Artificial intelligence has moved beyond experimentation. Enterprises are now under pressure to operationalize AI across every business function—from copilots and retrieval-augmented generation (RAG) to agentic AI and domain-specific foundation models.
As organizations move from pilots to production, they are discovering that scaling AI is no longer simply a compute challenge. It is equally a data, infrastructure, and operational challenge.
One of the Gartner® report’s central observations is:
“As organizations shift toward AI-first operating models and AI agents become increasingly widespread, the demand for AI factories grows.”
Gartner further states:
“The hardware for AI infrastructure integrates accelerator-based compute, high-speed networking, and specialized storage to maximize performance and efficiency for AI workloads such as chatbots, RAG, and AI agents.”
At DDN, we believe this reflects a significant shift in enterprise AI. The next generation of AI will not be defined simply by GPU count, but by how efficiently organizations can keep those GPUs supplied with data, support diverse AI workloads, and convert infrastructure investments into measurable business outcomes.
AI Factories Are Becoming the Enterprise Operating Model
According to Gartner,
We believe an AI factory must be treated as a complete production system.
Compute generates tokens, but the data platform determines how efficiently those tokens can be produced. Data ingestion, metadata management, checkpointing, intelligent caching, governance, and high-speed storage all directly influence GPU utilization, model performance, and infrastructure economics.
As AI workloads become increasingly agentic, these capabilities become foundational to enterprise success.
Power Has Become the New Constraint
Gartner identifies power and cooling as primary challenges facing enterprise AI deployments:
“AI infrastructure powered by advanced GPUs challenges the power and cooling capacities of current enterprise data centers.”
The report also notes:
“AI infrastructure at the multirack scale often demands high power densities and liquid cooling, exceeding the capabilities of traditional data centers.”
This changes how enterprises should evaluate AI infrastructure.
Instead of asking only how much compute they can deploy, organizations increasingly need to understand:
- How efficiently are GPUs being utilized?
- How quickly can data move through the AI pipeline?
- How much AI output can be generated within existing power budgets?
- How rapidly can new AI services reach production?
- What is the cost per AI workload and per token delivered?
Improving GPU utilization and eliminating data bottlenecks can often increase AI capacity without adding more hardware, power, or data center space.
AI Factory Reference Architectures Reduce Deployment Complexity
Gartner also highlights the growing importance of integrated AI infrastructure.
The report states:“Integrated rack-scale solutions supporting four to 32 GPUs, targeting air-cooled or retrofit data centers. In addition to fitting into facilities constraints, these curated-stack, integrated systems enable faster time-to-production versus building infrastructure piece-by-piece yourself.”
We believe this reflects an important market transition.
Organizations increasingly want validated architectures rather than assembling infrastructure from individual components. AI factories must integrate compute, networking, storage and software into a production-ready platform that can scale with evolving enterprise requirements.
Reference architectures help reduce deployment risk, accelerate implementation and provide a foundation that supports future AI growth.
Data Intelligence Is Becoming Strategic Infrastructure
Gartner states: “The hardware for AI infrastructure integrates accelerator-based compute, high-speed networking, and specialized storage to maximize performance and efficiency for AI workloads such as chatbots, RAG, and AI agents.”
We believe this is where data intelligence becomes a strategic differentiator.
High-performance storage, intelligent caching, rapid checkpointing, metadata services and secure multi-tenant architectures directly affect:
- GPU utilization
- Training performance
- Inference latency
- Time to first token
- AI economics
- User experience
As enterprises deploy RAG, inference and agentic AI, the data platform becomes the operational memory layer of the AI factory—continuously placing, protecting and delivering data wherever models and AI agents require it.
Operational Intelligence Determines Long-Term AI Success
These are increasingly the metrics enterprise leaders care about because they connect infrastructure performance directly to business outcomes.
We believe organizations should evaluate AI factories by the value they deliver:
- Faster model development
- Higher GPU utilization
- Lower infrastructure cost per workload
- Improved inference responsiveness
- Greater AI output from existing infrastructure
The defining question is no longer How much AI infrastructure have we deployed?
It is How much business value can that infrastructure produce?
Building the Next Generation of Enterprise AI
At DDN, we believe the data platform is the strategic foundation of every AI factory. When data is intelligently orchestrated across the AI lifecycle, organizations can maximize GPU productivity, improve operational efficiency, and generate greater business value from every infrastructure investment.
Gartner Attribution
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