The impact of AI in Finance is not limited to a few new features in your software. It is a deep transformation, brick by brick, and then in their interactions.
Yet, the subject remains difficult to interpret. Vendors each communicate within their own scope. Without a global perspective, it feels like everyone is doing the same thing. This article offers exactly that perspective: less detail, more overall vision.
These bricks exchange data with each other. Historically, AI and Data Science remained outsourced: 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 operations. 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 the creation of complete business applications on demand. It becomes a candidate for the role of Finance control tower. |
Be careful, however, of a frequent pitfall: 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 overall 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 today 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 to experiment with. Gains are measured quickly. The same applies to learning.
No. It can play a role as a control tower and orchestrator between applications. But ERP and EPM provide a structuring architecture, robustness, and governance that the data platform does not replace.
Automapping*: technique that allows for the automatic matching of accounts in different formats between two systems, without manual intervention. Used notably for reconciliation and consolidation.
Interco (interco reconciliation)*: process of reconciling transactions between entities of the same group to eliminate internal flows during consolidation.
Reconciliation (lettrage)*: accounting operation that consists of matching debit and credit entries to identify payments corresponding to invoices.
MCP (Model Context Protocol)*: standard protocol that allows a corporate conversational agent (Claude, Mistral, Copilot…) 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 allows for the conversion of images or scanned documents into digitally usable text.
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