Nasuni’s The State of Enterprise File Data Annual Report 2026 Finds Enterprise AI Adoption Is Outpacing Data Readiness

Nasuni’s

newly released

The State of Enterprise File Data Annual Report 2026

paints a picture of an

enterprise world racing aggressively toward AI adoption

while simultaneously discovering that most existing data infrastructure was never designed for the scale, complexity, and operational demands that modern AI systems require.

The report, based on a survey of 1,000 enterprise purchasing decision-makers across the United States, United Kingdom, France, Germany, Austria, and Switzerland, suggests that the next phase of enterprise AI competition may depend less on model access and more on how organizations manage unstructured operational data.

AI Adoption Is Moving Faster Than Enterprise Readiness

The findings show that AI has become the top IT investment priority for enterprises in 2026. Fifty-nine percent of respondents identified AI initiatives as their leading area of investment, representing a notable increase from the previous year.

At the same time, enterprises are increasingly recognizing that

AI deployment

cannot be separated from broader data management modernization efforts. Cloud data management, data intelligence, analytics, and unstructured data management all emerged as major investment priorities. Seventy-seven percent of respondents said they plan to increase investment in data intelligence and analytics capabilities, while 60% said they expect to increase spending on unstructured data management over the next 18 months.

The report suggests that many organizations underestimated how dependent AI systems would become on clean, accessible, well-governed enterprise data. Nearly half of organizations said AI initiatives had already exposed gaps in data quality or governance. The more advanced a company’s AI deployment became, the more likely it was to discover serious data issues.

Nasuni’s research also indicates that enterprises may still be in the early stages of understanding what large-scale agentic AI deployment actually requires. Although 97% of organizations report some level of AI agent deployment or testing, only 18% have achieved enterprise-wide deployment of AI agents.

Unstructured Data Has Become a Major Enterprise Bottleneck

One of the clearest themes throughout the report is the growing importance of

unstructured data

. Documents, emails, images, recordings, design files, engineering data, and collaboration assets now account for more than 90% of organizational data.

Yet despite the critical role this data plays in enterprise operations and AI workflows, 94% of surveyed organizations said they struggle to manage unstructured data effectively. Security concerns ranked as the largest challenge, followed by disaster recovery difficulties, collaboration issues, fragmented environments, and compliance complexity.

The report repeatedly highlights fragmentation as a central operational problem. Organizations currently rely on an average of four separate systems for storage, backup, and disaster recovery, while 22% report using more than six vendors simultaneously.

According to the findings, businesses using multiple disconnected systems experienced longer recovery times, greater operational pressure, and more difficulty scaling AI initiatives.

Nasuni also found that only 21% of enterprises currently operate a centrally managed file environment capable of delivering consistent performance across locations. The remaining organizations rely on varying combinations of fragmented systems, manual transfers, email-based sharing, or inconsistent centralized infrastructure.

That inconsistency appears to have direct productivity consequences. More than one-third of businesses said slow or inconsistent file access significantly harms employee productivity.

Rising AI Infrastructure Costs Are Reshaping IT Spending

The report also arrives during a period of rapidly increasing infrastructure costs tied to AI expansion. Forty-two percent of organizations expect significant increases in AI tooling and generative AI platform spending over the next year.

Nasuni notes that infrastructure hardware prices are also rising sharply, particularly memory and storage components. The report references projections showing combined DRAM and SSD pricing could rise by as much as 130% by the end of 2026.

This is creating tension inside enterprise IT budgets. Forty-six percent of respondents said increasing data growth is forcing higher storage infrastructure spending, while 43% reported direct budget trade-offs between storage infrastructure and AI initiatives.

The findings suggest many enterprises are beginning to realize that AI expansion is not simply a software problem. Large-scale AI deployment introduces major operational demands related to storage performance, governance, security, backup, disaster recovery, and cross-location data access.

Cybersecurity and Recovery Weaknesses Remain Significant

Cybersecurity and operational resilience emerged as another major concern throughout the report.

Seventy-one percent of organizations reported experiencing a cyberattack over the past year, an increase from 69% in the previous survey. Yet only 26% said they were able to easily detect, mitigate, and recover from those attacks.

Recovery times were particularly concerning. According to the findings, 70% of organizations required more than a week to fully recover from a cyberattack, with the average recovery period stretching to just over four weeks.

The report also found that 62% of organizations still rely primarily on traditional backup-based recovery systems rather than continuously protected or immutable data environments. Nasuni argues that these older approaches may be poorly suited for increasingly data-intensive AI environments where downtime and operational interruptions can become significantly more expensive.

Interestingly, organizations with more mature centralized data infrastructure appeared to recover substantially faster from cyberattacks. Firms using centralized or continuously protected data systems were more likely to restore operations quickly and also tended to report more advanced AI deployment maturity.

The report highlights architecture, engineering, and construction firms as some of the hardest hit sectors, with 82% of surveyed AEC organizations reporting cyberattacks over the previous year. Manufacturing and automotive companies also reported elevated attack rates, reinforcing concerns that operational industries with valuable intellectual property and critical infrastructure are becoming increasingly attractive targets for cybercriminals.

At the same time, the findings suggest many enterprises may be overestimating their recovery capabilities. While only 38% of organizations reported having centrally managed, immutable, or continuously protected data systems designed for rapid recovery, two-thirds of respondents still expressed confidence in their ability to recover critical unstructured data after a major incident.

AI Governance Is Becoming a Boardroom Issue

One of the more notable organizational shifts identified in the report involves decision-making authority around AI initiatives.

For the first time, the C-suite has overtaken IT departments as the primary decision-maker for enterprise AI strategy. Fifty-two percent of organizations said AI decisions are now primarily driven by executives such as CEOs, CTOs, CDOs, and CAIOs, compared to just 26% led primarily by IT departments.

Nasuni suggests this reflects the growing strategic importance of AI as organizations move beyond experimentation into operational deployment. AI is increasingly tied to broader business transformation efforts involving workforce structure, operational workflows, product strategy, and long-term competitiveness.

However, the report also points to a growing disconnect between executive-level AI ambitions and the underlying infrastructure realities faced by IT teams. While 70% of respondents believe their file data infrastructure can support AI scaling, the report repeatedly highlights persistent issues involving fragmented storage, governance gaps, inconsistent access, and weak recovery systems.

That disconnect may become more visible as enterprises transition from lightweight generative AI tools toward more autonomous AI agents capable of executing operational tasks across enterprise systems.

The Next AI Race May Be About Data Infrastructure

Although much of the public AI conversation still focuses on foundation models, benchmarks, and chatbot capabilities, Nasuni’s findings point toward a quieter but potentially more consequential shift happening inside enterprise technology stacks.

The report suggests that future AI success may increasingly depend on operational file infrastructure rather than model access alone. Organizations with fragmented storage systems, inconsistent collaboration environments, weak governance, and outdated recovery strategies may struggle to deploy AI reliably at enterprise scale even if they have access to the latest models.

The report also hints at a broader transformation in how enterprises think about proprietary data. Operational file data — including engineering files, internal documentation, collaboration records, images, recordings, and workflow artifacts — is increasingly being treated as a strategic asset capable of powering AI systems with company-specific context and institutional knowledge.

At the same time, the report warns that scaling AI without trusted, centralized, and governed data environments could amplify security risks, operational inefficiencies, and organizational complexity.

Nasuni’s

The State of Enterprise File Data Annual Report 2026

ultimately frames enterprise AI adoption not as a standalone software revolution, but as a deeper infrastructure transition that may require organizations to fundamentally rethink how they store, govern, secure, and operationalize their data.