What are the main trends that will shape AI in 2026?
The artificial intelligence (AI) market in Europe, the Middle East and Africa (EMEA) is poised for another major milestone. After years of experimentation and pilot projects, companies are now faced with the challenge of transforming their initiatives into concrete and measurable value. Here are the key trends that will shape AI in 2026, from model optimization to the adoption of open standards, including the strategic role of open source and hybrid cloud platforms.
Industrialization of AI and pressure on profitability
In 2026, the artificial intelligence market is expected to take a decisive step in the EMEA region by moving beyond the experimentation stage and entering a phase of structured industrialization. A recent survey indicates that only 7% of companies today derive “customer value” from their investments in this technology. After years of pilot projects, companies are now under a certain pressure and must demonstrate the profitability of their initiatives in this area and control their increasingly significant financial weight. As a result, we are moving towards bringing model inference closer to the data itself, whether to manage costs or to meet growing expectations in terms of digital sovereignty.
Optimization of models and emergence of hybrid cloud platforms
Inference performance is now the biggest bottleneck. As businesses develop real-time use cases, efficiency becomes paramount. Smaller, highly optimized models are gaining ground in computationally constrained and low-latency scenarios, while larger models continue to support deeper reasoning. At the same time, many industry sectors are relying more than ever on established, predictive machine learning and data science approaches and combining them with newer generative AI capabilities. This combination accelerates the demand for an open hybrid cloud platform: a robust infrastructure capable of efficiently running both paradigms while integrating with existing systems, ensuring compliance with governance standards, and being future-ready.
Strategic importance of open source and digital sovereignty
In this context, the role of open source technology in AI is becoming essential in Europe. Unlike traditional software, openness can cover several dimensions of artificial intelligence: code, model weighting and, although much more rarely, training data. Each aspect ensures a specific level of transparency and directly influences companies’ ability to enable portability across environments, extend a model’s functionality, audit risks, and build trust. For European companies, the adoption of open practices consistent with the principles of sovereignty, interoperability and regulatory compliance such as the European AI law will represent a decisive strategic advantage.
Evolution towards agentic AI systems and mature platforms
At the same time, the underlying technology stack continues to rapidly evolve. Simple prompt engineering is giving way to sophisticated agentic AI systems that can manage multi-step workflows and operate autonomously in enterprise environments. Adopting these systems raises the bar on requirements not only for high-performance orchestration automation and inference, but also for cultural and operational transformation. To keep up, major accounts will have to move from access to basic models to mature platforms that rely on MLOps best practices by combining end-to-end observability, solid governance and continued skill development of their workforce.
Adoption of open standards and consolidation of AI in the technology stack
In 2026, the success of businesses will depend on their ability to treat AI workloads as integral parts of their broader technology stack. Utilizing open source community projects, modern AI environments will increasingly rely on standard foundations (e.g. vLLM inference servers), as well as emerging innovations capable of delivering efficiency at scale like llm-ds. Finally, open standards and collaborative ecosystems will make it easier for companies to move from experimentation to using production AI at scale.
