HPE shares its technology predictions for 2026, focused on how artificial intelligence structurally redefines the operation of data centers and enterprise networks. The forecasts describe a scenario in which AI stops being an added layer and becomes the organizing principle of the entire infrastructure.
In data centers, 2026 marks the transition to models defined as AI-native. Artificial intelligence will be integrated into all functions, from workload placement to cabling diagnostics. The data center itself will evolve into a closed-loop system, capable of anticipating failures, automatically adjusting performance and even negotiating energy consumption, also reducing manual intervention.
New micro-hyperscaler model
In parallel, the edge and artificial intelligence will converge in what is emerging as a new “micro-hyperscaler” model. Peripheral data centers will stop looking like telecommunications closets and adopt architectures typical of large hyperscale campuses, with 400G and 800G Ethernet connectivity, inference accelerators and autonomous operations. Cities, universities and organizations in the retail sector will be able to locally execute workloads that until now were reserved for central clouds, transforming the edge into a strategic asset with an impact on competitiveness, operational resilience and regulatory compliance.
The design of data centers will also change its starting point. In 2026, the architecture will start with the network and not with computing. As AI models scale to trillions of parameters, Ethernet will establish itself as the preferred interconnection basis over proprietary technologies. The focus will shift from AI clusters to the so-called AI fabrics: open, high-performance and adaptive network fabrics, optimized for training and executing artificial intelligence models.
In this context, Ethernet will evolve towards autonomous behavior. According to HPE, the switches will incorporate AI-based telemetry to continuously optimize congestion, microburst management, and energy efficiency. The promise of intent-based networking will be realized when network fabrics are able to learn, predict, and self-correct in real time, without the need for command-line interfaces or manual adjustments.
Zero Trust Data Center
Security will become an intrinsic part of the infrastructure. Instead of being added as an external layer, each packet, port and process will incorporate its own level of trust, validated by distributed artificial intelligence engines capable of detecting anomalies at maximum speed. Hardware-anchored identities, continuous microsegmentation, and east-west traffic encryption will make the Zero Trust data center the default operating model.
In the field of enterprise networks, wireless operation will also undergo a profound change. In 2026, AIOps will be a prerequisite to extract real value from capabilities such as multi-link operation, wider channels or deterministic latency. Continuous learning models will make it possible to anticipate network congestion, optimize radio behavior and reorganize spectrum use in real time, displacing traditional debates about SSID, band selection or manual adjustments. The performance of wired and wireless networks will converge not through increased speed, but through unified AI-based experience management.
AI agents will take over the networks
The maturity of agentive AI will drive the evolution towards truly proactive networks. LANs will not only self-heal, they will anticipate impacts and optimize the experience before the user notices any degradation. AI agents embedded in switches and access points will interpret behavioral patterns, validate faults and execute corrective actions automatically, including managing hardware replacements without human intervention. It is not about the network adapting to users in real time, but about predicting the impact minutes or hours in advance.
This approach will accelerate full-stack convergence. In 2026, organizations will abandon fragmented networking decisions to adopt a single operating framework that spans wired, wireless, WAN, and, progressively, compute and storage. Cloud-based orchestration and AI-native automation will drive demand for a single source of truth and a single layer of intelligence that manages performance, experience, security, and lifecycle from customer to cloud. The value will no longer lie in isolated products, but in the ability of the whole to operate as a coherent system under common AI governance.
The transformation will also affect talent. The change will not be in replacing engineers, but in elevating their function. Conversational co-pilots and agentive assistants will assume the first operational level, resolving queries, managing policies, detecting anomalies and initiating corrective actions. The most effective professionals will be those able to collaborate with AI, define intent, validate decisions, and orchestrate automation at scale. In this scenario, the network engineer evolves into a strategic role, while AI becomes the operational backbone.
“In 2026, data centers will move from being managed as static environments to behaving as living systems, capable of learning, anticipating and optimizing themselves,” said Praveen Jain, senior vice president and head of Data Centers at HPE Networking. “AI will become integrated into every operational decision, from performance to energy.”
The network engineer evolves into a strategic role, while AI becomes the operational backbone
From a network perspective, the change is equally profound. “The maturity of AIOps and agentive AI will allow networks to go from reacting to problems to anticipating them,” explains Sujai Hajela, executive vice president and head of Campus and Branches at HPE. “In 2026, the user experience will be managed by the network as a whole, not by isolated technologies.”
Looking ahead to 2026, HPE anticipates that the architectures that succeed will be those capable of operating as a single entity. Artificial intelligence will unify infrastructure, the cloud will enable its delivery, and organizations will choose providers based on their ability to make the entire stack work as a continuous, coherent experience.
