HPE Discover 2026: With Data Gravity Reshaping the Enterprise, HPE and Lumen Are Building the Dynamic Architecture to Match

Data has mass and attracts applications, services, and processes toward where it resides, whether that’s on premises, at the edge, or in a hyperscaler. At the same time, CPU, GPU, power, and cooling capacity create their own pull, drawing workloads toward the environments best able to run them. Now with AI, organizations must balance two competing forces: data gravity pulling work toward where data resides, and CPU, GPU, power, and cooling capacity pulling work toward where it can run most efficiently.

With AI, real-time analytics, and exploding data volumes at the edge and across the enterprise, the approach to data has flipped.

Together, HPE and Lumen are addressing this challenge with an architecture that treats the enterprise not as a set of disconnected silos, but as a coordinated system that can sense conditions end-to-end and help operators adapt to the business needs of the moment.

The rise of the distributed agentic enterprise
Of course, distributed operations aren’t new. What’s new is the rate of change and the performance sensitivity of modern workloads, where AI pipelines and data-intensive applications can amplify every weakness in fragmented traditional infrastructure including limited visibility, static provisioning, and manual handoffs. Instead, AI-native operations demand real-time optimization, continuous orchestration, and autonomous remediation end-to-end across compute, storage, and networking.

To answer these new demands, HPE and Lumen are building a unified enterprise architecture where infrastructure can “think across” the distributed enterprise, rather than forcing teams to troubleshoot one layer at a time. Using Model Context Protocol (MCP)-based architecture, the solution is designed to provide a common layer that lets systems talk to each other, with agentic AI supplying the reasoning engine and programmable multi-cloud network fabric becoming the trusted path that connects where data resides with where compute capacity is available.

In other words, the solution isn’t simply about smarter switches or faster links, it’s an architecture built to provide coordinated intelligence across layers, with decisions that remain observable, explainable, and auditable. 

From static pipes to adaptive enterprise flow
With the new enterprise architecture, each layer plays a vital role.

GreenLake provides the foundation where data can live and be processed, without forcing a “move everything to cloud” forklift. This enables a hybrid cloud approach while supporting organizations that wish to keep substantial datasets on premises for governance, sovereignty, cost, or performance reasons.

Cloud Interlink provides the software-defined networking layer with integrated assurance, using telemetry to continuously validate that network and application behavior to help meet expectations against the outcomes, proactively perform root cause analysis, and enforce consistent and centralized application-aware policies across the environment. It serves as the control surface for intent-based connectivity between workloads and endpoints, which is critical when data and applications span multiple locations.

Lumen’s trusted network for AI: programmable fabric, Network-as-a-Service (NaaS) and Multi-Cloud Gateway are where distributed enterprise design becomes real. Lumen provides the high-capacity, low-latency network foundation that distributed AI depends on. On top of the backbone, Lumen’s NaaS capability gives customers a control plane that supplies the ability to adjust bandwidth dynamically to match workload needs, without waiting for manual provisioning cycles. Lumen Multi-Cloud Gateway helps extend the programmable fabric across enterprise environments, cloud destinations, and hyperscaler on-ramps, creating a private, scalable path for workloads and data moving across clouds, data centers, and hyperscalers. Through the Lumen NaaS API and port-level integration, connectivity can be surfaced inside the GreenLake operating experience, making it part of the workflow rather than a separate manual process.

For a real-world example of how bandwidth and latency can fundamentally change workflows, consider an illustration from the seismic services industry. A global energy provider modernized seismic data flow to reduce duplication and eliminate physical handoffs by transferring subsurface information as early as possible into the cloud for quality control, processing, and delivery.

Historically, the provider was constrained by insufficient satellite bandwidth, meaning data generated at sea had to remain on a vessel until it could be moved physically. With the arrival of Low Earth Orbit satellite technology, the energy provider successfully transmitted full-integrity 4D seismic data directly to the cloud. This improved transparency and reduced operational friction while reducing data delivery time from an average of nine days to one¹.

As this customer example demonstrates, removing bandwidth bottlenecks and manual handoffs doesn’t just accelerate transfers, it enables entirely new operating models. It supports shifting work earlier in the pipeline, moving steps closer to where compute is most effective, and eliminating duplicate datasets.

AI-native operations demand real-time optimization, continuous orchestration, and autonomous remediation end-to-end across compute, storage, and networking.

Successful enterprises treat data as a first-class citizen
Naturally, as infrastructure integrates AI to become more autonomous, both data and the associated governance policies become more important, not less. That’s why the MCP-based enterprise architecture solution enables its agentic systems operate within well-defined trust boundaries, policy constraints, security enforcement domains, compliance frameworks, audit requirements, and operational risk models. This approach creates safe autonomy, where the systems’ autonomous actions can stay transparent and auditable, which is especially essential for regulated industries.

Safe autonomy is consistent with trends in multiple data-intensive sectors. For example, a healthcare leader designed a platform to accelerate real-world AI innovation by combining scalable, standardized (and de-identified) data with integrated analytical tools and secure computing environments, improving accessibility, reproducibility, and speed of execution for research and model development. In short, successful AI at scale requires an integrated, well-governed ecosystem, not isolated tools. 

Dynamic distributed enterprise that keeps pace with data gravity
With organizations increasingly interested in gaining the infrastructure necessary for tackling the realities of AI and data gravity, the enterprise architecture solution is designed to address continuously evolving performance needs of data and workloads distributed across on-prem and cloud platforms. It combines:

• GreenLake including orchestration, observability, compute, storage, and private cloud
• HPE Cloud Interlink’s SDN-based network and application assurance and policy control and
• Lumen’s trusted network for AI, including high-capacity transport, NaaS Service programmability, NaaS API integration, and Multi-Cloud Gateway for private, scalable connectivity across clouds, data centers, and hyperscaler on-ramps

Get started moving from static connectivity and manual tuning to an adaptive model where bandwidth, placement, and performance are orchestrated as a system, today.

¹ https://www.tgs.com/technical-library/revolutionizing-seismic-data-transfer-from-sensor-to-client