Nasuni Research Finds 97% of Enterprises Are Adopting AI Agents, Yet Most Projects Fail to Meet Objectives
Nasuni, a platform specializing in unstructured data for enterprise teams and artificial intelligence (AI), has released findings from its annual research report, The State of Enterprise File Data Annual Report 2026. The study indicates a significant gap between AI adoption and successful outcomes. While 97% of organizations are either deploying or piloting AI agents, 57% report that their AI projects are falling short of their objectives. A key issue identified is data-related challenges, as 94% of enterprises struggle to manage unstructured data, which represents the majority of their data assets.
Currently, only 16% of companies prioritize unstructured data management as a critical information technology investment. However, 60% plan to increase their investment in this area over the next 18 months, signifying a growing awareness of the importance of proprietary operational data in achieving effective AI outcomes.
Sam King, Chief Executive Officer at Nasuni, remarked, “Enterprises are moving fast on AI projects, but most aren't getting the results they want. What this report makes clear is that AI success depends on how well you manage and prepare your data." He noted that many organizations still employ outdated methods for managing unstructured data, which hinders their ability to maximize its value. King emphasized the need for accessible data to support both team operations and AI initiatives.
The report highlights several challenges that organizations face in scaling AI and upgrading their data infrastructure. Key barriers include data security concerns (43%), integration issues (36%), and trust in data (33%). As a result, only 43% of AI projects achieve their intended goals. Additionally, nearly half of the organizations reported that AI initiatives have exposed data quality and governance issues, with 79% facing inconsistent file access and performance across different locations, complicating efforts to scale AI.
The research also sheds light on rising hardware costs, with 62% of organizations anticipating increases in hardware expenses due to surging prices of essential components. This adds pressure on IT budgets as enterprises work to adopt AI and modernize their infrastructure, leading to additional strains on systems designed to handle more data-intensive workloads. Despite accelerated AI adoption, organizations may have overestimated their preparedness for advanced applications, highlighting pressing gaps in data access, governance, and recovery.