Businesses are struggling to achieve AI success - this report reveals why

• Half of businesses have cancelled AI projects due to poor infrastructure
• 97% agree that cloud holds to key to simplification and efficiency
• Enterprises are also under pressure from a sustainability angle

Artificial intelligence isn't proving to be the golden key for many businesses, with two in three (65%) admitting their AI environments are too complex to manage and more than half (54%) have cancelled 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 businesses back, quickly followed by energy.

Looking ahead, 97% agree cloud is essential to scaling AI initiatives, with hybrid AI workloads expected to grow 162% over the next 12 months.

AI relies on good infrastructure foundations

DDN's report reveals the considerable role that third parties play, with 72% relying on external expertise and only 12% depending solely on in-house teams. This is notable, because 83% agree that teams are struggling today and 98% admit to AI skills gaps, further underscoring the need for outside help.

The study also found that most failures can be traced back to silos, either in storage, compute or data pipelines. "Enterprises are discovering that scaling AI isn’t a compute problem – it’s an integration problem," DDN CTO Sven Oehme wrote. "If your infrastructure isn’t unified, your AI can’t learn efficiently."

Other common reasons for failure include legacy technologies, poor cloud strategies, and the complexity of stacking tools instead of simplifying them.

"Without modern, unified infrastructure, AI can’t scale," DDN CEO Alex Bouzari said, slating companies for chasing models and GPUs instead of focusing on "the data layer underneath."

All of this against a backdrop 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 top inefficiencies. "Tokens per watt" is therefore emerging as a new performance metric for AI efficiency.

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