AI infrastructure is becoming a core driver of business outcomes rather than just an IT concern. According to 451 Research’s Voice of the Enterprise: AI & Machine Learning, Infrastructure 2025 survey, 94% of AI infrastructure buyers believe their infrastructure choices create competitive advantages for their organizations. This advantage comes from several areas: - **Performance and scalability:** Organizations face insatiable demand for compute resources and workload acceleration. The ability to run AI and ML workloads efficiently, at scale, directly affects how fast teams can experiment, deploy models, and iterate. - **Data handling across the lifecycle:** Rapidly expanding and increasingly unwieldy data volumes are now a major pain point. Infrastructure that supports strong data management, security, and privacy across the full data lifecycle helps organizations unlock more value from their data while staying compliant. - **Deployment flexibility:** Where AI workloads run is now a strategic choice. Many organizations are using a mix of on-premises, public cloud, managed service providers, colocation, and emerging edge locations. This hybrid approach lets them balance performance, cost, governance, and latency. As organizations gain more experience with AI—evidenced by rising generative AI adoption and declining project failure rates—the link between infrastructure decisions and business impact becomes clearer. Infrastructure is effectively the foundation that allows companies to reimagine how they build, deploy, and scale AI capabilities.

AI infrastructure budgets are growing quickly and drawing from multiple parts of the business. Key trends from the 451 Research study include: - **Broad-based budget growth:** More than 70% of organizations plan to increase spending across all major AI infrastructure categories—computing devices (such as AI PCs), servers, accelerators, storage, and networking, both on-premises and off-premises. - **Significant spending increases:** Over one-third of organizations expect to increase AI infrastructure spending by 25% or more, and roughly 1 in 10 plan increases of 50% or more. - **Meaningful share of IT budgets:** For many organizations, AI infrastructure already represents a substantial portion of IT spend. 44% say AI infrastructure accounts for 10–24% of their IT budgets, and another 33% report that it makes up 25% or more. - **Funding beyond IT:** AI infrastructure is not funded solely from IT budgets. More than two-thirds of organizations receive at least 10% of their AI infrastructure funding from outside IT, reflecting the cross-functional nature of AI initiatives. Decision-making authority still largely sits with IT, particularly CIOs and IT infrastructure leaders, but executive leadership and data and analytics leaders are also heavily involved. This mix of stakeholders and funding sources underscores that AI infrastructure is now a shared strategic investment, not just an operational line item.

Organizations are taking a hybrid, multi-venue approach to AI and ML workloads, with a mix of on-premises, cloud, and third-party environments. **Workload locations and strategies** - **On-premises vs. public cloud:** 57% of organizations train models on-premises, compared with 48% in public cloud. For production deployment, 53% run models on-premises and 47% in public cloud. - **Third-party environments:** More than half of organizations train and deploy models in managed service provider environments, and more than one-third use colocation facilities. - **Edge computing:** While still early, the edge is emerging as an important venue. 22% train models in edge locations (non-core datacenters), and 27% operationalize models at the edge. - **Hybrid by default:** 63% of organizations plan to use a combination of on-premises and public cloud for new AI/ML workloads over the next year. Only 18% expect to stay on-premises only, and 19% plan to use only public cloud. When it comes to moving workloads, most organizations are optimizing rather than overhauling: - 69% expect only slight shifts or no change in how they move AI/ML workloads between on-premises and public cloud. - 31% foresee a slight net movement from on-premises datacenters to public cloud. - 17% plan to move workloads only into public cloud, while 11% plan to move only from public cloud back on-premises. **Key challenges across the stack** Despite progress, organizations report challenges at multiple layers: - Around half say compute, storage, and networking for AI workloads are somewhat or very challenging, across on-premises, cloud, and edge. - About 6 in 10 struggle with data management and availability (57% on-premises, 56% cloud, 56% edge). - Security and privacy are top concerns, cited as challenging by 64% on-premises, 62% in cloud, and 60% at the edge. These pain points align with top buying criteria: security (55%), infrastructure reliability and availability (47%), and data privacy and governance (45%). When asked what would most improve AI/ML performance, buyers highlight access to cloud-based accelerators (54%), better storage performance (46%), on-premises GPUs (46%), enhanced networking (44%), and more memory capacity (40%). Sustainability is also gaining attention. 39% now cite sustainability as a key factor in AI infrastructure decisions, up from 33% a year earlier, as organizations rethink how and where they run resource-intensive AI workloads.