In 2025, the fragility of many virtualization strategies was exposed and how organizations find themselves tied to something that now does not work for them. The progressive accumulation of technologies over the years has caused executives to face systems that, today, are expensive to manage, complex to replace and are increasingly misaligned with the speed of transformation demanded by AI and digital modernization, due to their origin and nature being anchored in the past.
Looking ahead to 2026, Pressure to decouple critical workloads from legacy hypervisors will intensifydriven by escalating renewal costs, concerns about concentration risk and an increased 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 find that the operating model, not technology, is the main obstacle.
Coexistence of AI workloads and traditional virtual machines
In 2025, most companies treated virtualization and AI as separate domains, operationally and architecturally. As we enter 2026, that separation becomes unsustainable. Organizations are looking 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 execution layer for AI, requiring platform teams to establish unified governance, observability, and lifecycle management for both types of applications. The change 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.
Consolidation of platforms and 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. Exhaustive budget analysis, sovereignty expectations, and a shortage of qualified engineers are factors that come together to establish a clear guideline, or what exists is rationalized, or the company will face systemic fragility. Virtualization and application modernization will increasingly be seen as tools for unifying rather than just migrating.
Organizations are actively looking to consolidate runtime environments, reduce transfers of responsibility, 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 technology. Failure to do so risks increasing complexity at exactly the point where the cost of operating it becomes unsustainable.
Skills, operating models and modernization for resilience
In 2026, successful organizations will be those that recognize that modernization is as much about people, accountability, and decision rights as it is about code and computing. Virtualization programs began with a focus on CAPEX savings through server consolidation exercises, and have now transitioned to OPEX-driven programs, much more focused on delivering operational resiliency and reliable platforms.
That shift requires teams to operate more autonomously, closer to the workloads they support, with a lifecycle ownership that extends well beyond initial deployment. Organizations that create the right governance structures, empower teams to handle integrated virtualization and AI workloads, and integrate exit planning into platform strategy will not only withstand cost and resilience pressures, but use them to regain strategic agility.
What should be executed 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 fact, it is becoming the defining strategic decision for 2026. As workloads scale, resiliency expectations harden, and costs rise, organizations no longer treat infrastructure choices as tactical deployment planning; They are aligning workload placement with business intent, risk tolerance, and data gravity.
Expect a shift from “cloud first” or “on-prem by default” strategies toward situational deployment models that weigh latency, sovereignty, output flexibility, and operational maturity for each workload. The “how” becomes as important as the “where”: organizations will increasingly standardize orchestration and lifecycle management across environments to avoid operational silos or “stranded” workloads.
And, fundamentally, 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.
Por Ed Hoppitt, EMEA Director Business Value Practice, Red Hat
