Enterprise AI Masters thrive with focus on data & security
NetApp has released its latest global Enterprise AI maturity study, conducted by IDC, highlighting significant changes in enterprise attitudes towards artificial intelligence amid a rising focus on data readiness, security, and infrastructure.
The 2025 iteration of the study indicates a shift from conceptual experimentation to practical business value and return on investment, with large enterprises emerging as leaders in building AI-ready infrastructure compared to their peers.
Changing priorities
The 2024 study had pointed out AI's potential for substantial business outcomes, as well as shortcomings related to infrastructure, governance, and workforce skills. The 2025 findings show that organisations are now more concerned with optimising investments, addressing fragmented AI adoption, and ensuring data governance and security are integrated into the early stages of project development.
"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."
Business outcomes and maturity
The report identifies organisations that have adopted advanced approaches to AI infrastructure, data governance, and security as 'AI Masters'. These AI Masters experienced significantly better results than less mature organisations, achieving a 24.1% increase in revenue and a 25.4% improvement in cost savings. The study underscores that enterprises with higher AI maturity consistently outperform their counterparts on all measured business outcomes.
The survey also revealed that while the need for a complete overhaul of storage infrastructure is declining, with only 37% of firms reporting such a requirement in 2025 compared to 63% in 2024, substantial optimisation gaps remain. A significant 84% of companies say their storage systems are still not fully optimised for AI operations.
Security investments
Security is now taking precedence as organisations scale AI efforts. Of those identified as AI Masters, 62% reported increasing security budgets for AI initiatives over the past year, compared to 16% of less mature businesses. Enhanced security spending reflects elevated awareness of the risks associated with the broader rollout and integration of AI functionalities.
Broader AI adoption
The study distinguishes between organisations employing generative AI in isolated applications and those scaling agentic AI solutions, which require cohesive enterprise-wide systems. Enterprises identified as AI Masters, according to the report, possess more robust foundations in data quality, security, and infrastructure, facilitating more comprehensive and scalable AI deployment. Less mature organisations are more likely to have siloed generative AI use cases, which may hinder broader adoption of more advanced AI models.
According to the report, achieving responsible, organisation-wide AI scaling demands trustworthy and modern data infrastructures instead of experimental or ad hoc deployments.
"Enterprises that modernize their data pipelines, governance frameworks, security approaches, and storage architectures are the ones turning AI pilots into production-grade applications that deliver the highest measurable business outcomes," said Dave Pearson, IDC Research Vice President, Infrastructure Solutions.
Data practices and infrastructure
One of the central findings of the combined 2024 and 2025 studies is that sustainable business impacts from AI depend more on underlying data practices and systems architecture than on initial KPI improvements alone. AI Masters have shifted from sporadic improvements towards consolidating architectures that are cloud-smart, scalable, data-aware, adaptive, and automated.
Speed, scalability, security, and adaptability are highlighted as foundational elements, not optional add-ons, for organisations seeking to maximise business value from their AI projects.
Survey scope and methodology
The research was conducted through surveys with over 1,200 global decision makers in enterprise IT, data science, data engineering, and software development whose responsibilities include AI initiatives. These interviews offered insights into evolving challenges, perceived benefits, and best practices for adopting and scaling AI. The study categorises organisations by maturity: AI Emergents, AI Pioneers, AI Leaders, and AI Masters, using a model that assesses approaches to infrastructure, policy, governance, resource efficiency, and collaboration.
The study's findings continue to contribute to the ongoing discussion about how enterprises can best realise value from AI-driven transformation by investing in modern data infrastructure and governance frameworks.
