CHATSWORTH, CA — January 13, 2026 — As enterprises race to adopt and deploy artificial intelligence (AI), a new report finds the biggest threat to success isn’t the size of your model or the speed of accelerators — it’s the infrastructure that supports them.
The 2026 AI Infrastructure Report, conducted by Vanson Bourne and commissioned by AI data platform provider DDN, with collaboration from Cognizant and Google Cloud, surveyed 600 U.S. IT and business leaders to uncover the hidden pressures slowing AI adoption.
The findings reveal that while organizations are seeking to adopt AI, most lack the foundation to sustain it. Rising complexity associated with infrastructure, underutilized cloud environments, unplanned energy requirements, and persistent skills gaps are quietly stalling projects and eating into ROI.
Findings:
- Two-thirds of organizations (65%) say their AI environments are too complex to manage.
- Over half (54%) have delayed or canceled AI initiatives in the past two years.
- A majority (97%) agree that cloud infrastructure is essential to scaling AI.
- Nearly all (93%) are actively seeking to reduce AI’s energy impact.
- Most (83%) say their internal teams are struggling with AI workloads today.
“The AI boom has hit an infrastructure wall,” said Alex Bouzari, CEO and Co-Founder at DDN. “Companies are chasing models and GPUs, but the real bottleneck is the data layer underneath. Without modern, unified infrastructure, AI can’t scale.”
The study exposes that AI infrastructure complexity, not capability, is the silent killer of ROI.
Sixty-five percent of respondents say their AI environments are already too complex to manage, causing 54% of them to delay or cancel AI projects. With AI workloads projected to grow 110% in the next year, 76% of leaders still face fundamental data challenges, from legacy infrastructure and siloed datasets.
The reported complexity in managing AI environments, affecting two-thirds of organizations (65%), is fundamentally a consequence of infrastructure fragmentation; organizations deploy AI workloads across a patchwork of disconnected solutions, which are separate systems for data processing, training compute, and serving endpoints, none of which were inherently designed for the scale and demanding requirements of Gen AI. This fragmentation mandates continuous, complex data movement, necessitates manual, resource-intensive resource orchestration across disparate silos, and ultimately prevents the cohesive scaling of compute, storage, and networking necessary for efficiency.
Unified AI infrastructure, provided by leaders like DDN, purpose-built for scale, simplicity, and efficiency, is now the single biggest driver of success. Trying to retrofit traditional, fragmented systems to handle modern AI workloads rarely works and often guarantees failure. The organizations breaking through are those simplifying—not stacking.
“Enterprises are discovering that scaling AI isn’t a compute problem—it’s an integration problem. If your infrastructure isn’t unified, your AI can’t learn efficiently. Simplicity is the new scalability.” —Sven Oehme, DDN CTO
Ninety-seven percent of respondents say cloud is essential to scaling AI, and more than half cite it as their fastest path to production. Cloud-based deployments let teams experiment, onboard GPUs faster, adopt the latest technologies quickly, and reduce early-stage failure rates; making it the most common “launch zone” for successful AI adoption.
“The survey responses validate the investments we’ve made over the years to develop a robust cloud infrastructure that empowers organizations to easily scale AI workloads,” said Asad Khan, Sr Director, Product Management, Google Cloud. “With Google Cloud Managed Lustre, customers can leverage the latest GPUs and TPUs while reducing complexity and accelerating innovation.”
AI’s next constraint isn’t compute, it’s energy. AI’s rapid scale has created an unprecedented energy demand that has become an operating constraint in AI data centers. Most (93%) of respondents report they are actively seeking to reduce AI’s energy footprint, and nearly half (47%) cite power and cooling costs as their top infrastructure constraint.
The report highlights the importance of maximizing AI output per watt, a next-generation efficiency metric that quantifies how effectively AI workloads convert energy into usable compute. DDN is uniquely positioned to help organizations improve their energy usage situation by keeping GPUs fully saturated and moving data in parallel, enabling up to 70% reduction in power and cooling costs.
Almost all (98%) organizations cite a skills shortage (in both IT and data science roles) as a major barrier to scaling AI. In fact, external research highlights that 65% of organizations have abandoned AI projects due to a lack of skills. However, the report shows that one of the ways leaders are closing this gap is through ecosystem collaboration, pairing internal expertise, proven reference architectures, and pre-tested solutions with partners like DDN, Cognizant, Google Cloud, NVIDIA, and others.
Most enterprises now recognize that external expertise is not a stopgap—it’s a strategic enabler. That’s why 72% rely on third-party expertise to build and manage their AI infrastructure, while just 12% depend solely on in-house talent. Organizations who make this connection are forming deeper, long-term partnerships that accelerate implementation while transferring knowledge and reducing operational friction.
“I hope to strengthen cross-departmental collaboration to better integrate the expertise and resources of different teams. This will help improve the efficiency and effectiveness of AI projects, ensuring that we can fully leverage the potential of AI.”—IT decision maker, Finance
The research underscores that tomorrow’s AI leaders won’t be defined by bigger models; they’ll be defined by smarter infrastructure. Organizations that simplify, unify, and optimize their systems while leveraging partner expertise report faster time-to-results, better GPU utilization, and stronger operational momentum. Those still relying on fragmented or siloed infrastructure are seeing costly delays and lower ROI.
In response to these findings, DDN is reaffirming its commitment to accelerating enterprise AI adoption through a new generation of programs launched at SC25.
Each program is designed to eliminate complexity, improve efficiency, and shorten the path from experimentation to production AI.
- AI FASTTRACK: A comprehensive initiative to help enterprises move from AI pilots to production with speed, simplicity, and confidence.
- DDN Enterprise AI HyperPOD™: A turnkey RAG and inference platform co-engineered with Supermicro and NVIDIA.
Collaboration Across the Ecosystem
The report reflects the joint expertise of DDN, Cognizant, and Google Cloud:
- DDN provides the data intelligence backbone to power AI at scale.
- Cognizant brings deep enterprise transformation expertise, partnering with organizations to operationalize AI and achieve measurable business value.
- Google Cloud delivers scalable, AI-native infrastructure designed for efficiency and sustainability.
The State of AI Infrastructure 2026 was conducted by Vanson Bourne, an independent research firm, and commissioned by DDN, with sponsorship from Cognizant and Google Cloud. The study surveyed 600 senior IT and business leaders across industries in the U.S. between July and September 2025.
