AI Finance: +30% Productivity through Automation and Data

How an 85-employee SME transformed its financial performance by automating monthly closing and adopting predictive analysis. All without increasing headcount or exposing sensitive data.

A monthly closing that lasts 72 hours. A finance director who is overwhelmed. A team that's leaving. This is Lila's story, a finance director at an 85-employee food distribution SME with 12 sales points and a heterogeneous accounting system. Every end of the month, her team of 4 people spent 72 hours closing manually, retyping data between Sage, Excel, and PDF reports. Result: consistency errors, ever-tightening deadlines, and an increasing number of internal requests for more reliable indicators.

"We close every month as if starting everything from scratch," Lila confided. "We lose 40% of our time on data entry. And no one can guarantee that the numbers truly reflect the reality on the ground."This is typical of hundreds of intermediate-sized organizations that accumulate tools without connecting them. For a finance director (S5), the risk is investing in a tool that doesn't follow the process. The risk for a finance director is not being able to justify the ROI.p>

Context: a monthly closing that costs €18,000

Lila manages an annual budget of €8.2 million. Her team of 4 devotes 72 hours per month to closing. At the average hourly rate of this sector (€45/hour HT), this represents €3,240 per month — over €39,000 per year for an activity that, by nature, adds no business value.p>

The problem does not stem from a lack of tools. Sage manages accounting, Excel handles dashboards, Google Sheets manages bank reconciliations. The problem comes from the fact that nothing talks to anything. Data flows through CSV files sent by email, formulas copy-pasted, and multiple versions multiplying across workstations.p>

"I have 47 Excel files on my desk," Lila says. "And I no longer know which one is the right one."p>

This is the universal failure mode: the project was delivered, and no one uses it. Except here, there is no project. There is a costly status quo.p>

Problem statement and measurable objectives

Before any automation, one cannot improve what has not been mapped. Lila and her team first had to answer three questions:

1. Where are the 72 hours lost each month?
2. What are the recurring errors and their actual cost?
3. What is the gain from targeted automation?

The objectives set following an internal audit:

• Reduce monthly closing time from 72h to 24h (i.e., -66%)
• Eliminate 3 types of recurring errors (recurring entries, manual reconciliations, currency conversions)
• Gain 40 hours/month in team time for analysis and forecasting

These objectives hinge on three requirements:

• Reliability: data must be unique and traceable.
• Reversibility: the system must be stoppable without data loss.
• Compliance: any sensitive data transits through a GDPR-certified hosting.

For a finance director, the keyword is not "automation". It is "traceability".

The solution: an automated value chain, not another tool

Rather than installing a complete ERP or a general-purpose AI solution, DATALIA proposed a modular approach:

Step 1: mapping data flows

We start with a deliverable that any finance director can use alone, without DATALIA:

Framework for mapping financial flows

To gather: screen layouts of the 4 software applications, exports from recent months, organizational chart of bank reconciliations.

1. List each data source (Sage, bank, sales points, suppliers).
2. Identify transfer points (CSV export → email → Excel import).
3. Note moments where human intervention occurs (reconciliation, manual entry, correction).
4. Estimate time spent per intervention.

Output: a table indicating the percentage of time that is automatable vs. manual.

Annotation: this method takes 2 to 3 hours. It fails if real screens cannot be accessed. Beware of informal flows — often the most costly ones.

The initial assessment:

65 out of 72 hours (90%) can be automated. The remaining 7 hours correspond to special cases — humans remain in control of exceptions.

Step 2: connecting existing tools

DATALIA connected Sage, online banking, and exports from 12 sales points via an API layer. No migration. No replacement. Just a reliable link between what already exists.

Each night, an automated flow:

1. Extracts entries from Sage (via API or dedicated connector).
2. Automatically reconciles bank transactions.
3. Applies currency conversion rules in real time.
4. Pushes results to a single dashboard, accessible via SSO.

Nothing magical. Just operational rigor.

Step 3: integrating a sovereign AI layer

The key for a finance director: We don't do AI for the sake of AI. We do AI to answer a specific question.

DATALIA.App, the company's sovereign AI, is configured to:

• Analyze reconciliation discrepancies and propose hypotheses.
• Group recurring entries by pattern and suggest automatic rules.
• Generate dynamic dashboards on demand, respecting access rights.

The AI is hosted on the company's own infrastructure. No sensitive data leaves the network. Every request is logged. Every response is audited.

Step 4: moving to predictive analysis

The real differentiator for a finance director: shifting from monthly closing to weekly forecasting.

Once the 65 recurring hours are automated, the team has 40 extra hours per month. These hours are redirected towards:

• 5h/week: consolidating discrepancies and adjusting hypotheses.
• 3h/week: analyzing customer trends by segment.
• 2h/week: reviewing cash flow streams and alerts for overruns.

AI doesn't replace thinking. It accelerates it.

Results: 72h → 24h, with 40h freed up for analysis

6 months after deployment:

Measurement period: 6 months (March to August 2024).

Lila now has 40 free hours per month for analysis. She produces real-time dashboards, detects spending trends 3 weeks earlier, and can justify every figure to management.

What didn't work

The first version of the system automated 80% of flows but broke 3 essential special cases:

• Discrepancies related to outstanding check purchases.
• Exceptional entries related to non-recurring marketing campaigns.
• Bank reconciliations for a sales point located abroad (different currency and time zone).

Solution: an "exclude from automation" button with manual alert. The human decides. The AI proposes. No one overlooks a case.

Secondly, the first version did not log AI actions sufficiently. During an internal audit, the DPO demanded full traceability. DATALIA added an audit module that records every automated decision — source, applied rule, result.

Lesson: Automation without traceability is technical debt. And for a finance director, a debt of trust.

Key takeaways

• Automate the normal path, keep humans for exceptions. 90% of value lies in 10% of the process. Target these 90% first.
• Never automate data to hide it. AI should clarify rules, not mask them. A finance director decides, the AI serves.
• ROI is calculated on freed time, not saved time. 48 hours/month freed = 576 hours/year. At €45/hour, that's €26,000/year of value created.
• Compliance is a condition, not a constraint. Sovereign and traceable hosting is essential from the first deployment.
• Deliver in iterations. 3 automated and validated flows are better than 12 improvised ones.

Why this is reproducible

This approach does not work only for a food distribution SME. It is based on three universal principles:

1. Map before automating. Every organization can follow the framework above.
2. Connect, don't replace. Automation does not question existing tools; it links them.
3. Keep control over exceptions. Humans remain decision-makers for special cases.

Success conditions:

• A finance director or control manager available 2 hours/week during the deployment phase.
• Access to APIs or exports of existing tools (Sage, QuickBooks, online banking).
• A GDPR-compliant hosting for sensitive data.

For a 300-employee mid-sized company, the same framework can free up 120 hours/month. For a 1,000-employee group, 400 hours/month.

The question is no longer "Does it cost a lot?" The question is "How much does it cost not to do it?"

Discover how DATALIA supports finance directors in digital transformation.

Frequently asked questions

Can an SME really automate its monthly closing?

Yes. The break-even point is reached from 30 hours of recurring processing per month. Beyond that, automation immediately pays off. For a structure of 85 employees like in our case, ROI is achieved in 4 months.

Can AI replace my closing team?

No. AI automates 90% of recurring flows. The remaining 10% of exceptions remain managed by humans. The goal is to free up time for analysis, not to eliminate positions.

Checklist for framing a financial automation project

To check before signing:

• Are automatable flows identified and mapped? [ ]
• Do existing tools have exploitable APIs or exports? [ ]
• Is the data host certified ISO 27001 GDPR compliant? [ ]
• Is each automated action traceable and reversible? [ ]
• Is the team trained to validate and correct results? [ ]

Output: a green light or clear blockage before commitment.

Annotation: this checklist helped avoid 2 abandoned projects among DATALIA clients. It takes 1 hour to complete.

Automate your monthly closing and free your team for analysis with DATALIA: DATALIA →