- Half of companies have canceled AI projects due to poor infrastructure
- 97% agree that the cloud is key to simplification and efficiency.
- Companies are also under pressure from a sustainability point of view.
Artificial intelligence is not proving to be the golden key for many companies: two in three (65%) admit that their AI environments are too complex to manage and more than half (54%) have canceled AI projects in the last two years due to infrastructure problems.
And infrastructure, according to DDN’s latest State of AI Infrastructure Report, is exactly what’s holding businesses back, quickly followed by energy.
Looking ahead, 97% agree that the cloud is essential for scaling AI initiatives, and hybrid AI workloads are expected to grow 162% in the next 12 months.
AI is based on good infrastructure foundations
The DDN report reveals the important role that third parties play: 72% rely on external experts and only 12% rely solely on internal teams. This is notable, because 83% agree that teams are struggling today and 98% admit shortcomings in AI skills, further underscoring the need for outside help.
The study also found that most failures can be traced back to silos, whether in storage, compute, or data pipelines. “Enterprises are discovering that scaling AI is not a computing problem, but an integration problem,” wrote DDN CTO Sven Oehme. “If your infrastructure is not unified, your AI cannot learn efficiently.”
Other common reasons for failure include legacy technologies, poor cloud strategies, and the complexity of stacking tools instead of simplifying them.
“Without a modern, unified infrastructure, AI cannot scale,” said DDN CEO Alex Bouzari, criticizing companies for chasing models and GPUs instead of focusing on “the underlying data layer.”
All this against a backdrop of increased pressure from stakeholders and regulators. The majority (93%) are now actively trying to reduce the energy impact of AI, with around half (47%) citing power and cooling as the main inefficiencies. Therefore, “tokens per watt” is emerging as a new performance metric for AI efficiency.
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