Digital transformation refers to the integration of digital technologies into all areas of a business, from internal processes to customer relations. This term encompasses both the automation of repetitive tasks and the complete overhaul of a data-driven business model. In 2024, this approach is complicated by a new regulatory dimension: the European AI Act, which came into effect this year, requires documentation and classification of every use of artificial intelligence deployed within the organization.
Far from being a simple change of tools, digital transformation involves governance, skills, and culture within a company. Understanding its technical components before embarking on the journey helps avoid the most costly mistakes.
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AI Act and Data Governance: The Regulatory Framework Redefining Digital Transformation
Most guides on digitalization overlook a crucial constraint: since 2024, any European company using an AI system must classify its use cases by risk level. A scoring tool for applications or fraud detection does not fall under the same regime as a customer service chatbot.
In practical terms, the AI Act will be fully applicable in 2026. Until then, companies must formalize an internal AI policy that specifies authorized tools, prohibited uses, and human validation rules. Specialized resources, such as those available at https://www.liaisonsnumeriques.fr/, help structure this regulatory monitoring applied to digital.
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This obligation changes the very nature of a transformation project. Before selecting an automation tool or a generative AI module, one must verify the supplier’s compliance, document data processing, and plan a validation circuit. Choosing a non-compliant tool exposes one to sanctions and costly setbacks.
For an SME, this means that a compliance audit must precede (or accompany) any technological deployment, rather than follow it. Integrating this requirement from the start avoids having to reconfigure systems already in production.
Management Skills: What Companies Lack to Drive Digitalization
The main barrier to digital transformation is neither budgetary nor technological. It is a lack of internal skills to drive change. Many companies purchase software licenses without having the profiles capable of configuring them, training teams, and measuring actual adoption.
Three types of skills are simultaneously lacking:
- Data mastery: knowing how to structure, clean, and leverage existing data before adding new tools. A poorly fed CRM produces no value, regardless of its sophistication.
- Hybrid project management: coordinating external providers, business teams, and a general management that does not always share the same timeline. This role goes beyond that of a classic IT project manager.
- Change management: translating a tool into daily practice, identifying resistances, adapting training. Without this skill, the adoption rate remains low, and the return on investment does not materialize.
Recruiting a versatile profile capable of covering these three dimensions is challenging. Training internally takes time. The intermediate solution, often more realistic for an SME, is to combine a motivated internal reference with occasional external support during critical phases.
Digital Transformation of SMEs: Start with a Narrow Scope
Failures in digitalization share a common trait: a scope that is too broad from the outset. Attempting to digitize accounting, customer relations, production, and internal communication simultaneously creates confusion and dilutes resources.
A more effective approach is to isolate a high-impact, low-technical-complexity process. For example, the dematerialization of purchase orders or the automation of reminders for unpaid invoices. This first project produces visible results quickly, which facilitates team buy-in for subsequent steps.
Criteria for Choosing the Right Starting Process
The ideal process for a first digital transformation project meets three conditions: it involves few stakeholders, it relies on data already available (even in paper or spreadsheet format), and its improvement yields measurable short-term gains (processing time, error rate, customer response time).
A successful pilot project in three months lends credibility to the entire digital strategy with management and employees. Conversely, an eighteen-month project without tangible results fuels skepticism.
Data Security and Compliance in a Digitization Project
Every new digital tool creates an additional entry point for cyber threats. The proliferation of cloud solutions, API integrations, and remote access expands the attack surface. For a company undergoing transformation, data security is not an optional module added at the end of the project.
Three measures must accompany any deployment:
- Map personal data flows and verify their compliance with GDPR before migrating to a new tool.
- Require each cloud provider to locate servers in the European Union and provide documented security certifications.
- Implement a role-based access policy: each employee accesses only the data necessary for their function, not the entire information system.
These precautions slightly slow down the initial deployment. However, they prevent incidents whose costs (financial and reputational) far exceed those of prevention.
Link Between AI Act and Data Protection
The AI Act and GDPR overlap without replacing each other. An AI system that processes personal data must comply with both frameworks simultaneously. Documenting the purpose of each AI processing becomes a dual obligation, both for data protection and for AI risk classification. Companies that address these two compliance requirements in parallel, rather than in separate silos, save time and ensure consistency.
Digital transformation in 2024 is no longer just about choosing digital tools. The European regulatory framework, the scarcity of management skills, and security requirements redefine priorities. Starting small, documenting each step, and integrating compliance from day one remains the most reliable path for an SME looking to digitize its processes without losing control of its trajectory.