A company that digitizes its invoices has not completed its digital transformation. It has simply replaced paper with a PDF file. The real shift occurs when an algorithm reads these invoices, detects anomalies, follows up with suppliers, and adjusts the cash flow forecast without human intervention. This is precisely the type of leap that artificial intelligence enables in the digital transformation of businesses.
AI Act and deployment in companies: what the European regulation changes starting in 2026
Most articles on AI in business talk about automation and productivity gains. Few address the regulatory constraints that now frame these deployments. The European regulation AI Act provides for a gradual application starting in 2026, with strengthened requirements on high-risk AI systems.
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Are you using a scoring tool to sort applications or assess a client’s creditworthiness? This type of system falls into the high-risk category. The company must then document how the model works, track each use, and ensure systematic human oversight.
Article 50 of the regulation also imposes transparency obligations: inform the user that they are interacting with a chatbot, label AI-generated content (images, videos, texts). For marketing or customer relations teams, transparency regarding AI content becomes a legal obligation, not an editorial choice. Firms are already assisting companies in achieving compliance, such as Bewise, which works on the strategic and operational dimensions of these projects.
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Business process automation: beyond repetitive tasks
Automating data entry or sorting emails is the first step. The second, less visible, concerns the decision-making processes themselves. Take a purchasing department: a machine learning model can analyze the order history, cross-reference actual delivery times with contractual conditions, and suggest adjustments to restocking thresholds.
This is no longer mechanical automation. This is what we call cognitive automation: the system learns from past data to formulate recommendations suited to the context. The distinction matters because it determines the type of skills to mobilize internally.
What distinguishes a viable automation project from a gimmick
Have you ever seen an internal chatbot that no one uses? The problem rarely lies with the technology. It stems from the initial framing. Three conditions separate a viable project from an abandoned tool:
- The targeted process generates a sufficient volume of data to train or feed the model. Without structured data, AI runs idle.
- The relevant business team participated in defining the rules and thresholds. A model imposed by the IT department without consultation ends up in a corner.
- The expected gain is measurable on a specific indicator (processing time, error rate, response time), not on a vague promise of “productivity.”
An AI project without a predefined success indicator is a risky project. This discipline applies to both SMEs and mid-sized enterprises.
Training and skills development: the real bottleneck
The main obstacle to digital transformation through AI is not the technology budget. It is the ability of teams to use the deployed tools. A predictive analytics software is useless if the sales manager cannot interpret the results it produces.
Several public initiatives exist to support this skills development. Bpifrance offers accelerators dedicated to artificial intelligence, designed for SMEs and mid-sized enterprises that want to structure their approach without relying solely on external providers.
Which profiles to prioritize for training
Not everyone needs to understand how a neural network works. However, certain profiles must quickly upskill:
- Operational managers, who need to know how to formulate a business need that can be translated into an AI use case.
- Data teams (analysts, controllers), who feed and validate the models on a daily basis.
- Compliance officers, who must ensure that uses comply with the regulatory framework, particularly the AI Act.
- Teams in direct contact with the customer, who use conversational AI or personalization tools.
Training managers to formulate a precise AI need accelerates a project more than adding a data scientist to the team. The bottleneck lies upstream, in translating the business problem into a question that can be exploited by an algorithm.
Agentic AI: the next layer of digital transformation
In recent months, a concept has been gaining traction in companies’ roadmaps: agentic AI. The idea is simple to understand by contrast. A classic chatbot answers a question. An AI agent autonomously performs multiple actions to achieve a goal.
Concrete example: an AI agent in a customer service department can receive a complaint, consult the customer’s history in the CRM, check the order status in the ERP, draft a personalized response, and trigger a credit note, all without a human intervening at each step.
This type of architecture changes the very nature of the service provided. Agentic AI transforms fragmented task flows into continuous processes, where traditional automation was limited to isolated actions. Companies testing these approaches structure their data and tools around this orchestration logic.
Digital transformation driven by artificial intelligence is not just about plugging a tool into an existing process. It requires rethinking data flow, model governance, and team training. The companies that move the fastest are those that treat AI as an organizational issue, not as an isolated IT project.