L analysis, consolidation of subsidiary comments, identification of causes, or production of reporting… But it would be extremely complex to train correctly and very sensitive to any change in scope or data.
We therefore favor a collection of agents that collaborate with each other. This often includes a “Pilot & Synthesis” agent that receives questions and redirects them to the right sub-agents (analyst, formatting, web search…) and synthesizes the results. Each is more specialized and easier to control and evolve.
| Key takeaway: Several specialized agents are better than a single generalist agent. |
In this galaxy of agents that will equip your department, many will be dedicated to monitoring and challenging the results of their peers. We know that LLMs hallucinate, but we can train other LLMs to detect these hallucinations, poor report layout, figures without real references… This is now a standard. Every LLM process has its integrated LLM safeguard.
| Key takeaway: Every AI agent should have its own AI safeguard. |
This is the part no one wants to read. No, generative AI is not magic. If your data is chaotic, siloed, inconsistent from one source to another, and with business definitions of varying scope, then AI or no AI, you will produce hallucinations = figures you believe are real but have no reality. Adding an autonomous AI agent to these areas greatly increases the potential for hallucination.
Exploiting the potential of generative AI therefore implies up-to-standard governance and data quality. AI can help improve quality, but it cannot do it without you.
| Key takeaway: Without quality data, AI amplifies hallucinations rather than avoiding them. |
There is a risk we haven’t addressed yet that often causes concern faster than hallucination: what happens to data once it is entered into a generative AI tool? There is a fundamental difference between consumer use, where data can be kept and reused by the publisher, and secure enterprise use, where it is contractually protected and not reused for model training.
Pasting a P&L extract or a supplier negotiation into an unvalidated tool is equivalent to letting sensitive data leave your control perimeter—this is what is called “Shadow AI.” The rule is simple: the same confidentiality reflexes that apply to an email apply to an AI prompt. And this is not a regulatory vacuum: the European AI Act and your internal controls (SOX or equivalent) apply to the results produced by an AI exactly as they do to any other result.
| Key takeaway: An AI prompt deserves the same confidentiality reflexes as an email. |
Even with these precautions, risks of hallucination always remain. Yes, our analytical power will be multiplied, yes our time to action will be reduced, but let us never neglect the time for monitoring and validating results. It is in our nature, in our duties, and it should be natural. We remain responsible for the figures and analyses produced. Part of the evolution of our role will be to understand when and why they hallucinate and to enrich the safeguards and controls we apply to them.
| Key takeaway: AI multiplies your analytical power, but you remain responsible for the result. |
It is an extremely powerful new duo that does not just perform existing tasks faster, but opens up new analyses and projections that were previously humanly impossible. Its integration must be progressive, applying it where it makes sense, while controlling its potential deviations and, above all, never abdicating our role as those responsible for the result.
By Stéphane Portier, Head of Innovation at MeltOne:
The impact of AI in Finance: how are your architectures evolving?
No, not alone. An LLM can hallucinate on complex calculations. Figures must always come from a deterministic calculation tool (EPM, ERP…), with generative AI handling context and synthesis.
Two main risks: hallucination (an invented result that appears real) and data security (Shadow AI, use of tools not validated by the organization).
By breaking down tasks between specialized agents that are easier to train and control, adding dedicated quality control agents, and systematically maintaining human validation.
No. It automates research, cross-referencing, and first-level writing tasks, but the responsibility for the result and its validation remains human.
Generative AI: a family of algorithms based on LLMs, capable of generating text, images, or other content in natural language from an instruction.
LLM (Large Language Model): a language model trained on immense volumes of text, which predicts the most probable sequence of words in a given context.
AI Agent: a program built around one or more LLMs, specialized in a business task (analysis, research, synthesis…) and capable of interacting with other tools.
Deterministic calculation: a calculation whose result is always identical for the same input data, as opposed to a probabilistic generation like an LLM.
These bricks exchange data with each other. Historically, AI and Data Science remained externalized: too technical, too specific.
This is no longer the case.
Important point: AI does not revolutionize the ERP. It enriches it through successive layers, based on the historical core. This is consistent: what we look for in an ERP is process, rigor, and governance. We don’t overturn that.
| 💡 Key takeaway: AI in the ERP does not revolutionize historical functioning. It enriches it through successive layers: specific AI modules, conversational agents, autonomous agents, then MCP connection to the outside. |
The layered logic is identical to the ERP. The difference: the EPM is not the backbone of the company. It is the steering cockpit; more flexible and more in the hands of the business users. Developments therefore arrive there faster.
💡 To go further on these EPM use cases: Finance AI Agents: 6 concrete use cases by Stéphane Portier, Head of Innovation at MeltOne.
| 💡 Key takeaway: The EPM becomes a true steering assistant, not just a reporting tool. AI in the EPM follows the same layered logic as the ERP, but with higher velocity. The EPM is not the backbone of the company: developments there are faster and more in the hands of the business users. |
This brick is historically the most technical. And yet, it is the one evolving the fastest under the impulse of AI. The goal: reduce dependence on IT and give Finance teams true autonomy over their data.
| 💡 Key takeaway: The Modern Data Stack is enriched with 3 AI layers: management and discovery of business context, autonomous AI agents for Finance, and creation of complete business applications on demand. It becomes a candidate for the role of Finance control tower. |
Be careful, however, of a frequent drift: believing that the data platform can replace everything. Finance applications provide a structuring architecture, robustness, and governance that we look for in our professions. It orchestrates, it completes, it corrects, but it does not replace.
| 💡 Key takeaway: The impact of AI in Finance on the global architecture manifests in 3 points: improved communication between applications via MCP, maintained importance of data quality and governance, and the persistence of an inter-application No Man’s Land that the data platform can help address. |
The impact of AI in Finance manifests in layers within each brick (ERP, EPM, data platform). It improves communication between applications via MCP servers and extends analysis and automation capabilities. It does not replace existing architectures: it augments them.
An MCP (Model Context Protocol) server allows a corporate conversational agent (Claude, Mistral, Copilot…) to query a Finance application in natural language, without specific development. ERP, EPM, and data platforms now expose their own MCP servers.
No. AI enriches these solutions through successive layers. It does not change their nature. An ERP remains the transactional system of record. An EPM remains the steering cockpit. AI adds autonomy, proactivity, and connections to them.
Start with the native agents of your existing solutions. Evaluate the quality and governance of your data. Identify a non-critical process for experimentation. Gains are measured quickly. The same applies to learning.
No. It can act as a control tower and orchestrator between applications. However, ERP and EPM provide a structuring architecture, robustness, and governance that a data platform does not replace.
Automapping*: a technique that automatically matches accounts of different formats between two systems without manual intervention. Used particularly for reconciliation and consolidation.
Interco (interco reconciliation)*: the process of reconciling transactions between entities within the same group to eliminate internal flows during consolidation.
Matching*: an accounting operation that involves reconciling debit and credit entries to identify payments corresponding to invoices.
MCP (Model Context Protocol)*: a standard protocol that allows a corporate conversational agent (Claude, Mistral, Copilot, etc.) to query and interact with third-party applications in natural language, without specific development.
Modern Data Stack*: a modern set of cloud technologies dedicated to the collection, storage, transformation, and visualization of corporate data.
OCR (Optical Character Recognition)*: optical character recognition technology that converts images or scanned documents into digitally usable text.
Let’s talk about your project and discover how MeltOne can turn your challenges into concrete, high-performing solutions.
