Improving Financial Performance with AI: Automation, Reporting and Steering
Automating repetitive tasks, centralizing data and generating reliable real-time reports allows finance teams to save several hours per week and focus on analysis. But every approach has its price.
Automating repetitive tasks, centralizing data and generating reliable real-time reports allows finance teams to save several hours per week and focus on analysis. But every approach has its price.
Context: A B2B distribution company wanted to reduce compliance gaps and monthly closing delays.
Action: Implementation of a sovereign AI platform connected to Odoo and point-of-sale systems.
Result: 67% reduction in reconciliation errors, monthly closing time reduced from 12 to 5 working days in six months.
Table of Contents
Context
DistriCo, a French industrial distribution group, has 420 employees spread across 18 agencies and generates annual revenue of €185 million.
The finance department, led by a CFO with 14 people, manages routine accounting, bank reconciliation, payroll and monthly dashboards. Data comes from three main sources:
- Odoo ERP (accounting, purchasing, sales),
- Cash register and point-of-sale systems (POS),
- Banking platform (automated statements).
Each month, the team spent 18 hours copying data between systems, correcting reconciliation discrepancies and formatting reports for management.
The CFO estimated that 40% of the team’s time was spent on manual, low-value tasks, and data entry errors led to an average of two customer complaints per month related to incorrect invoices.
Internal survey conducted in January 2025:
→ 73% of employees find the monthly reports « difficult to interpret ».
→ 61% believe financial indicators arrive too late for strategic decisions.
DistriCo therefore sought to modernize its approach, without resorting to an expensive global ERP or a U.S. cloud solution that is not GDPR compliant.
After a restricted call for tenders with three French providers, DATALIA was selected for its expertise in sovereign AI and its track record in successful deployments within distribution groups.
The specifications set three objectives:
- Reduce bank reconciliation errors by 50% within six months,
- Automate 70% of recurring entries,
- Generate dynamic financial reports accessible in one click.
These objectives had to be met while maintaining full traceability of data flows, in accordance with the requirements of the CFO and the compliance department.
The project began in March 2025, with a pilot phase on two agencies before gradual rollout across the entire group by September 2025.
This case study details the approach taken, the technologies used and the lessons learned from this transformation.
Problem and Objectives
Before the intervention, the monthly accounting closing process followed a linear but fragile pattern:
| Step | Responsible | Average Duration | Error Rate |
|---|---|---|---|
| Export of sales (POS) | Administrative | 2h | 3% |
| Entry in Odoo | Accountant | 4h | 5% |
| Bank reconciliation | CFO | 5h | 8% |
| Report generation | Financial Controller | 4h | 2% |
Each step involved manual copying, a source of errors and time loss. Bank reconciliation, for example, relies on fixed rules coded by the CFO: discrepancies above €50 required manual validation, adding 2 to 3 hours of processing.
The project objectives were clearly defined:
- Reduce reconciliation errors by 50% by automating matching rules via AI,
- Automate 70% of recurring entries (sales, fixed costs, assets),
- Generate dynamic reports updated in real time and accessible through a single dashboard,
- Ensure GDPR compliance and full traceability of processing within six months.
These objectives were validated by the steering committee, composed of the CFO, the CISO and the Quality Manager.
The allocated budget was €85,000 over 12 months, including development, integration and training.
The CFO emphasized two non-negotiable requirements:
- Data must never leave the group’s infrastructure,
- Every AI decision must be reversible and auditable.
These constraints guided the technology choice toward a self-hosted AI solution, integrated into the existing Odoo ecosystem.
The Implemented Solution
DATALIA proposed a modular architecture based on three pillars:
- DATALIA.App, a sovereign AI hosted internally, capable of predictive analysis and process automation,
- Odoo, already present in the group, as the single entry system,
- API Connectors, to link cash register systems (POS) and the banking platform.
The following diagram describes the implemented data flow:
POS → API Connector → DATALIA.App → Odoo → Dynamic Reports → CFO Dashboard
Bank → API Connector → DATALIA.App → Odoo → Automated Reconciliation
DATALIA.App was deployed on an internal virtual machine, hosted in the group’s data center, and configured to comply with the security policies established by the CISO.
The key modules enabled include:
- Financial Automation Engine : automation of recurring entries using predictive analysis models,
- Intelligent Reconciliation Module : bank reconciliation assisted by AI with decision traceability,
- Dynamic Reporting Studio : generation of customizable reports exportable in PDF and Excel.
The pilot phase concerned two agencies in Lyon and Marseille, representing 18% of annual revenue. It allowed validating data flows and adjusting AI models before gradual deployment.
The major deployment stages:
| Phase | Date | Action | Validation |
|---|---|---|---|
| Pilot | March-April 2025 | Deployment on 2 agencies | Weekly meeting with CFO |
| Expansion | May-June 2025 | Replication on 6 additional agencies | CISO internal audit |
| Full Rollout | July-September 2025 | All agencies (18) | User acceptance validated |
| Closure | October 2025 | Delivery of final reports | Presentation to the Executive Committee |
Training was conducted in small groups, with hands-on workshops on report configuration and automation supervision.
A data processing register was updated and signed by the group DPO, in accordance with the GDPR.
This approach allowed minimizing operational disruptions while ensuring a gradual increase in team skills.
Results
The following indicators were measured six months after full deployment:
| Indicator | Before (February 2025) | Objective | After (September 2025) | Change |
|---|---|---|---|---|
| Monthly hours dedicated to entries | 72 h | -70% | 22 h | -69% |
| Reconciliation error rate | 8% | -50% | 2.6% | -67.5% |
| Monthly closing delay | 12 days | -50% | 5 days | -58% |
| Number of unresolved bank discrepancies | 12 | -60% | 5 | -58% |
| Time to generate a report | 4 h | -80% | 48 min | -80% |
The table above shows a significant improvement across all key indicators.
In addition, the number of customer complaints related to billing errors decreased by 73%, from 2.3 to 0.6 per month on average.
The CFO estimates that the team now saves approximately 50 hours per month, equivalent to one full-time position.
These hours have been reallocated to discrepancy analysis and preparation of management meetings.
The internal audit confirmed that 94% of AI decisions were automatic, with 6% subject to human review, remaining below the 10% risk threshold.
A regular review was established with the compliance department to monitor model evolution and ensure traceability.
Management praised the improvement in quality and responsiveness of financial reports during its quarterly meeting in October 2025.
What Didn’t Work
- Automation of operating expenses: Supplier invoice recognition in PDF format failed during the first months. DATALIA.App could not correctly extract VAT amounts in 23% of cases. An alternative solution was implemented: sending files in a normalized XML format through the dedicated messaging system.
- Dependence on predictive models: The sales trend analysis model showed inaccuracies during the launch of a new product. An alert was triggered when a discrepancy of more than 15% was detected, forcing manual revalidation of the forecast.
- User adoption: Some financial controllers took more than two weeks to adopt the new dashboard. Personalized support was needed to overcome resistance to change.
However, these difficulties were anticipated and integrated into the mitigation plan established from the pilot phase.
Key Takeaways
- Sovereign AI is achievable for CFOs: By relying on a self-hosted solution like DATALIA.App, it is possible to modernize processes without losing control of data.
- The pilot is essential: Testing the solution on a subset of agencies allowed adjusting AI models and workflows before large-scale deployment.
- Continuous training is a lever: Monthly workshops facilitated smooth tool adoption and reduced misuse errors.
- Indicators must be monitored regularly: Quarterly performance reviews enabled quick correction of any deviations.
The digital transformation of the finance function goes beyond automation: it requires process reorganization, skills development and clear governance.
DistriCo successfully combined operational performance and regulatory compliance through an AI solution integrated into its Odoo ERP, hosted internally and driven by a highly involved team.
This model is replicable in other groups of similar size, provided the connectors and business rules are adapted to each context.
The next step planned by DistriCo is to extend the use of AI to budget forecasting and detection of accounting anomalies, by integrating a supervised machine learning module.
This evolution should further increase efficiency while anticipating financial risks.
To learn more about AI solutions applied to finance and performance management, visit the DATALIA website:
A free audit of your financial processes can quickly identify the most effective automation levers for your company.
FAQ
What is the difference between financial automation and predictive AI?
Automation follows programmed rules to execute repetitive tasks (data entry, reconciliation). Predictive AI learns from data to anticipate trends, model scenarios and detect anomalies. At DistriCo, both approaches coexist: automation manages routine flows, while AI predicts discrepancies.
Is it possible to deploy sovereign AI without using a foreign cloud?
Yes. DATALIA.App can be installed locally within a company’s infrastructure. At DistriCo, all processing is hosted in a French data center, compliant with GDPR and audited by ANSSI. No financial data leaves the premises.
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