Recently, DataBank sponsored research seeking to understand where on the AI adoption curve enterprises see themselves as well as where they are experiencing their greatest opportunities and obstacles.
Data Maturity is Less of a Bottleneck; Integration, Scaling, and Talent Gaps Are Now Key Barriers
Fewer organizations cite poor data quality as a major blocker (only about 20% report issues), reflecting substantial investment in data readiness.
The primary remaining hurdles are around integration, scaling, deployment, and particularly talent shortages, with leadership and upskilling seen as crucial for successful AI adoption. (See related resource: Human Capital in the Cloud: USA’s Data Center Workforce Trends)
A Hybrid AI Infrastructure Approach is Emerging as the Norm
Nearly two-thirds (64%) of enterprises currently start with public/private cloud for AI, but there is a clear trend toward hybrid models. (See related resource: Has a Cloud-Only IT Strategy Been Stretched to Its Limits?)
Organizations are increasingly combining cloud services with on-premises and colocation data centers (especially for sensitive workloads), driven by factors such as security, compliance, performance, and data sovereignty.
Geographic Decentralization of Infrastructure is Accelerating
Seventy-six percent of respondents expect to expand their AI infrastructure into new regions closer to data sources or end-users, mainly for latency reduction, regulatory compliance, and improved performance.
While AI training is expected to centralize, inference workloads will become increasingly distributed geographically. (See related resource: How AI at the Edge is Revolutionizing Real-Time Decision Making)
AI Adoption Strategies are Becoming More Sophisticated and Use-Case Specific
Enterprises are moving from generic third-party AI/LLM tools to more customized or fully proprietary models to address business needs.
The approach to both AI applications and underlying infrastructure is becoming multifaceted – blending off-the-shelf applications, custom solutions, and tailored deployment models to optimize for security, compliance, and performance. (See related resource: Why Machine Learning Models Demand High Performance Computing)
Accelerating AI Infrographic
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