AI Adoption in the EU: The Gap Between the Real 20% and Gartner's Projections

The Funcas report on the adoption of artificial intelligence technologies in the European Union reveals sustained growth but far from saturation. According to the data collected, the proportion of companies using AI has risen from 7.6% to 20% in four years. This figure, while positive, contrasts with the narrative of massive adoption that often dominates technology headlines, showing a more nuanced reality in the European business fabric.

The adoption curve follows classic patterns of technological diffusion, where most companies are still in the experimentation or pilot implementation phase. The gap between adoption and intensive use is particularly notable: many organizations that declare they use AI do so superficially, without integrating the technology into their core business processes. This discrepancy suggests that the real value generated by AI has not yet been fully materialized in the operational productivity of most firms.

Furthermore, sectoral heterogeneity plays a crucial role in these figures. While industries such as technology or financial services advance with greater speed, traditional sectors like manufacturing or agriculture show slower adoption due to the complexity of their processes and the need for higher initial investment. This diversity explains why the 20% average may hide very different internal dynamics within each economic sector.

Gartner, for its part, presents a more optimistic outlook in its strategic trends for 2026. The report highlights the importance of AI-native development platforms and supercomputing as fundamental pillars for digital transformation. According to Gartner, companies that integrate agents, semantics, and convergent platforms will outperform their competitors, leveraging the capacity of these tools to automate complex workflows and generate real-time insights.

The analytics firm predicts that by 2030, at least one in ten companies will be AI-first, having outperformed their rivals through the adoption of these technologies. This projection suggests a significant acceleration in the coming years, although it does not specify the exact intermediate adoption rate between 2026 and 2030. The concept of AI-first implies that artificial intelligence ceases to be an auxiliary tool to become the core of business strategy and decision-making.

This optimistic scenario is based on the maturation of support infrastructures and the reduction of inference costs. Gartner argues that the convergence of data, analytics, and AI will allow organizations to scale their solutions more efficiently. However, this vision requires companies to overcome the cultural and technical barriers that currently hinder widespread implementation, something that is not guaranteed for all organizations.

The discrepancy between Funcas data and Gartner's projections is explained by several structural factors. First, the European regulatory framework imposes transparency, security, and privacy requirements that slow down the implementation of AI solutions. The EU AI Act, although still in the implementation phase, is already influencing companies' investment decisions, which prioritize legal compliance over deployment speed.

Second, the shortage of specialized talent in AI and data limits companies' ability to develop and implement their own solutions. Many organizations rely on external providers, which increases costs and reduces control over the technology. This external dependence can create bottlenecks in internal innovation and hinder the rapid adaptation of AI models to the specific needs of each business.

Finally, Europe's computing and data storage infrastructure remains behind that of the United States and Asia, affecting the scalability of AI solutions. The availability of state-of-the-art data centers and cloud processing capacity are determinants for AI model performance. This infrastructure disadvantage can translate into higher latencies and operational costs, discouraging large-scale adoption by European companies.

One of the most relevant signals of change in 2026 is the European Commission's decision to designate Amazon Web Services (AWS) and Microsoft Azure as gatekeepers under the Digital Markets Act (DMA). This measure, announced in June 2026, implies that these platforms must comply with obligations of interoperability, data portability, and non-discrimination. This designation marks a milestone in the regulation of cloud infrastructure in Europe.

The designation of AWS and Azure as gatekeepers, despite not meeting the usual quantitative thresholds, reflects the growing weight of cloud infrastructure in the European AI ecosystem. Forced interoperability could facilitate the migration of workloads between providers, reducing switching costs and fostering competition. This could accelerate AI adoption by making cloud solutions more accessible to European companies, eliminating technical barriers that previously limited user flexibility.

In addition, the data portability obligation allows companies to move their digital assets without artificial contractual or technical frictions. This empowers end users, who can negotiate better terms with their cloud providers. In the context of AI, where data is the main input, this mobility capability is crucial to ensure that organizations do not get trapped in closed ecosystems and can leverage the best solutions available in the market.

For companies seeking to integrate AI sustainably, it is crucial to distinguish between initial experimentation and large-scale implementation. AI governance must be a central component of the strategy, not a later addition. This includes establishing frameworks of ethics, audit, and accountability that guarantee the responsible use of technology, ensuring that AI models are transparent, fair, and aligned with the organization's values.

Furthermore, organizations must prioritize staff training and the building of a data culture. AI adoption is not just a technological project, but an organizational transformation that requires changes in processes, roles, and mindsets. Collaboration with specialized providers can accelerate this process, but it must be accompanied by a clear strategy of technological independence and data control, avoiding excessive dependence on third-party solutions.

In summary, closing the gap between current adoption and future projections requires a comprehensive approach that combines technological innovation with organizational adaptation and regulatory compliance. Companies that manage to balance these elements will be better positioned to leverage the opportunities offered by artificial intelligence in the European market, transforming the current gap into a sustainable competitive advantage.