NetApp and IDC’s 2025 global Enterprise AI maturity study reveals that enterprise AI adoption has shifted from early experimentation to a focus on proof of value, underpinned by data readiness, security, and scalable infrastructure investments. The report makes clear that only the most mature organizations—called “AI Masters”—are translating AI initiatives into superior and sustainable business outcomes, outpacing their less mature peers across revenue, cost savings, and operational agility.
AI Maturity Levels and Their Impact
IDC’s maturity model divides organizations into four levels: AI Emergents, AI Pioneers, AI Leaders, and AI Masters. Just 13% of surveyed enterprises qualified as “Masters,” but this group reported the highest aggregate business impact from AI—averaging 24.1% revenue growth and 25.4% cost savings versus only 15.8% and 15.9%, respectively, for the least mature organizations.
Masters have moved beyond fragmented AI adoption. They prioritize unified data architectures, robust governance, and integrated security. Their focus on foundational data quality and infrastructure agility enables company-wide adoption of advanced AI (particularly agentic AI), while less mature peers remain siloed or stalled at isolated GenAI use cases.
Infrastructure and Storage: Still a Challenge
Although storage bottlenecks are easing, 84% of firms admit their storage is not fully optimized for AI workloads, even as those reporting a need for a total overhaul dropped from 63% in 2024 to 37% in 2025. Masters lead in demanding next-gen storage capabilities—such as “store once, use anywhere” access and advanced ransomware protection.
Masters have also committed to making storage infrastructures adaptive (cloud-smart and automated) and focus intently on the movement and integration of data between environments—a critical requirement for scaling complex AI.
Security and Governance Take Center Stage
Security investment has intensified, particularly among AI Masters: 62% increased security budgets for AI initiatives in the past year, compared to just 16% of less mature organizations. Data governance, compliance, and privacy remain ongoing concerns, and while Emergents often perceive themselves as “done” with these challenges, IDC finds that real-world operational maturity brings deeper awareness of persisting gaps and regulatory complexity.
The shift to agentic AI—where systems operate autonomously and adaptively—raises the stakes for integrated security and continuous oversight across enterprise workflows.
Productivity and Skills
AI Masters are realistic about their remaining challenges: major improvements are still needed in pipeline tooling, automation, and access to clean, sensitive data to maximize the productivity of data scientists and developers. However, the skills gap is closing in 2025, as automated workflows and improved data toolchains offer fresh opportunities for efficiency gains.
Key Takeaways for Enterprises
Enterprises making the largest, most measurable business gains from AI have modernized their data pipelines, security frameworks, and storage architectures.
GenAI is driving urgency for more robust security, data governance, and flexible architectures, but only mature organizations are well-positioned to scale further into agentic AI.
While storage optimization has improved, most companies’ infrastructures remain a barrier to full-scale, enterprise-wide AI.
Sustainable, transformational business impact hinges on investment in intelligent data infrastructure, not one-off, piecemeal upgrades.This IDC study highlights a pivotal moment where the gap between AI proof-of-concept and real business value depends on the sophistication of data practices and enterprise architecture. As AI use cases become more complex and autonomous (agentic), the organizations embracing holistic data readiness, modern storage, and embedded security are setting the pace in the AI.
