The solution lies not in more isolated tools, but in strategic consolidation on a unified platform. A hybrid cloud that bridges legacy and future, a partner ecosystem built on transparency, and a security strategy that anticipates threats are the pillars of this new cycle. To analyze these key elements, a group of Red Hat experts breaks down the trends that will define 2026 in AI, virtualization, hybrid cloud, security, and digital sovereignty.
AI
Author: Chris Wright, chief technology officer and senior vice president, Global Engineering, Red Hat
We are at the dawn of a new and chaotic technological era, where the rapid pace of generative AI innovation is transforming how every business operates. AI cannot be a solution in search of a problem; instead, AI adoption must be tied to real-world use cases. This means CIOs must ensure that these AI use cases move from proof of concept to production. Because AI is advancing so rapidly, businesses need the ability to quickly integrate new technologies into a production environment, where value can be realized immediately on a common, stable, and trusted platform. By 2026, this flexibility should be a central focus for CIOs: Open platforms that connect heterogeneous systems, workloads (from traditional applications to AI agents), and requirements will be crucial. It's about the ability to build for today's production demands while being ready for tomorrow's AI workloads.
Author: Robbie Jerrom, Senior Principal Technologist AI, AI BU, Red Hat.
AI-generated content can be inaccurate. As we enter 2026, businesses are moving from AI experimentation to demanding value at production scale. Recent data from a Red Hat survey shows that 76% of organizations are still in the exploration phase of AI use cases, but will invest an average of 32% more in AI next year. Most generative AI pilot projects have not delivered measurable returns despite significant investment, creating pressure to demonstrate ROI through operational deployment.
The key shift we're seeing is toward autonomous, agentic AI systems that can plan and execute multi-step workflows with enterprise applications. The increasing adoption of task-specific agent frameworks, such as MCP, is greatly accelerating this trend. But here's the challenge: early data from Gartner suggests that many of these agentic projects will fail due to inadequate governance and unclear business value. Success will require treating AI agents as digital partners with well-defined scope and accountability, rather than as magic bullets for ill-defined problems.
The economics of AI are forcing a fundamental reevaluation of efficient inference and data gravity. Running every prompt through premium models is like chartering private jets for local trips—technically possible, but economically unsustainable. We are seeing innovative organizations implement multi-model strategies, directing simple tasks to efficient, lower-parameter models, while reserving expensive frontier models for complex reasoning.
But a significant architectural shift is underway. Instead of moving enterprise data to centralized GPU clusters, we're bringing AI inference closer to where the data resides. RAG pipelines are processing at the data source, inference is being done at the edge for manufacturing plants, and distributed models are being deployed in regional data centers. This approach avoids costly data movement while addressing sovereignty and latency requirements. Combining intelligent model routing with distributed processing can dramatically reduce both inference costs and data transfer overhead, while also improving response quality.
The platform and operational challenges of 2026 will focus on orchestration and observability. Agent AI requires sophisticated MLOps capabilities, monitoring agent interactions in distributed systems, managing persistent context and memory across sessions, and implementing governance control mechanisms that can intervene when autonomous systems exceed their limits. We are seeing enterprises demand platforms capable of handling the entire lifecycle, from model versioning and A/B testing to compliance tracking and cost attribution. Successful organizations will build on unified platforms that manage AI workloads at the same level as traditional applications, enabling seamless integration with existing enterprise systems while maintaining the flexibility to adopt emerging models and frameworks.
The market rewards pragmatism over promises: lean model portfolios optimized for specific tasks, infrastructure decisions based on where data resides rather than vendor preferences, and measurement frameworks that connect AI outcomes to business objectives. The question is not whether AI transforms businesses—early evidence shows it can—but whether businesses can build the operational maturity needed to capture value at scale.
Author: Pauline Truong - AI Specialist Solution Architect, Red Hat.
By 2026, the EMEA market will decisively move from AI experimentation to a phase of structured industrialization. Data from Red Hat’s latest study shows that only 7% of organizations are “generating customer value” from their AI investments. After years of pilot projects, companies are now facing pressure to demonstrate ROI and manage the growing financial burden of AI initiatives. As a result, we are seeing a shift toward model-to-data inference, both to manage costs and to meet the rising expectations surrounding digital sovereignty.
Inference performance is now emerging as the primary hurdle. As businesses scale real-time use cases, efficiency becomes critical. Smaller, highly optimized models are gaining traction for scenarios with limited computing resources and low latency, while larger models continue to support more comprehensive reasoning. At the same time, many industries still rely on established approaches to predictive ML and data science, combining them with newer generative capabilities. This combination is accelerating the demand for an open hybrid cloud platform—a robust infrastructure that can efficiently operationalize both paradigms while integrating with existing systems, ensuring compliance with governance standards, and remaining future-proof.
In this context, the role of open source in AI is becoming fundamental in Europe. Unlike traditional software, openness in AI can encompass several dimensions: the code, model weights, and, though much less common, training data. Each aspect provides a different level of transparency and directly influences an organization's ability to enable portability across environments, extend a model's capabilities, audit risks, and build trust. For European companies, adopting open practices aligned with the principles of sovereignty, interoperability, and regulatory compliance (including the EU AI Act) will represent a significant strategic advantage.
Meanwhile, the underlying technology stack is evolving rapidly. Simple prompt engineering is giving way to more advanced agentic AI systems capable of managing multi-step workflows and operating autonomously in 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 need to progress from basic model access to mature platform capabilities based on MLOps best practices, with end-to-end observability, robust governance, and ongoing workforce training.
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 scalability such as llm-d. Open standards and collaborative ecosystems will enable organizations to more seamlessly transition from experimentation to production-ready AI at scale.
Author: Brian Stevens, senior vice president and chief technology officer for AI, Red Hat.
Over the past three years, the industry has witnessed massive investments in generative LLM training at state-of-the-art labs around the world. The result has been a wide array of powerful reasoning models, now available both as open source and proprietary. Last year, we saw the emergence of agents, powered by these advanced reasoning models and integrated into a broad ecosystem of tools, data, and systems. What this means for 2026 is that the emphasis is now shifting to inference platforms, which means production platforms for running these agents in a scalable, efficient, reliable, and secure manner. Just as we did two decades ago with Red Hat Enterprise Linux, through Red Hat AI, we are providing the unified inference platform that not only delivers production at scale but also enables any model, any accelerator, any cloud.
Virtualization
Author: Ed Hoppitt, EMEA Director - Business Value Practice, Red Hat
Migration Pressure from Traditional Virtualization Platforms:
In 2025, the fragility of many virtualization strategies was exposed, revealing how organizations are tied to something that no longer works for them. The progressive accumulation of technologies over the years has left executives facing systems that are now expensive to manage, complex to replace, and increasingly misaligned with the speed of transformation demanded by AI and digital modernization, due to their origins and nature being rooted in the past. Looking ahead to 2026, the pressure to decouple critical workloads from legacy hypervisors will intensify, driven by escalating renewal costs, concerns about the risk of concentration, and a greater focus on operational resilience. The opportunity is no longer just to modernize virtual machines for efficiency, but to treat virtual machine migration as a strategic mechanism to reduce technical debt, regain architectural control, and create a platform capable of supporting both current and future workloads. Those who wait for renovations to force change will discover that the operating model, not the technology, is the main obstacle.
Coexistence of AI Workloads and Traditional Virtual Machines:
In 2025, most enterprises treated virtualization and AI as separate domains, both operationally and architecturally. Entering 2026, that separation becomes untenable. Organizations seek to run mission-critical and data-intensive AI inference workloads side-by-side without duplicating infrastructure or creating parallel operational structures. This requires an approach to virtualization that recognizes virtual machines both as a consolidation target and as part of a broader AI execution layer, demanding that platform teams establish unified lifecycle management, observability, and governance for both types of applications. The shift here is not technical but cultural. Enterprises will need to integrate AI operational disciplines directly into existing workload platforms, rather than building new silos to accommodate them.
Platform Consolidation and the Drive to Reduce Technical Debt:
The trend we saw throughout 2025, of platforms multiplying faster than teams can absorb, risks reaching an unsustainable limit in 2026. Thorough budget analysis, expectations of sovereignty, and a shortage of skilled engineers are converging to establish a clear guideline: either streamline existing infrastructure, or the enterprise will face systemic fragility. Virtualization and application modernization will increasingly be seen as tools for unification rather than simply migration. Organizations are actively seeking to consolidate runtime environments, reduce responsibility transfers, and align operating models across legacy and cloud-native applications. Those that succeed will treat platform design as an organizational transformation rather than a simple infrastructure upgrade, investing in skills, platform engineering, and governance as much as in technology. Failure to do so risks increasing complexity precisely at the point where the cost of operating it becomes unsustainable.
Skills, Operating Models, and Modernization for Resilience:
By 2026, successful organizations will be those that recognize that modernization is as much about people, accountability, and decision-making rights as it is about code and computing. Virtualization programs began by focusing on capital expenditure savings through server consolidation exercises and have now shifted to operating-expense-driven programs, much more focused on delivering operational resilience and reliable platforms. This shift requires teams to operate more autonomously, closer to the workloads they support, with lifecycle ownership extending well beyond initial deployment. Organizations that create the right governance structures, empower teams to handle integrated virtualization and AI workloads, and embed exit planning into platform strategy will not only withstand cost and resilience pressures but also use them to regain strategic agility.
What Should I Run Where, How, and Why?
Throughout 2025, the most common question platform teams faced was deceptively simple: “What should I run where, how, and why?” In reality, it’s becoming the defining strategic decision for 2026. As workloads scale, resilience expectations harden, and costs rise, organizations are no longer treating infrastructure choices as tactical deployment planning; they’re aligning workload placement with business intent, risk tolerance, and data gravity. A shift from “cloud-first” or “on-premises by default” strategies to situational deployment models that weigh latency, sovereignty, exit flexibility, and operational maturity for each workload is expected. The "how" is becoming as important as the "where": organizations will increasingly standardize orchestration and lifecycle management across all environments to avoid operational silos or stranded workloads. And, crucially, the "why" will focus on value realization and resilience: advisors are already questioning whether workloads justify the cost of premium infrastructure, whether they require GPU adjacency or simply predictable availability, and whether they strengthen or erode long-term operational autonomy. Those who integrate this decision-making into platform strategy, rather than project planning, will move faster and avoid architectural debt that could otherwise take years to resolve.
Hybrid Cloud
Author: Michael Ferris, senior vice president, chief operating officer and chief strategy officer, Red Hat
We are reaching a tipping point where IT modernization is no longer just about efficiency. It is rapidly becoming a matter of survival. Businesses are caught between a rock and a hard place with the volatility of the virtualization market and the absolute imperative to adopt AI. These two pressures are exposing technical debt like never before, transforming it from a bothersome operating cost into an insurmountable barrier that can stifle innovation. All this while business demands continue to accelerate.
To succeed, businesses will need technology that bridges the gap between the trusted stability of existing systems and the flexible, intelligent systems—think AI agents, for example—where we know future innovation will occur. Platforms that leverage existing investments in people and processes and can adapt to future capabilities will drive industries forward. Delaying this work has always carried risks, but the stakes are even higher in 2026. It is now or never to lay the technological foundations for the future.
Digital Sovereignty
Author: Fevzi Konduk, EMEA Director Software & ISV Ecosystem, Red Hat
Digital sovereignty will continue to shape the European software market as a result of the pursuit of greater digital autonomy and operational resilience. This represents a fundamental shift in the market, going beyond regulatory compliance, as seen in previous years. This change is being driven by significant macroeconomic factors and regulatory requirements.
New rules and regulations, such as the Digital Operational Resilience Act (DORA), the Network and Information Systems Security Directive 2 (NIS2), and the Data Protection Act, are redefining market expectations for data security and management. Customers are demanding greater control over their data and a clearer understanding of their digital supply chains. Therefore, there is a growing focus on reducing technological dependencies and mitigating operational risks associated with third-party providers.
The key question is no longer just where the data is stored, but who ultimately has control and access to it.
This changes the competitive landscape for software vendors. Building verifiable trust is now a key business objective, leading to a "sovereignty by design" approach. This has become a competitive business advantage.
However, the most significant evolution is at the organizational level. Success demands a cultural shift from a traditional legal compliance approach to a product capability mindset. This requires that sovereignty be an inherent feature of the product, part of the software architecture, and not something that operates in parallel or is added as an afterthought.
By 2026, the conversation will shift from regulatory awareness to proven execution. Market leaders will be those who have made this organizational leap, leveraging enterprise open source not just as a technology choice, but as the strategic foundation for delivering the verifiable autonomy, control, and resilience our customers now demand.
Security
Author: Chris Jenkins, senior principal chief architect, Red Hat
The IT cybersecurity and digital sovereignty landscape in 2026 will focus on AI-driven threats, both real and potential, and the need for demonstrable, regional control over data. Challenges for customers will shift from simple regulatory compliance to managing AI governance risk, as the first major security incidents attributable to autonomous and agentic AI systems are predicted to occur. This will force companies to make a practical shift in focus, moving away from enthusiastic AI experimentation (as seen in previous years) toward establishing an 'infrastructure of trust' and tangible evidence of responsible AI use.
Market pressures will also intensify due to stricter and more targeted global regulations, such as evolving EU laws (NIS2, DORA, AI Act, Cyber Resilience Act, etc.) that prioritize digital and operational resilience. Business objectives will focus on transforming this regulatory obligation into a competitive advantage by adopting an 'AI on AI' defense strategy: using proactive cybersecurity approaches and AI-based security platforms to combat hyper-automated attacks. For people and processes, this demands a significant cultural shift from reactive security to 'Security by Design' and 'Security by Default' strategies, integrating sovereignty, including data localization, directly into architectural decisions. The concept of digital provenance and Confidential Computing will become critical technology approaches, enabling organizations to verify the origin and integrity of data and maintain control even in multi-cloud environments, thereby mitigating geopolitical risk and strengthening their digital autonomy.
