Improve financial performance with AI and automation

How to reduce costs and improve financial forecasting through AI, automated workflows and decision-focused BI.

Byline: The DATALIA team · Published on August 05, 2026

Quick answer

By combining automation of financial tasks, AI-assisted reporting and predictive models, you reduce rekeying, accelerate the close and have defendable figures for the executive committee in under 6 months.

Context

An SME of 120 employees, in the services sector, had scattered Excel files, three billing tools and a monthly close process that required two full-time people for eight days.

The CFO was looking for a way to make forecasts reliable, reduce time spent on reconciliations and obtain comparable indicators between subsidiaries.

Problem and objectives

Main problem: a time-consuming, unreliable management control process that is costly to produce.

Measurable objectives set with the CFO:

• Reduce accounting close time by 50% (working days).
• Halve the number of rekeying operations between systems.
• Obtain 30-day cash forecasts with an average error below 5%.
• Establish a full-cost model to evaluate integration bids and project amortization.

Solution implemented

What approach did we choose?

We adopted an incremental approach, prioritizing flows with high rekeying volume and low decision value. First automate customer reconciliations, then reporting consolidation, finally forecasting by model.

Step 1 — Mapping and prioritization

Objective: identify the 10 processes that cost the most hours. To collect: files, interviews with two controllers, lists of interfaces. Method: half-day workshop + extraction of a sample of 30 records. Output: prioritization grid.

Step 2 — Automating data entry and connecting systems

Action: automate the import of documents and the entry of recurring invoices via rules and OCR, and synchronize billing and accounting. Expected result: fewer rekeyings, exception rules routed to a single human controller.

Step 3 — AI-assisted reporting

Action: build a financial datamart and deploy aggregation and cleaning models. AI assists report generation: grouping suggestions, anomaly detection and proposed explanations. Humans validate the suggested corrections.

Step 4 — Predictive models for cash forecasting

Action: calibrate a 30-day cash forecasting model on 18 months of history, with exogenous variables (sales forecasts, supplier due dates). Modeling favors stability and interpretability over complexity.

Operational deliverables provided

Calculation model: Full cost of an automation project

Objective: estimate the real integration cost to compare quotes.

To collect: internal hours/day, average hourly rate, integrator cost, annual license cost, data remediation cost.

Method :

1. Calculate the estimated internal hours for scoping, testing, training.
2. Add integrator cost (days × rate) and licenses.
3. Plan data remediation in separate person-days.
4. Amortize the total over 36 months and compare to the annual productivity gain.

Output: total cost and annualized cost. Note: useful to compare three quotes. Does not work if the assumptions about hours are not validated by operations.

Quote comparison grid (excerpt)

Objective: make integrator offers comparable.

Columns: scope included, data remediation (days), expected performance, SLA, training (days), annual maintenance, possible hidden costs.

Method: weight each column (0–5) according to your priority and calculate a weighted score.

Output: score and scope alignment. Note: validate data remediation and testing duration before signing.

Results

Measured over 9 months after project start.

What immediate financial gains?

Reduced control hours ≈ direct savings on payroll. Fewer errors = fewer corrections and penalties. Better forecasting reduces the cost of short-term credit lines.

What didn't work

Common mistake: starting by automating processes that are too complex. We first tried to automate multi-currency credit note management. Result: low automation rate and rising costs.

Corrective applied: revert to standard flows (recurring invoices, customer reconciliations) and keep exception handling manual until stabilization.

Key lessons

• Prioritize volume and repetitiveness: automate simple, frequent tasks first.
• Measure time saved in person-days before choosing an integrator.
• Plan a testing buffer: 20–30% extra project time for data remediation and testing.
• Choose interpretable predictive models: the CFO must be able to explain variances in committee.
• Validate a full-cost model to make quotes comparable.

Common CFO mistake: « On a déjà payé un outil qui ne sert pas » — what to do?

The real question is scope and adoption. Before buying, you must quantify current usage cost (hours × rate) and compare it to the total project cost. If the tool exists but is not adopted, invest first in data remediation/training and run a pilot on 2 processes.

Role of DATALIA

We support CFOs in costing and comparing quotes, data remediation and iterative deployment of financial automations. DATALIA offers an audit of your flows, a comparison grid and the setup of management dashboards. To learn more, consult our product page or request an audit: DATALIA.

Limitations of this approach

Automation does not fix poor-quality data at the source. If your accounting rules contradict each other across subsidiaries, automation amplifies the error. Likewise, forecasting remains dependent on the quality of commercial inputs.

Scaling up: how to deploy across multiple entities?

1. Standardize chart of accounts and nomenclatures.
2. Deploy in waves: pilot → 2 subsidiaries → global.
3. Measure common KPIs and centralize exception governance.

Next operational step

Run a half-day scoping workshop to quantify avoided rekeyings. With 1 day of workshop you get a prioritization grid and a quantified order of priority.

Frequently asked questions

How much does an automation project typically cost for an SME?

The cost varies depending on data remediation and scope. Estimate: integrator cost (days × rate) + annual licenses + internal hours. Amortize over 36 months to compare to the gain in FTE. Use the model presented above.

Can an SME use AI for forecasting without extensive internal data science?

Yes. Favor simple, interpretable models (regression, time series) and an AI assistant for cleaning. The important parts are data governance and testing led by management control.

Key takeaways

• Automate repetitive, high-volume tasks first: that's where savings are quickest.
• Compare quotes with a full-cost model to avoid budget surprises.
• An interpretable predictive model is better than a more performant but opaque one for the CFO.

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