The AI Readiness Illusion: 86% of Organizations Say Data Concerns Delay or Derail AI Projects
Bangalore, October 2026 : New Iron Mountain research finds businesses are pushing towards greater AI autonomy without the trusted, well-governed information needed to scale
- 4 in 10 organizations 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 organizations cite information spread across disconnected systems or departmental silos, while 53% point to legacy formats or physical archives.
- 70% of organizations say they cannot effectively track the lifecycle (origin or journey) of the data used in their AI models.
New research from Iron Mountain (NYSE: IRM), a global leader in information management services, 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 organizations 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 organizations 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 organizations 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 organizations 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. Organizations 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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Read Iron Mountain’s new report: The Information Supercycle: Aligning Data, Infrastructure and Capital for AI Scale
About Iron Mountain
Iron Mountain Incorporated (NYSE: IRM) partners with more than 240,000 customers across 61 countries, including approximately 95% of the Fortune 1000. We serve as the trusted guardians of what matters most, helping unlock what’s possible. By connecting physical, digital, and intelligent workflows, our lifecycle solutions help organizations realize enterprise value at scale. From complex challenges across information management and digital transformation to data security, data centers, and asset lifecycle management, we empower organizations to navigate an ever-changing landscape to prepare for tomorrow’s possibilities. Our longstanding commitment to safety, security, sustainability, and continuous innovation defines everything we do.
To learn more about Iron Mountain, please visit www.IronMountain.com.