New research from Iron Mountain reveals a growing gap between AI ambition and readiness as businesses move towards more autonomous AI.
Published in Iron Mountain’s new report, The Information Supercycle: Aligning Data, Infrastructure and Capital for AI Scale, the findings show that while 95% of organisations say using data to improve business outcomes is a top priority, 86% have experienced AI project delays or cancellations because of data concerns in the past 12 months.
The scale of work still required to make information usable for AI remains significant. 4 in 10 organisations spend at least 41% of their AI project timelines on manual data preparation, while only 39% fall into the report’s ‘digitized and structured’ category, where dark data is centrally managed, indexed and readily usable for analytics and AI.
Fragmented and legacy information remains a major barrier. 56% of organisations cite information spread across disconnected systems or departmental silos, while 53% point to legacy formats or physical archives.
Those gaps become more consequential as businesses move beyond generative AI towards agentic systems capable of acting across workflows. 70% of organisations say they cannot effectively track the lifecycle (origin or journey) of the data used in their AI models.
Varun Gupta, Senior Vice President, Global Product & Solutions Marketing at Iron Mountain, said: “AI readiness is about much more than deploying the latest technology. Enterprises need to know that the information powering their AI is accessible, current and governed, with clear ownership and permissions. As AI agents move from generating answers to taking actions across business workflows, these foundations become essential to scaling AI with confidence.”
As AI begins to make and act on more decisions, the consequences extend beyond poor outputs. Fragmented information can create security exposure, compliance and audit risk, higher discovery costs and loss of institutional knowledge. Organisations need continuous lifecycle governance that gives them a clear, auditable view of information from creation to destruction, helping verify the data activated for AI is current, compliant and trusted.
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