AI in Finance: Automation and Decision-Making

A SME finance department reduced its monthly closing time from 8 days to 24 hours through AI automation and automated reporting.

Can accounting and financial control services really save 75% of time on a monthly closing? In a medium-sized organization, the answer depends less on the tool's power than on the rigor of the setup between data sources and reporting dashboards. This is demonstrated by the following case study, conducted in a French industrial SME with 180 employees, where the finance department went from a closing leading to 8 days of delay each month to an automated closing in less than 24 hours, without resorting to a traditional ERP or a third-party editor. The lever is not AI itself but the data architecture and integrated validation rules. The goal was not to replace financial controllers, but to make data available when needed. This is not a laboratory experiment: it is a deployment carried out over 14 months, with iterative driving and continuous measurement of deviations.

We will detail why most AI projects in finance fail at the operational level, as observed by the DATALIA consulting firm in over 30 similar missions. The main cause is not technical: it is a lack of alignment between accounting rules, business flows and performance indicators. In this case study, every technological choice has a measurable equivalent in hours saved or errors eliminated. The final result — a 75% time saving on the closing, without extended working hours — comes precisely from this rigor of setup.

Context: A finance department under pressure and scattered data

The company, an industrial subsidiary of the [Masked Name] group, manages a production site in the Auvergne-Rhône-Alpes region. Its finance department has 3 employees: a senior accounting project manager, a junior financial controller and an HRIS expert. Accounting data comes from three sources: Odoo (general accounting), a production scheduling software (weekly CSV exports), and a complementary pension fund (monthly Excel files). The monthly closing involved 40 hours of manual processing each month, with an error rate of 12% on bank reconciliations. The average time between the end of the month and the availability of performance indicators was 8 days. This delay directly impacted management responsiveness on purchasing and cash flow decisions.

The initial assessment was established using DATALIA's audit grid, measuring 16 friction points. The main one: 70% of accounting discrepancies originated from missing or misclassified entries in Odoo, often related to non-standardized end-of-month operations. No flow was automatically fed back into the performance dashboard. Cash flow alerts, calculated manually in Excel, were rarely available before the 5th working day of the following month. This delay was deemed unacceptable by management, which demanded real-time visibility on non-accounting commitments. This context guided the choice of a phased approach, with progressive automation of accounting flows, then automated reporting incorporating operational performance indicators.

The 3 critical data sources

The first step was to map each flow with a process mapping template, identifying break points. The production scheduling exported 3 weekly CSV files, but 18% of rows lacked an accounting code. Direct interfacing corrected this rate through a batch assignment rule by production batch. The second flow, the pension fund, required a JSON format generated manually each month: automation eliminated this bottleneck. Finally, Odoo did not automatically transmit standardized end-of-month entries (depreciation, provisions): an AI script was set up to generate these entries according to an internal regulatory framework validated by the DPO.

Problem: Reporting always late, obsolete alerts

Key financial indicators (cash flow, operating margin, debt ratio) were produced through a manual validation chain: extraction → consolidation → verification → distribution. This chain took an average of 6 working days. Cash flow alerts, based on fixed thresholds (e.g., exceeding €10,000), were ineffective because they did not incorporate the dynamics of procurement. Management wanted dynamic indicators, but without resorting to a traditional BI tool, considered too expensive for an SME. This blocking point led to the adoption of an approach based on micro-tools connected via API, with an AI layer for data normalization and automatic generation of dashboards.

The solution: Sovereign AI for operational finance

The chosen solution combines a self-hosted AI engine at the customer's site (via DATALIA.App) with an automated workflow layer. Data is extracted daily via OData connectors from Odoo, Python scripts for CSV/HRIS files, and a REST API for the pension fund. Once centralized in a local relational warehouse, the validation rules — 127 control rules — are executed by the AI. Abnormal entries are reported to the finance department, but 89% of entries pass without human intervention. Daily reporting is automatically generated, with dynamic alerts recalculated every night. Everything is hosted on the customer's infrastructure, with data encryption and complete access traceability, in accordance with GDPR.

The 5 deployment steps

1. Flow mapping : 15 days of audit to inventory 23 business flows, prioritized according to their impact on the closing.
2. Data warehouse installation : dedicated local server, with daily replication of source databases.
3. AI connector deployment : multi-format extraction script, with incremental schema validation.
4. Configuration of accounting rules : integration of the general chart of accounts and the company's internal rules.
5. Automatic report generation : Power BI Embedded dashboard, with dynamic alerts via webhook.

Results: 75% time savings and increased reliability

What didn't work: Errors to avoid

The first prototype, tested in 6 weeks, failed because the access control layer was not integrated from the start. HRIS data, sensitive with regard to GDPR, was transmitted through an unencrypted pipeline for 3 weeks. This issue delayed the deployment by 8 weeks. Another major error: the first version of the dashboard displayed aggregated indicators without breakdown by product. Decision-makers rejected the report because it did not allow them to locate anomalies. This feedback led to a redesign of the data model, with 5 additional dimensions added (product, site, channel, status, transaction type). These failures, although costly, allowed validating an approach based on short iterations, measured by adoption and accuracy indicators.

Key takeaways for finance departments

• Automation works if the rules are written and validated before the AI.
• Dynamic reporting requires a centralized data architecture, not isolated connectors.
• Self-hosted AI avoids the risk of leaking sensitive data.
• Alerts must be calculated every night, not on fixed thresholds.
• Iterative management allows correcting deviations without blocking the project.

To remember: Reproducibility and success conditions

This project is reproducible in an industrial or service SME, subject to two conditions: data management at the source, and clearly defined governance. The approach based on micro-tools connected works if the flows are standardized and the validation rules are codified. The 75% time saving on the closing is not linked to a single tool: it is the combination of local AI, automated workflow and dynamic reporting. What makes this experience reproducible is the absence of dependency on a third-party editor for the AI engine. The finance department retains control over data, access and business rules. This model has been validated by DATALIA on 5 similar structures, with average savings of 60 to 80% on recurring accounting processes.

Frequently asked questions

Can AI replace a financial controller?

No. AI generates raw data and alerts, but strategic analysis remains human. In this case, the controller gained 30 hours/month to focus on predictive analysis.

What is the cost of a sovereign AI solution for finance?

The deployment costs between €20,000 and €50,000 depending on the complexity of flows. Here, the ROI was achieved in 7 months thanks to hours saved on the closing.

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