Fragmented Infrastructure Is Holding Your AI Back

• • Fragmented hybrid infrastructure is the primary barrier preventing enterprises from scaling AI workloads, with 67% of decision makers reporting their environments feel disconnected and disjointed.
  • Storage performance and data access have surpassed compute as the leading bottlenecks for critical workloads, making a unified data foundation essential for AI success.
  • Enterprises are actively consolidating around fewer, more powerful platforms, with 92% prioritizing partners who can deliver unified server, storage and data protection rather than separate point products.

Most enterprises don’t have an infrastructure shortage. They have an infrastructure coherence problem. Years of point-solution buying, parallel cloud migrations and disconnected modernization initiatives have produced environments that are capable in isolation but fragmented as a whole. And when it’s time to run AI at production scale, that fragmentation becomes the ceiling.

According to the Modern Enterprise Readiness Study, 67% of business and IT decision makers say their hybrid and multicloud environment isn’t fully unified and feels fragmented. Another 70% agree that running AI, workplace and data center modernization as separate initiatives makes it harder to operate efficiently. These aren’t outliers. They’re the predictable result of an era where speed of adoption mattered more than architectural coherence.

The question isn’t how it happened. It’s what to do about it now.

The bottleneck isn’t where you think it is

When AI initiatives stall, the instinct is to look at compute. But the data tells a different story. Eighty percent of respondents say storage performance and data access are now bigger bottlenecks than raw compute capacity for many of their critical workloads. Meanwhile, 66% say their data center and infrastructure aren’t fully ready to support their most demanding workloads at production scale, including AI and analytics.

AI workloads are unforgiving. They require fast, clean and consistent access to large volumes of data. When the underlying infrastructure is fragmented, that access becomes unreliable. Data pipelines slow down. Security policies become inconsistent. Integration overhead consumes budget and bandwidth before a single model reaches production.

Fifty percent of respondents also lack a clear, actionable roadmap for innovation across AI, data and security. The bottleneck isn’t ambition. It’s the infrastructure beneath it.

What unified infrastructure actually delivers

The case for consolidation isn’t theoretical. Enterprises are already moving in this direction, and the momentum is clear.

• • 89% plan to join onto fewer, more powerful storage and server platforms to support data-intensive workloads while reducing complexity.
  • 92% say they’re more likely to view a partner as strategic if that partner can deliver a unified server, storage and data protection platform rather than separate point products.
  • 92% say integrated data protection and cyber-recovery capabilities are now key criteria when selecting storage and server platforms.
  • 90% expect security to be embedded in every proposal from a strategic partner, not treated as a separate add-on.

When storage, compute, data protection and AI infrastructure are unified, each layer strengthens the others rather than creating new friction. Security and resilience, when built into the platform from the start, stop being obstacles and start functioning as accelerators. Simplification isn’t a compromise. It’s a structural advantage.

Distributed doesn’t have to mean disconnected

A common concern is that distributed AI infrastructure will recreate the same fragmentation enterprises are trying to escape. It doesn’t have to. Eighty-eight percent of respondents are already running at least one AI workload on premises, and leading enterprises are deploying workloads across on-premises, edge and cloud environments while maintaining a unified view of their data, security posture and operational performance.

The architecture that makes this work connects your layers: storage, compute, networking, data protection and AI infrastructure aligned so each component reinforces the others. When infrastructure is distributed but managed as a coherent whole, AI workloads can run where they need to run without generating new silos.

The competitive dimension here is real. When your infrastructure is unified, your teams spend less time managing integration and more time delivering outcomes. Procurement consolidates. Security policies apply consistently. AI initiatives move from proof of concept to production without unnecessary roadblocks.

Three practical starting points

You don’t have to simplify everything at once. You just have to start in the right place.

1. 1. Audit your infrastructure for fragmentation costs. Map where integration complexity, data silos and disconnected protection are slowing your teams down and costing more than they should.
   2. Trace your highest-priority AI workloads back to your data foundation. Where is data access creating bottlenecks? Where is storage performance the limiting factor? Start your modernization there.
   3. Ask your technology partners the right questions. Can they deliver a unified server, storage and data protection platform? Do they have a clear approach to distributed AI infrastructure? Can they show you a roadmap from your current environment to a full-stack, AI-ready foundation?

The path to AI at scale runs through your infrastructure, not around it. Enterprises that address fragmentation directly, unifying their platforms, modernizing their data foundation and embedding protection at every layer, are building something that lasts. They’re not just running AI pilots. They’re running AI businesses.

Connect with a Dell expert to explore what a unified infrastructure roadmap looks like for your environment, or register for a Dell Technologies Forum near you to see these solutions in action.

Source: Modern Enterprise Readiness Study, a Dell Technologies and Vanson Bourne survey across 2,950 business and IT decision makers from 35 countries, Jun 2026.

Frequently Asked Questions:

Why is storage performance a bigger bottleneck than compute for AI workloads?

AI workloads require fast, consistent and high-volume access to data. When storage systems are siloed or underperforming, data pipelines slow, models can’t be fed at the speed they need and AI initiatives stall before they reach production. The Modern Enterprise Readiness Study found that 80% of respondents identify storage performance and data access as bigger bottlenecks than raw compute capacity, which reflects how fundamentally data-intensive modern AI workloads have become.

What does unified infrastructure look like in a real enterprise environment?

Unified infrastructure means your storage, servers, data protection and AI infrastructure are designed to work together as a coherent platform rather than as separate tools from separate vendors. It allows consistent security policies, simplified management, faster data access for AI workloads and greater resilience. It doesn’t require replacing everything at once. It means filling gaps with platforms that are built to integrate, and consolidating around partners who can support the full stack.

How should enterprises prioritize where to begin their infrastructure modernization?

Start by identifying where fragmentation is costing you the most. Map the integration complexity, data silos and disconnected protection layers that are slowing your teams down. Then trace your highest-priority AI workloads back to the data foundation they depend on. Where is data access creating bottlenecks? Where is storage performance the limiting factor? Starting there, rather than trying to modernize everything at once, gives you early momentum and a clear line of sight to AI at scale.