Business 6 min read -3 May 26-Updated 7 August 26 -Clément Gamberini
How to adopt AI in business?
AI in business involves integrating artificial intelligence models into specific business processes to save time, ensure task reliability, or inform decisions. The right starting point is to identify a high-volume, time-consuming process where the gain is measurable. In 2024, 10% of French businesses with 10 employees or more reported using AI, compared to 6% a year earlier (INSEE). In this article, we will see how to adopt AI and create value for your business.
In brief
AI in business creates value when applied to a specific, high-volume process with a measurable ROI.
The most profitable uses today: customer service, document processing, compliance, decision support.
The right method starts with the business problem, validates the value on a prototype, then industrialises with human supervision.
At Odealim Group, an AI solution boosted productivity by +150% in processing customer requests.
Adoption is progressing rapidly: the share of French micro-businesses and SMEs using AI doubled in one year, to 26%.
Adopting AI in business, for what uses?
Behind the term, there are very different uses that we can categorise into four families.
Theprocess automation handles high-volume repetitive tasks: classifying requests, extracting data from a document, checking for compliance. The document exploitation allows querying an internal corpus in natural language (contracts, procedures, files) and extracting the right information in a few seconds. The decision support relies on business data to forecast, prioritise, or recommend. The customer relationship finally covers assistants who propose answers that teams validate.
The common thread among these uses: they tackle concrete and measurable work.
Where French businesses stand
Adoption remains varied. According to the 'INSEE, 9% of businesses with fewer than 50 employees use AI, compared to 33% of those with 250 employees or more. The information and communication sector leads (42%), far ahead of construction (3%). France (10%) remains below the European average (13%).
The dynamic, however, is clear. The proportion of French micro-businesses and SMEs reporting the use of AI doubled in one year to reach 26% (Baromètre France Num 2025). The gap is therefore widening between businesses that are structuring their first use cases and those that are waiting. Rather than aiming for a large programme, it is often better to start with a use case with demonstrable value.
Where AI creates value, by function
Not all processes lend themselves to AI. Those that do share three traits: a high volume, too many rules for classic automation, and a result whose quality is measurable. Here are the functions where the return on investment is most tangible today.
Function
Typical use case
Observed Impact
Customer service
Processing of email requests, proposed responses then validated by the teams
A successful IA project follows a simple thread: we frame by value, we prove on real data, we industrialise, then we measure. This discipline avoids the most common pitfall, the prototype that impresses in demonstration but doesn't hold up in production.
Concretely, framing selects the use case according to its business impact and defines success indicators before the first line of code. The prototype validates value on real volumes. Industrialisation connects the solution to existing tools (ERP, files, API) and provides an interface where teams validate. Measurement follows the indicators set at the outset, then feeds subsequent iterations.
Governance and success factors
Three factors separate a lasting deployment from a stalled project. Firstly, data : an AI is only reliable if it relies on accessible, up-to-date, and well-structured data. Secondly, human validation : on sensitive topics, AI suggests and teams decide, via a control interface. Finally,adoption : a tool that no one uses creates no value, hence the importance of change management and upskilling.
AI reaches its limits when the target process is ill-defined, when data is unavailable, or when the need is too variable to justify the investment. In these cases, classic automation or a simple tool overhaul often provides a better service. Recognising these situations is part of a serious deployment.
This upskilling is also where many deployments stall: teams receive a tool without ever learning to use it beyond the basic prompt. A generative AI training lasting a few days is often enough to unblock the situation, provided it starts from the teams' real use cases. Jedha teaches this format in 42 hours, with no technical prerequisites, to active executives who leave with their own configured assistants. One module is dedicated to securing usage, GDPR included.
Client case study: the Odealim Group
The Odealim Group, an insurance brokerage player (€165 million in turnover, 900 employees), receives tens of thousands of customer requests by email each month. Processing them at scale without degrading service quality was becoming difficult.
Galadrim designed a bespoke AI solution that classifies requests, searches for useful information in files and contracts, and then proposes a response. Each proposal is reviewed and validated by the business teams via a dedicated interface. The decision remains human; productivity increases significantly.
300
internal users
50 000+
responses per month
+150 %
productivity
4 key takeaways
AI is a means, the starting point remains a measurable business problem, not technology.
The most profitable uses target a precise, high-volume process where quality is measured.
Method is paramount : frame by ROI, prove with real data, industrialise, measure.
Human validation and data quality determine long-term reliability.
Conclusion
AI in business is neither a magic formula nor a gimmick. It's a lever that creates value when it's applied to the right process, with a clear method and predefined indicators. Starting small, proving value, then expanding remains the safest path.
Wondering which process is best suited to AI in your organisation? Let's talk about it.
FAQ
Where to start with AI in business?
With a time-consuming, high-volume process with a measurable outcome, rather than a large programme. We frame a first use case according to its ROI, prototype it with real data, then industrialise it if it delivers on its promises. This initial success funds and legitimises subsequent ones.
How much does an AI project cost in a company?
The budget depends on the scope, the quality of available data, and the level of integration into the information system. A first framed use case costs significantly less than a global programme. The important thing is to define the ROI indicator before starting, to compare the investment with the expected gain.
Our data is sensitive. Is AI compatible with our security requirements?
Yes, provided hosting and access are framed from the outset. Depending on the needs, data can remain in Europe, be anonymised, or run on a dedicated infrastructure. Human validation and response traceability complete this setup.
What ROI to expect and in what timeframe?
The return depends on the use case, but it is measured concretely: time saved, volume processed, productivity. At Odealim, the solution achieved +150% productivity gain on processing customer requests. The first gains often appear within the first weeks of going live.
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