Digitalization in companies is no longer just about deploying an ERP or migrating servers to the cloud. The topic has shifted: business leaders are taking control of tool creation, generative AI is redefining roadmaps, and the shortage of digital skills remains the main bottleneck. Understanding these issues requires examining these three areas in depth rather than listing generic benefits.
No-code and low-code platforms: business-led transformation
The IT department is no longer the sole actor in digitalization. For the past two years, no-code and low-code platforms have redistributed the ability to produce internal applications. A logistics manager creates their order tracking workflow, a financial controller automates their reporting, an HR professional builds an onboarding form, all without writing a line of code.
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This transfer of skills changes the organizational dynamic. Business units become co-architects of their own digital transformation, which reduces the IT backlog and accelerates deployment cycles. SMEs and mid-sized companies find this particularly suitable: limited budgets, small IT teams, and an immediate need for agility. To better understand the challenges of digitalization in companies, it is essential to observe how these tools redistribute roles between the IT department and business units.
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The main risk is the proliferation of uncontrolled applications. Without a validation framework, each department produces its own tools, with siloed data and heterogeneous security levels. The solution lies in a hybrid governance policy:
- A catalog of platforms validated by the IT department, with predefined connectors to the central information system
- Digital referents in each business unit, trained in best practices for data management
- A quarterly review process of created applications to identify duplicates and compliance gaps
Without this framework, no-code generates an invisible technical debt that ultimately slows down the transformation it was supposed to accelerate.
Generative AI and digital strategy: a pillar, not a gadget
Generative AI has ceased to be an experimental tool. ChatGPT, Copilot, Gemini, and their business variations are now integrated into transformation roadmaps as a structural pillar alongside cloud and data.
Concrete use cases are multiplying: assistance in drafting contractual documents, first-level customer support, prototype generation in R&D, creation of personalized training content. For each business process, the question is no longer “can we use AI?” but “what level of delegation should we grant to AI and with what safeguards?”.
Data governance and compliance
The integration of generative AI raises an issue that many companies underestimate: the governance of data injected into models. A salesperson who pastes a client file into an external AI assistant potentially exposes personal data outside the company’s GDPR perimeter.
We recommend mapping data flows before deploying any generative AI tool. This involves distinguishing between data that can be used internally (anonymized reports, technical documentation) and data that requires a siloed environment (client data, financial data, HR data).
Companies that move fastest in this area are those that have appointed an AI manager reporting to senior management, rather than just the IT department. Generative AI impacts the value creation strategy, not just the technological infrastructure.
Shortage of digital skills: the structural brake on digitalization
All the tools in the world are useless if no one knows how to use them properly. A study by ITCEQ published in 2026 on the technological absorption capacity of companies confirms what we observe on the ground: about two-thirds of the companies surveyed cite the shortage of digital skills as the main barrier to their transformation.
The problem is not limited to recruiting developers or data scientists. It concerns all employees: managers unable to read an analytical dashboard, salespeople who do not leverage their CRM, operators who bypass digital tools out of habit.
Continuous training and skills development
The digitalization of training itself is part of the answer. Micro-learning platforms, adaptive learning paths, and job simulators allow for continuous training without immobilizing teams for entire days.
- Identify skill gaps by department through a structured digital diagnosis (not just a simple satisfaction questionnaire)
- Prioritize training on tools already deployed rather than on hypothetical future technologies
- Measure the actual adoption of digital tools (connection rates, usage frequency, quality of entered data) rather than just participation in training sessions
- Integrate digital referents into teams to provide daily support, beyond formal sessions
Digital transformation is also a managerial challenge. A manager who does not use digital tools themselves sends a clear signal to their team: these tools are optional. Senior management has a direct responsibility for leading by example.
Digitalization of processes and customer data: beyond dematerialization
Digitizing a paper form is not digitalizing a process. True transformation occurs when the collected data feeds into an automated or semi-automated decision chain. A digitized purchase order that ends up in a shared folder without automatic processing does not change productivity.
The challenge focuses on interoperability between tools. A fragmented information system produces unusable data, regardless of the degree of digitization of each isolated component. Companies that derive measurable benefits from their digitalization are those that have invested in connectors between their management tools, customer platforms, and internal databases.
Customer data management illustrates this requirement well. A CRM manually populated by rushed salespeople generates incomplete records and duplicates. In contrast, a CRM connected to billing tools, customer support, and marketing campaigns produces a unified view that allows for personalized customer experiences and anticipates needs.
Digitalization in companies cannot be managed from a strategic committee disconnected from the field, nor from operational teams left to their own devices. Organizations that succeed in their transformation are those that articulate technical governance, skills development, and managerial adoption around specific business objectives, not around a catalog of tools.