The rapidly evolving IT landscape, driven by artificial intelligence (AI), edge computing, and rising security threats, presents unprecedented challenges in managing compute infrastructure. Traditional management tools struggle to provide the necessary scalability, visibility, and automation to keep up with business demand, leading to inefficiencies and increased business risk. Yet organizations need their IT departments to be strategic business partners that enable innovation and drive growth. To realize that goal, IT leaders should rethink the status quo and free up their teams’ time by adopting a unified approach to managing infrastructure that supports both traditional and AI workloads. It’s a strategy that enables companies to simplify IT operations and improve IT job satisfaction. 5 IT management challenges of the AI era Cisco recently commissioned Forrester Consulting to conduct a Total Economic Impact™ analysis of Cisco Intersight. This IT operations platform provides visibility, control, and automation capabilities for the Cisco Unified Computing System (Cisco UCS), including Cisco converged, hyperconverged, and AI-ready infrastructure solutions across data centers, colocation facilities, and edge environments. Intersight uses a unified policy-driven approach to infrastructure management and integrates with leading operating systems, storage providers, hypervisors, and third-party IT service management and security tools. The Forrester study first uncovered the issues IT groups are facing: Difficulty scaling: Manual, repetitive processes cause lengthy IT compute infrastructure build and deployment times. This challenge is particularly acute for organizations that need to evolve infrastructure to support traditional and AI workloads across data centers and distributed edge environments. Architectural specialization and AI workloads: AI is altering infrastructure requirements, Forrester found.  Companies design systems to support specific AI workloads — such as data preparation, model training, and inferencing — and each demands specialized compute, storage, and networking capabilities. Some require custom chip sets and purpose-built infrastructure, such as for edge computing and low-latency applications.

@import url(‘https://fonts.googleapis.com/css2?family=Inter:[email protected]&display=swap’); a { color: var(–color-primary-main); } .ebm-page__main h1, .ebm-page__main h2, .ebm-page__main h3, .ebm-page__main h4, .ebm-page__main h5, .ebm-page__main h6 { font-family: Inter; } body { line-height: 150%; letter-spacing: 0.025em; font-family: Inter; } button, .ebm-button-wrapper { font-family: Inter; } .label-style { text-transform: uppercase; color: var(–color-grey); font-weight: 600; font-size: 0.75rem; } .caption-style { font-size: 0.75rem; opacity: .6; } #onetrust-pc-sdk [id*=btn-handler], #onetrust-pc-sdk [class*=btn-handler] { background-color: #1796c1 !important; border-color: #1796c1 !important; } #onetrust-policy a, #onetrust-pc-sdk a, #ot-pc-content a { color: #1796c1 !important; } #onetrust-consent-sdk #onetrust-pc-sdk .ot-active-menu { border-color: #1796c1 !important; } #onetrust-consent-sdk #onetrust-accept-btn-handler, #onetrust-banner-sdk #onetrust-reject-all-handler, #onetrust-consent-sdk #onetrust-pc-btn-handler.cookie-setting-link { background-color: #1796c1 !important; border-color: #1796c1 !important; } #onetrust-consent-sdk .onetrust-pc-btn-handler { color: #1796c1 !important; border-color: #1796c1 !important; } Coming out of 2025, AI data center development remains defined by momentum. But momentum is not the same as certainty. Behind the headlines, operators, investors, utilities, and policymakers are all testing the assumptions that carried projects forward over the past two years, from power availability and capital conditions to architecture choices and community response. Some will hold. Others may not. To open our 2026 industry polling, we’re taking a closer look at which pillars of AI data center growth are under the most pressure. What assumption about AI data center growth feels most fragile right now?

Sovereign AI and National Infrastructure Policy JLL frames artificial intelligence infrastructure as an emerging national strategic asset, with sovereign AI initiatives representing an estimated $8 billion in cumulative capital expenditure by 2030. While modest relative to hyperscale investment totals, this segment carries outsized strategic importance. Data localization mandates, evolving AI regulation, and national security considerations are increasingly driving governments to prioritize domestic compute capacity, often with pricing premiums reaching as high as 60%. Examples cited across Europe, the Middle East, North America, and Asia underscore a consistent pattern: digital sovereignty is no longer an abstract policy goal, but a concrete driver of data center siting, ownership structures, and financing models. In practice, sovereign AI initiatives are accelerating demand for locally controlled infrastructure, influencing where capital is deployed and how assets are underwritten. For developers and investors, this shift introduces a distinct set of considerations. Sovereign projects tend to favor jurisdictional alignment, long-term tenancy, and enhanced security requirements, while also benefiting from regulatory tailwinds and, in some cases, direct state involvement. As AI capabilities become more tightly linked to economic competitiveness and national resilience, policy-driven demand is likely to remain a durable (if specialized) component of global data center growth. Energy and Sustainability as the Central Constraint Energy availability emerges as the report’s dominant structural constraint. In many major markets, average grid interconnection timelines now extend beyond four years, effectively decoupling data center development schedules from traditional utility planning cycles. As a result, operators are increasingly pursuing alternative energy strategies to maintain project momentum, including: Behind-the-meter generation Expanded use of natural gas, particularly in the United States Private-wire renewable energy projects Battery energy storage systems (BESS) JLL points to declining battery costs, seen falling below $90 per kilowatt-hour in select deployments, as a meaningful enabler of grid flexibility, renewable firming, and

OpenAI framed Stargate as an AI infrastructure platform; a mechanism to secure long-duration, frontier-scale compute across both training and inference by coordinating capital, land, power, and supply chain with major partners. When OpenAI announced Stargate in January 2025, the headline commitment was explicit: an intention to invest up to $500 billion over four to five years to build new AI infrastructure in the U.S., with $100 billion targeted for near-term deployment. The strategic backdrop in 2025 was straightforward. OpenAI’s model roadmap—larger models, more agents, expanded multimodality, and rising enterprise workloads—was driving a compute curve increasingly difficult to satisfy through conventional cloud procurement alone. Stargate emerged as a form of “control plane” for: Capacity ownership and priority access, rather than simply renting GPUs. Power-first site selection, encompassing grid interconnects, generation, water access, and permitting. A broader partner ecosystem beyond Microsoft, while still maintaining a working relationship with Microsoft for cloud capacity where appropriate. 2025 Progress: From Launch to Portfolio Buildout January 2025: Stargate Launches as a National-Scale Initiative OpenAI publicly launched Project Stargate on Jan. 21, 2025, positioning it as a national-scale AI infrastructure initiative. At this early stage, the work was less about construction and more about establishing governance, aligning partners, and shaping a public narrative in which compute was framed as “industrial policy meets real estate meets energy,” rather than simply an exercise in buying more GPUs. July 2025: Oracle Partnership Anchors a 4.5-GW Capacity Step On July 22, 2025, OpenAI announced that Stargate had advanced through a partnership with Oracle to develop 4.5 gigawatts of additional U.S. data center capacity. The scale of the commitment marked a clear transition from conceptual ambition to site- and megawatt-level planning. A figure of this magnitude reshaped the narrative. At 4.5 GW, Stargate forced alignment across transformers, transmission upgrades, switchgear, long-lead cooling

There is also the Lenovo ThinkSystem SR650i, which offers high-density GPU computing power for faster AI inference and is intended for easy installation in existing data centers to work with existing systems. Finally, there is the Lenovo ThinkEdge SE455i for smaller, edge locations such as retail outlets, telecom sites, and industrial facilities. Its compact design allows for low-latency AI inference close to where data is generated and is rugged enough to operate in temperatures ranging from -5°C to 55°C. All of the servers include Lenovo’s Neptune air- and liquid-cooling technology and are available through the TruScale pay-as-you-go pricing model. In addition to the new hardware, Lenovo introduced new AI Advisory Services with AI Factory Integration. This service gives access to professionals for identifying, deploying, and managing best-fit AI Inferencing servers. It also launched Premier Support Plus, a service that gives professional assistance in data center management, freeing up IT resources for more important projects.

SK Hynix reported during its October earnings call that its HBM, DRAM, and NAND capacity is “essentially sold out” for 2026, while Micron recently exited the consumer memory market entirely to focus on enterprise and AI customers. Enterprise hardware costs surge The supply constraints have translated directly into sharp price increases across enterprise hardware. Samsung raised prices for 32GB DDR5 modules to $239 from $149 in September, a 60% increase, while contract pricing for DDR5 has surged more than 100%, reaching $19.50 per unit compared to around $7 earlier in 2025. DRAM prices have already risen approximately 50% year to date and are expected to climb another 30% in Q4 2025, followed by an additional 20% in early 2026, according to Counterpoint Research. The firm projected that DDR5 64GB RDIMM modules, widely used in enterprise data centers, could cost twice as much by the end of 2026 as they did in early 2025. Gartner forecast DRAM prices to increase by 47% in 2026 due to significant undersupply in both traditional and legacy DRAM markets, Chauhan said. Procurement leverage shifts to hyperscalers The pricing pressures and supply constraints are reshaping the power dynamics in enterprise procurement. For enterprise procurement, supplier size no longer guarantees stability. “As supply becomes more contested in 2026, procurement leverage will hinge less on volume and more on strategic alignment,” Rawat said. Hyperscale cloud providers secure supply through long-term commitments, capacity reservations, and direct fab investments, obtaining lower costs and assured availability. Mid-market firms rely on shorter contracts and spot sourcing, competing for residual capacity after large buyers claim priority supply.