The research reveals a widening gap between AI adoption and outcomes: 97% of organizations have deployed or are piloting AI agents, yet 57% of AI projects are not reported to be delivering their objectives. This shortfall is largely driven by data-related challenges, with nearly all enterprises (94%) struggling to manage unstructured data, which comprises the majority of their data footprint. While only 16% currently prioritize unstructured data management as a core IT investment, 60% plan to invest over the next 18 months, reflecting growing recognition of the role proprietary, operational data plays in driving desirable business outcomes with AI.
“Enterprises are moving fast on AI projects, but most aren’t getting the results they want,” said Sam King, Chief Executive Officer at Nasuni. “What this report makes clear is that AI success depends on how well you manage and prepare your data. Too many organizations are still relying on outdated approaches to unstructured data management, limiting their ability to unlock its full value. Your proprietary, operational data is your greatest asset, but only if it’s accessible and ready for your teams and the AI that supports them. At a time of rising hardware costs, supply risk, and growing complexity, getting your data house in order is the ultimate no-regrets move — and it’s exactly where we’ve been investing to help customers turn AI ambition into AI results.”
The findings point to several critical challenges and shifts organizations must address as they work to scale AI and modernize their data infrastructure, including:
· AI is exposing data gaps as inconsistent access slows scale: Nearly half (46%) of organizations say AI initiatives have revealed issues with data quality and governance, while 79% report inconsistent file access and performance across locations, contributing to widespread challenges scaling AI.
· Turning AI into ROI at scale remains a challenge: 90% of organizations report barriers to scaling AI — driven by data security concerns (43%), integration roadblocks (36%), and lack of trust in data (33%) — contributing to just 43% of projects successfully delivering on their objectives.
· Agentic AI adoption is outpacing readiness: While nearly all organizations are piloting AI agents, only 18% have deployed them at scale, underscoring a disconnect between ambition and scaling.
· Rising hardware costs are adding new pressure to IT budgets: 62% of organizations expect hardware costs to increase as key components like DRAM surge creating new challenges as enterprises scale AI and modernize infrastructure. As AI adoption accelerates, these rising costs are compounding the strain on infrastructure needed to support more data-intensive workloads.
Organizational AI adoption is accelerating, but the findings suggest many enterprises have overestimated their readiness for more advanced use cases, with gaps in data access, governance, and recovery becoming increasingly difficult to ignore.
These challenges span industries. In AEC, 66% of firms cite security as their top unstructured data concern; manufacturers continue to face elevated cyber risk and longer recovery times; and energy, oil, and gas organizations remain split on whether AI decision-making should sit with the C-suite or IT, creating misalignment around objectives. As AI and agentic systems evolve, these gaps will only widen, making it critical for organizations to modernize their data foundations to support what comes next.
