Research Finds Data Readiness and Infrastructure as Critical to Success in the AI Era

NetApp (NASDAQ: NTAP), the intelligent data infrastructure company, today unveiled a new global Enterprise AI maturity study conducted by IDC*. This second annual study reveals a sharp shift in how enterprises approach artificial intelligence and confirms AI has entered a new phase where the most mature organizations prioritize data readiness, protection and security while they make impactful infrastructure decisions and investments to support current and next-gen AI.

Findings from 2024 highlighted AI’s potential to drive dramatic business outcomes, but also exposed deep gaps in infrastructure, governance, and skills. In 2025, the conversation has evolved: organizations are now confronting ROI pressures, fragmented adoption, and the need to embed data governance and security from the start.

“AI is no longer about proof of concept—it’s about proof of value,” said Syam Nair, Chief Product Officer at NetApp. “IDC’s latest research shows that the real differentiators are data preparedness and infrastructure: the companies focusing on data quality and building modern, cloud-smart, scalable, and adaptive architectures are the ones turning AI into true business impact. That’s why NetApp believes every organization needs an Intelligent Data Infrastructure to succeed in the AI era.”

Key findings from the 2025 study include:

• Maturity pays off. AI Masters (organizations pursuing the most advanced AI infrastructure, data governance and security approaches) consistently outperform their less mature peers across every AI business outcome: Masters achieved 24.1% revenue improvement and 25.4% improvement in cost savings, far outpacing less mature peers.
• Infrastructure remains a bottleneck. While the percentage of firms reporting that their storage required a major overhaul dropped from 63% in 2024 to 37% in 2025, 84% still say their storage is not fully optimized for AI.
• Security is now front and center. 62% of AI Masters increased security budgets for AI initiatives in the past year, compared to just 16% of less mature organizations.
• Agentic AI favors the Masters. With stronger data, security, and infrastructure foundations, AI Masters are already ahead. Less mature peers remain siloed in GenAI adoption, an approach that will not scale to Agentic AI’s enterprise-wide demands.

Together, these findings highlight a single point: scaling AI responsibly requires more than experimentation – it demands a trusted, modern, and intelligent data infrastructure.