How AI Workloads are Redefining the Role and Design of Data Centers — BizTechReports - July 16, 2025
By Abby Fontz - July 16th, 2025
The growing reach of AI across business operations is reshaping the demands placed on data centers in every industry. This BizTechReports briefing synthesizes insights from four recent reports to explore how data centers are evolving—consolidating platforms, securing new power sources, adopting more flexible investment models, and redesigning infrastructure to support AI at scale.
As generative AI (GenAI) moves from pilot projects to production environments, enterprises are encountering a wave of new infrastructure constraints—particularly around data center design, energy management, and compute provisioning. For many IT and business leaders, supporting AI workloads at scale has become a catalyst for a broader reassessment of how infrastructure is architected, financed, and maintained.
Recent analysis from BizTechReports and experts at Gartner, NYI, and Vigilent suggests that data centers, long considered backend utilities, are now critical enablers of digital competitiveness. As AI capabilities expand, so too do the requirements for flexibility, efficiency, and long-term infrastructure planning.
Aligning Infrastructure with Core Business Strategy
One such foundational shift underway is the move toward platform consolidation. Business leaders are evaluating whether their current data management platforms can be transformed into a Retrieval-augmented generation (RAG) based system, replacing stand-alone document/data stores as the knowledge source for business GenAI applications. According to Gartner, by 2028, 80% of GenAI applications will be developed using existing data management platforms rather than isolated or purpose-built systems (Gartner, June 2, 2025).
Prasad Pore, Senior Director Analyst, Gartner
This trend reflects a strategic shift in how enterprises approach AI—not as a separate technical initiative, but as an integrated part of broader digital transformation agendas. In practice, this means investing in infrastructure that supports data interoperability, governance, and responsiveness across the enterprise. For organizations that have previously relied on fragmented platforms or legacy environments, the shift presents both a technical and organizational challenge.
Rising Energy Demands Reshape Infrastructure Planning
Bob Johnson, VP Analyst, Gartner
Supporting AI workloads at scale has introduced an increasingly urgent issue: power. Model training and inference tasks require immense computational resources, which in turn place growing strain on grid capacity and energy budgets. Gartner analysts have noted that nuclear energy—long sidelined in enterprise IT discussions—is re-emerging as a serious consideration for long-term, sustainable power (Gartner, June 3, 2025).
This discussion isn’t purely speculative. Power availability is now a gating factor in data center location decisions, procurement strategies, and operational continuity. The shift toward energy-intensive AI operations is forcing organizations to consider alternative sources, as well as smarter energy management strategies inside the facility.
Cooling systems, density management, and workload placement are all being re-evaluated in light of the performance-to-power tradeoffs AI introduces. In parallel, emissions and ESG targets are pushing operators to balance performance needs with environmental impact.
Investment Models Shift Toward Flexibility and Control
Financial considerations are also playing a larger role in how data center decisions are made—particularly for mid-sized organizations. While cloud infrastructure remains a major component of many enterprise strategies, overreliance on single-vendor environments has prompted a growing number of businesses to explore hybrid and colocation options.
NYI reports that many mid-market firms are reassessing their approach, opting for infrastructure models that allow more control over cost, performance, and compliance (NYI, April 16, 2025).
Phillip Koblence, Co-founder & COO, NYI
This reflects a pragmatic response to the unpredictability of AI workloads. Enterprises want to avoid being locked into capacity or pricing models that may not reflect real-world usage. Hybrid approaches—balancing on-premise, colocation, and cloud—allow organizations to adjust more quickly and contain infrastructure costs without sacrificing responsiveness or security.
Design Requirements Evolve with AI’s Unpredictability
Cliff Federspiel, Founder, President and CTO, Vigilent
From an engineering perspective, traditional data center design principles are being tested. AI workloads introduce not only higher power and compute demands, but also a level of variability that many static infrastructure environments aren’t built to handle.
In a recent BizTechReports interview with Vigilent founder, president and CTO Cliff Federspiel, modularity, real-time resource orchestration, and intelligent cooling systems were highlighted as core components of next-generation data center design (Vigilent, June 23, 2025).
Operators are increasingly adopting flexible design frameworks to accommodate workload bursts, fluctuating power densities, and dynamic provisioning needs. Physical space planning, network routing, and even floor layout are being reconsidered to better support rapid changes in usage patterns.
As AI systems evolve, so too must the underlying infrastructure—often in ways that demand new skills, new partners, and new capital strategies.
The Role of Infrastructure in the GenAI Economy
Across industries, the pressure to deploy AI efficiently is reframing how enterprises define infrastructure readiness. Data centers are no longer static support systems—they’re adaptive platforms for growth, differentiation, and long-term value creation.
For technology and business leaders, this means rebalancing investments between core infrastructure, energy sourcing, platform modernization, and data accessibility. The organizations that succeed in this transition will be those that align infrastructure decisions with business outcomes—while remaining agile enough to adapt to what AI requires next.