Forrester Identifies AI as Key Driver Reshaping Hybrid Cloud Strategies in 2026

A recent report from Forrester, published on October 2, 2026, emphasizes that Artificial Intelligence (AI) is the primary force redefining hybrid cloud strategies for the coming year. The report, titled "Forrester says AI is reshaping cloud strategy for 2026," outlines ten key developments that technology leaders must consider as AI moves from experimental stages to broader enterprise deployment. This shift is transforming the market from commodity cloud services to an "AI-native cloud" paradigm. This development is crucial for practitioners because it signifies a move beyond simply distributing workloads across on-premises and public cloud environments. Instead, the focus is now on intelligently orchestrating, governing, and optimizing these environments specifically for AI at scale. The report highlights that enterprises are grappling with increased multi-cloud complexity, escalating AI-related cloud costs, and heightened data sovereignty concerns, all while striving to deploy business-facing agentic AI. This means that hybrid cloud architecture is no longer a compromise but a deliberate strategy to balance performance, cost, compliance, and resilience for AI workloads. The broader trend in cloud and DevOps has been a gradual evolution towards more sophisticated workload placement and management. Initially, hybrid cloud emerged as a way to integrate existing on-premises systems with public cloud resources, often driven by migration efforts or specific compliance needs. Over time, Kubernetes became a staple for managing containerized workloads across these diverse environments, enabling greater portability and scalability. Now, with the rapid acceleration of AI adoption, the conversation is shifting again. AI workloads, particularly training and inference, demand high-performance computing, low-latency interconnects, and predictable capacity, which public clouds alone may struggle to provide consistently or cost-effectively. This has led to a renewed emphasis on strategic workload placement, where organizations carefully decide where each component of their AI lifecycle (data processing, model training, inference) should reside to optimize for performance, cost, security, and compliance. In practice, this means that IT and DevOps teams must develop a more granular understanding of their AI workloads' requirements. They need to assess whether sensitive data should remain in private environments for compliance, while scalable tasks leverage public cloud resources. This necessitates robust hybrid cloud management platforms that offer end-to-end visibility, consistent policy enforcement, and automated resource allocation across all environments. Practitioners should focus on implementing "federated governance" – central policy with distributed execution – ensuring unified tagging, cost allocation, policy as code, and continuous compliance for their AI deployments. Furthermore, the report suggests that cloud providers are embedding AI tools directly into their operational, security, and governance offerings, pushing buyers to integrate AI strategy with their overall cloud strategy. This implies a need for practitioners to evaluate how well their chosen hybrid cloud platforms support AI-native operations, including the ability to manage AI agents, secure AI systems, and optimize costs associated with AI compute. The future of hybrid cloud is intrinsically linked to how effectively organizations can leverage it as an AI execution layer.