Data readiness & infrastructure drive enterprise AI success
A new global study conducted by IDC and NetApp has underscored the importance of data readiness and infrastructure in realising the benefits of artificial intelligence within enterprises.
The annual Enterprise AI maturity study focused on how organisations worldwide are shifting their approach to artificial intelligence, with an emphasis on the business impact of mature data and infrastructure strategies. The research segmented companies based on their AI maturity, identifying a distinct group known as "AI Masters"-those investing in advanced infrastructure, data governance, and security measures for AI initiatives.
Business impact
The study's findings revealed that AI Masters achieve measurably higher business outcomes from AI implementation. According to the results, organisations in this category have realised a 24.1% improvement in revenue and a 25.4% increase in cost savings compared to less mature peers. These outcomes were consistently higher across all measured AI business metrics.
The research reviewed over 1,200 global decision makers involved in enterprise IT, data science, data engineering, and software development projects related to AI. The results highlighted that the most mature organisations are distinguished by their focus on ensuring data quality, embedding robust governance frameworks, and developing scalable, cloud-smart architectures.
"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."
Infrastructure challenges
Despite progress, infrastructure remains a significant hurdle for many organisations. While the percentage of firms reporting that their storage required a major overhaul decreased from 63% in 2024 to 37% in 2025, a considerable 84% of surveyed enterprises stated their storage systems are still not fully optimised for AI workloads.
This infrastructure gap was particularly noticeable among organisations that have not yet reached the most advanced levels of AI maturity. The research noted that advanced adopters have begun moving beyond incremental upgrades towards architectures that are described as cloud-smart, scalable, data-aware, adaptive, and automated.
Security prioritisation
Security considerations have become central to AI investment planning. The research found that 62% of AI Masters increased their security budgets for AI initiatives over the past year. By contrast, only 16% of less mature organisations reported similar increases, highlighting a significant disparity in risk and data protection priorities.
The study also observed that with the advent of more autonomous forms of AI-referred to as Agentic AI-those with strong data, security, and infrastructure foundations are better positioned to expand adoption across the enterprise. Less mature organisations were found to be mostly focused on preliminary generative AI pilots, a strategy that is not expected to scale effectively to broader, enterprise-wide deployments.
Foundational investments
According to IDC's maturity model, the organisations furthest ahead-termed AI Masters-are characterised by their shift to more holistic upgrades, focusing on interconnected data policy, architecture and governance. These structural investments underpin the ability to shift AI from isolated initiatives to enterprise-scale, production-grade applications.
"Enterprises that modernise 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.
The results from both the 2024 and 2025 surveys underpin the critical connection between architecture, data practices, and demonstrable AI business value. IDC's analysis suggests that while some organisations can deliver short-term gains from individual AI initiatives, the most sustainable and significant business impact arises from robust data quality controls and substantial infrastructure development.
The research indicates that in the present AI landscape, speed, scale, security, and adaptability are considered essential for extracting business value from enterprise AI deployment. The findings emphasise that mature data and technology strategies set the baseline for scaling AI effectively and ensuring that value is generated across all business functions.
