In 2026, the EMEA market is set to move decisively beyond AI experimentation and into a phase of structured industrialisation. Recent survey data shows only 7% of organisations are “driving customer value” from their AI investments today.
After years of pilots, businesses now face pressure to prove ROI and control the escalating financial burden of AI initiatives.
As a result, we’re seeing a shift toward bringing model inference closer to the data itself, both to manage costs and to meet growing expectations around digital sovereignty.
Inference performance is now emerging as the key bottleneck. As enterprises scale real-time use cases, efficiency becomes critical. Smaller, highly optimised models are gaining traction for compute-constrained and low-latency scenarios, while larger models continue to support deeper reasoning.
At the same time, many industries still rely on established predictive ML and data-science approaches, blending them with newer generative capabilities.
This combination is accelerating demand for an open hybrid cloud platform, a robust infrastructure that can efficiently operationalise both paradigms while integrating with existing systems, ensuring adherence to governance standards, and remaining future-proof.
In this context, the role of open source in AI is becoming pivotal in Europe. Unlike in traditional software, openness in AI can span several dimensions: the code, the model weights, and, though far less common, the training data.
Each aspect provides a different level of transparency and directly influences an organisation’s ability to enable portability across environments, extend a model’s capabilities, audit risks, and build trust.
For European enterprises, adopting open practices aligned with principles of sovereignty, interoperability, and regulatory compliance (including the EU AI Act) will represent a significant strategic advantage.
Meanwhile, the underlying technology stack is rapidly evolving. Simple prompt engineering is giving way to more advanced agentic AI systems capable of managing multi-step workflows and operating autonomously across enterprise environments.
Adopting these systems raises the bar not only for high-performance inference and orchestration automation, but also for cultural and operational transformation.
To keep pace, enterprises will have to progress from basic model access to mature platform capabilities grounded in MLOps best practices, with end-to-end observability, strong governance, and continuous workforce upskilling.
Success in 2026 will depend on treating AI workloads as fully integrated components of the broader enterprise stack.
Built on community-driven open source projects, modern AI environments will increasingly rely on industry-standard foundations like vLLM, alongside emerging innovations for large-scale efficiency such as llm-d.
Open standards and collaborative ecosystems will enable organisations to transition more smoothly from experimentation to production-ready AI at scale.
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