- Half of the companies have canceled AI projects due to poor infrastructure
- 97% agree that cloud is the key to simplification and efficiency
- Companies are also under pressure from a sustainability angle
Artificial intelligence is proving not to be the golden key for many companies, with two in three (65%) admitting their AI environments are too complex to manage and more than half (54%) canceling AI projects over the past two years due to infrastructure issues.
And infrastructure, according to DDN’s latest State of AI Infrastructure Report, is exactly what’s holding companies back, quickly followed by energy.
Looking ahead, 97% agree that cloud is critical to scaling AI initiatives, with hybrid AI workloads expected to grow 162% over the next 12 months.
AI relies on a good infrastructure foundation
DDN’s report reveals the significant role played by third parties, with 72% relying on external expertise and only 12% relying solely on internal teams. This is notable because 83% agree that teams are struggling today and 98% admit to deficiencies in AI skills, further underscoring the need for outside help.
The study also found that most failures can be traced back to silos, either in storage, computing or data pipelines. “Companies are discovering that scaling AI isn’t a computing problem—it’s an integration problem,” wrote DDN CTO Sven Oehme. “If your infrastructure isn’t unified, your AI can’t learn effectively.”
Other common causes of failure include legacy technologies, poor cloud strategies, and the complexity of stacking tools instead of simplifying them.
“Without modern, unified infrastructure, AI cannot scale,” said DDN CEO Alex Bouzari, urging companies to chase models and GPUs instead of focusing on the “data layer underneath.”
All this against the background of increased pressure from stakeholders and regulators. Most (93%) are now actively trying to reduce AI energy impact, with around half (47%) citing power and cooling as the biggest inefficiencies. “Tokens per watt” is therefore emerging as a new performance metric for AI efficiency.
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