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The Information Supercycle
How the path to AI scale runs through your data, technology, and architecture.
Report overview
When your AI problem isn't AI. It's your data.
AI models are advancing fast. But scaling AI across the enterprise requires something more fundamental: a high-quality, governed data estate and the infrastructure to support it. For many organizations, both are constrained by legacy complexity, trapped information, and limited infrastructure capacity.
The cost of dark data
As much as a third of enterprise data can be considered ROT.
Another 52% is dark data with unknown value, at least a portion of which is almost certainly more ROT.1
Download the report >>From funding AI to scaling it across the enterprise.
From funding AI to scaling it across the enterprise.
Make your information and infrastructure ready to scale AI.
Iron Mountain is the only company that can help organizations reduce legacy complexity, unlock the value of enterprise information, and build the infrastructure needed to move AI from pilots to enterprise scale.
61%
have trapped data.
From ROT to ROI: Transforming data debt into AI fuel.
Only 39% of enterprises have digitized and structured data, leaving 61% with trapped data that's not AI-ready.2 You need to unify authority across physical records, digital and governance lifecycles.
Explore Chapter 1 >>17%
fund through reclaimed spend.
Capitalizing on legacy assets to fund the AI future.
Only 17% of enterprises are currently funding AI through reclaimed IT spend.2 You can reclaim value from legacy IT and data to fund the AI of tomorrow.
Explore Chapter 2 >>70%
can't track data in their AI models.
The activation gap: Turning dark assets into AI advantage.
70% of survey respondents say their organization cannot effectively track the lifecycle of data used in their AI models.2 It's time to turn dark data into queryable AI-ready intelligence.
Explore Chapter 3 >>80%
of IT load capacity.
AI ambitions are hitting an infrastructure wall.
In 2026, inference overtakes training, and by 2030 AI inference is expected to account for 80% of total IT load capacity.3 We'll help you optimize infrastructure and treat clean data as green data
Explore Chapter 4 >>
This is a once-in-a-generation shift. Here’s how leaders can meet the moment:
Self-funding AI through liquidation
By aggressively liquidating data debt and reclaiming value from retired hardware, organizations can redirect significant budgets previously wasted on maintenance towards new AI investments. Furthermore, by establishing a verified lineage across this unified data estate, leadership ensures that AI outputs are built on a foundation of clean, high-fidelity information, making the resulting autonomous decisions fundamentally responsible, unbiased, and fully traceable for audit and governance.
Unifying the intelligence core
By digitizing and consolidating physical and digital records into a singular, queryable system, organizations can overcome dark data challenges and provide employees with high-speed, responsible intelligence. A new unified system of intelligence turns static archives into a dynamic, compliant fuel for AI across every workflow—ensuring that autonomous decisions are grounded in a comprehensive, verified history. Organizations can then move forward with a new level of confidence, empowering employees with trustworthy insights, and reassure customers that their data is being managed with the highest standards of integrity and purpose.
Securing IT assets and infrastructure responsibly
With growing concerns about the energy demands of AI, the most mature organizations are linking data minimization and IT infrastructure to carbon reduction. By responsibly purging dark data and legacy IT assets and infrastructure, organizations free up the power and budget required for AI compute. Prioritizing sustainability within each go-forward investment also serves as a critical "permission to operate" in a landscape where environmental accountability is as essential to the brand as its technological capabilities.
Aligning authority and governance
Fragmentation is the enemy of AI at scale. Creating an AI information architecture requires a restructuring of how the C-suite views capital and information. Organizations must move beyond departmentalized spending and By elevating data strategy and governance, IT asset lifecycles, and forward-looking infrastructure into a singular high-stakes conversation aligned to their AI roadmaps, organizations can move beyond departmentalized spending. Extending planning cycles ensures that AI is not treated as a modular expense, but as a core asset. When authority is unified, the budget follows, and teams can move forward under a single vision with confidence.
In the last 12 months, 86% of the companies surveyed have had AI projects disrupted by bad data.2
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See how Iron Mountain’s offerings can help you
go from AI pilot to enterprise scale.
Sources
1 Simon Jelley, “How to keep your data from ROT-ing in the cloud,” Forbes Business Council, Forbes, January 12, 2023.
2 Iron Mountain, “The information supercycle: Aligning data, infrastructure, and capital for AI scale,” October 2026.
3 Structure Research and Iron Mountain Data Centers, “Will AI eat the cloud?” November 2025.
Sources reviewed October 2026.