As artificial intelligence shifts from experimental pilots to business-critical applications, enterprises are adopting more complex infrastructure strategies that emphasize hybrid models and geographic distribution. That is the conclusion of new sponsored research published by DataBank, a U.S.-based provider of enterprise colocation, connectivity, and managed services.
The study, Accelerating AI: Navigating the Future of Enterprise Infrastructure, finds that companies are moving away from relying exclusively on generic, cloud-based AI platforms. Instead, they are increasingly developing customized models and distributing workloads across colocation, on-premises systems, and cloud environments. This evolution reflects both the growing demands of AI-driven operations and the limitations of single-environment solutions.
According to DataBank CEO Raul Martynek, most businesses begin their AI initiatives in the cloud, but infrastructure flexibility has become a priority as workloads diversify. “The ability to support distributed inference alongside centralized training, while still meeting security and compliance requirements, is essential,” said Mr. Martynek. He emphasized that hybrid approaches are quickly becoming the norm rather than the exception.
The research identified five key trends shaping enterprise AI adoption:
First, organizations are already seeing measurable business gains. More than one-third of respondents expect to generate returns within the next year, while a quarter report steady annual ROI. The quick wins associated with early AI adoption are now giving way to transformative use cases that redefine business processes.
Second, the nature of challenges facing companies is shifting. While poor data quality was once cited as a major barrier, only 20 percent now see it as a leading issue. Instead, organizations point to shortages of skilled talent, difficulties scaling AI operations, and integration complexities as their main obstacles.
Third, hybrid infrastructure is emerging as the standard. Although nearly two-thirds of enterprises begin AI efforts in public or private clouds, many later add colocation and on-premises systems to manage sensitive workloads more securely and cost-effectively.
The fourth finding is a move toward global distribution of AI workloads. More than three-quarters of businesses plan to expand AI infrastructure closer to data sources and end users, reducing latency and meeting regulatory requirements. While inference workloads are being spread across geographies, the training of AI models is increasingly centralized.
Finally, AI strategies themselves are becoming more sophisticated. Enterprises are transitioning from broad, off-the-shelf models to proprietary and customized systems. Infrastructure approaches now combine large language models, specialized solutions, and tailored deployment methods.
The implications for enterprise infrastructure would be significant. Latency optimization, compliance, and scalable design are now critical factors in deployment decisions. Contrary to earlier expectations, the report indicates that data maturity is no longer the main bottleneck. Instead, the lack of skilled expertise has emerged as the central challenge.
DataBank notes that enterprises planning AI infrastructure should prepare for a future defined by hybrid, distributed strategies and proprietary models. The full report, Accelerating AI: Navigating the Future of Enterprise Infrastructure, provides detailed analysis and recommendations for organizations seeking to align infrastructure with evolving AI demands.
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