Digital transformation refers to the integration of digital technologies across all activities of a company, from customer relations to production. Accelerating this process is not just about stacking tools: it requires rethinking data governance, adapting internal skills, and considering a rapidly evolving European regulatory framework.
Generative AI and digital transformation: a lever for rapid prototyping
Since the end of 2022, generative AI (ChatGPT, Copilot, and their industry equivalents) has changed the speed at which companies design and deploy new digital services. Where a prototyping cycle used to take several months, these tools allow for the production of functional mock-ups, technical documentation, or code in just a few days.
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The impact is tangible on three fronts: automated customer assistance, large-scale content writing, and software development. A small business that integrates an AI assistant into its technical support reduces its response times without needing to hire immediately. According to Microsoft’s 2024 Work Trend Index, this adoption significantly accelerates the deployment of digital services in European companies.
Specialized players like NetLab assist companies in this integration by structuring the technological building blocks necessary for coherent digitalization.
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A common pitfall is deploying generative AI without governance. A language model fed with unstructured customer data produces inconsistent results and exposes the company to compliance risks. Technology alone does not accelerate anything if the data foundation is fragile.
Data governance: the prerequisite that companies underestimate
Before automating anything, a company must know what data it has, where it is stored, and who can access it. This step, often relegated to the background, conditions the entire subsequent digital transformation.
The Data Governance Act, which came into effect in September 2023, now regulates the sharing and reuse of certain data at the European level. For SMEs as well as large groups, this means structuring data governance not only for efficiency reasons but also to remain compliant.
What data governance concretely involves
- Mapping internal data flows (CRM, ERP, business tools) to identify duplicates, silos, and blind spots regarding information quality
- Defining clear roles: who validates the data, who updates it, who authorizes its sharing with a partner or service provider
- Implementing regular cleaning processes, as a customer database that is 30% outdated skews any predictive analysis
Without structured data governance, digital innovation rests on unstable foundations. Advanced analytics or artificial intelligence solutions produce results proportional to the quality of the data provided to them.
AI Act and compliance: anticipating obligations from the design phase
The European AI Act, adopted in 2024, classifies artificial intelligence systems by risk level. Companies operating in finance, healthcare, or industry are the first to be subject to the strictest obligations, but any organization deploying AI in its processes must understand where it stands in this classification.
A common mistake is treating compliance as an administrative burden to be managed after deployment. In practice, integrating compliance from the design phase of a digital project avoids costly redesigns and regulatory bottlenecks downstream.
Three risk levels to know
The AI Act distinguishes between minimal risk systems (spam filters, content recommendations), high-risk systems (credit scoring, automated recruitment, assisted medical diagnosis), and prohibited practices (social scoring, behavioral manipulation). A company launching a customer scoring tool must document its algorithms, ensure decision traceability, and provide a mechanism for human recourse.
This regulation is not a barrier to digital transformation. It structures practices and provides a framework of trust for customers and partners. Companies that comply early gain a credibility advantage in their market.
Internal skills and adoption: the human factor of digitalization
Technologies only produce results if teams know how to use them. Too many digital transformation projects fail because the tool was chosen before assessing the skill level of end users.
Training does not mean organizing a two-hour session on new software. It involves identifying digital skill gaps on a position-by-position basis, then building progressive pathways tailored to the relevant professions. A salesperson does not have the same needs as a logistics manager when facing a data analysis tool.
- Designate digital referents in each department to relay best practices and report friction points
- Favor tools that integrate with existing solutions rather than multiplying new interfaces
- Measure actual adoption (frequency of use, error rate) and not just the number of activated licenses
Team adoption determines the return on investment of a digital project. An ERP deployed but bypassed by half of the users transforms nothing.
The digital transformation of companies will not slow down. European regulations like the Data Governance Act and the AI Act are reshaping the rules of the game, while generative AI compresses development timelines. Organizations that structure their data governance and invest in their teams’ skills before choosing their tools are the ones that will reap lasting benefits from this acceleration.