Computing infrastructure in a new era

Is your computing infrastructure ready to fuel business growth in the AI era?

Explore how emerging tech and flexible, AI-powered solutions can transform digital operations into strategic growth drivers for organizations.

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Computing Infrastructure in a Nutshell

Computing infrastructure forms the core of an organization’s technology landscape, integrating hardware, software, networks, and cloud systems to power data processing, storage, and communication. It supports everyday operations, drives productivity, and strengthens competitive advantage. From servers and storage to operating systems and business applications, these components work together to ensure secure and scalable performance. Infrastructure can be deployed in traditional, cloud-based, or hybrid models—each offering distinct benefits tailored to different business needs.

Emerging technologies are reshaping how organizations approach computing infrastructure.

As developments like GenAI, AI agents, and quantum computing increase companies’ data requirements, computing infrastructure is evolving from a basic utility to a key business enabler. Once considered a back-office concern, digital infrastructure is now a board-level priority.

Infrastructure disruptions can directly affect an organization’s reputation and stakeholder trust. For companies operating in regulated sectors—or those delivering AI- enabled services as part of their core offerings—poor infrastructure planning may contribute to financial risk, including potential credit impacts.

As highlighted in the KPMG 2025 Futures Report, the stakes are high for companies evaluating infrastructure options that will support future growth.

Unlock the insights you need to lead in a rapidly evolving landscape. The 2025 Futures Report delves into seven pivotal areas of innovation: Artificial Superintelligence, Computing Infrastructure, Quantum Computing, Space Economy, Digital Assets, Environmental Resilience, and Advanced Manufacturing.

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Historically, many companies have relied on their own data centers to achieve cost efficiencies, maintain data proximity, and meet tailored infrastructure needs.

Organizations continuing this approach in the age of AI may find it offers a more direct path to integrating the technology across their operations. However, this requires a unified vision and coordinated decisions across IT, finance, and product teams.

Meanwhile, many organizations are shifting more of their data to cloud solutions. In these cases, companies must assess which workloads are best suited for public cloud versus on-premises environments. On-premises infrastructure often requires upfront investment in secure systems and customized AI software stacks, but may offer lower ongoing costs. Regardless of location, cloud solution costs can escalate quickly with extensive AI workloads.

Hybrid cloud models—combining on-premises infrastructure with cloud capabilities—are proving to be a viable option for organizations seeking flexibility based on evolving workload needs and strategic goals.

As AI adoption grows, hyperscale data centers are a compelling option due to their ability to support large-scale AI workloads and deliver the necessary computational power. These centers feature high server density, access to advanced silicon chips, and scalable infrastructure designed for high-speed connectivity.

A notable trend is the use of co-location facilities, which can accommodate increased power demands while offering space and support for managing AI workloads. These models also allow organizations to share operational and financial risks with co-location providers.

Co-location considerations

Proximity

Close proximity to existing data centers and cloud providers helps minimize latency. Organizations with real-time processing needs or low-latency applications may benefit from edge computing or facilities located near users or data sources. For example, most public clouds on the East Coast are hosted in Virginia, while geographic features in the western U.S. may impact data transmission.

Energy and space costs

Power and space availability in co-location facilities are critical. The energy required to run AI applications can significantly affect operating costs. Organizations should seek facilities with favorable utility pricing and mixed-source energy solutions—such as modular nuclear, geothermal, and battery storage—to ensure uptime. It’s important to proactively secure future power availability by specifying kilowatt needs and timelines to co-location partners, and to formalize agreements to prevent jurisdictional changes

Tax incentives

State and local governments may offer incentives for using co- location facilities, including sales tax exemptions, R&D support, reduced property costs, and manufacturing benefits. These incentives can yield immediate or long- term savings. Companies should engage early with jurisdictions to negotiate favorable terms.

Looking ahead, organizations will increasingly treat infrastructure as a dynamic business capability. This includes adopting infrastructure-as-code and AI-native development workflows to integrate intelligence across systems. Progressive companies will also embrace carbon-aware computing to guide long-term IT decisions, especially as emissions data becomes a focus for investors and regulators.

As advanced technologies like quantum and neuromorphic computing emerge, organizations must explore orchestration frameworks that enable adoption without disrupting core operations.

	Plan Ahead for Co-location: Early consideration of tax incentives, latency, and power/space availability is essential due to growing competition for resources.
	Optimize AI Workload Distribution: Organizations must evaluate which workloads are best suited for cloud versus co-location environments. Total cost of ownership, startup costs, and ongoing IT and staffing needs should inform these decisions.
	Talent Availability: Talent shortages across data center models are a growing challenge, especially for roles like control engineers and monitoring specialists. Even in areas like cybersecurity and network operations, high turnover and competition persist. Succession planning, knowledge transfer, and upskilling are critical to maintaining institutional expertise.

KPMG LLP supports organizations with site selection and foundational infrastructure planning—whether building new data centers for AI workloads or securing space in existing co-location facilities. Through our managed services, we help clients strategize AI workload distribution, manage AI software stacks, and address talent needs. Our business-first approach and strategic alliances help organizations stay ahead in a rapidly evolving landscape.

Our services include:

Modern Managed Services: Support for identifying, installing, securing, and managing AI workloads across public cloud and co-location environments.
Tax Incentive Consulting: Guidance on negotiating incentives and selecting optimal sites, informed by experience from prior engagements.
Site Selection Development: Searches based on latency, proximity, energy costs, incentives, and availability.