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Breaking through the data ceiling: How content intelligence accelerates business value
Whitepaper
New primary research from Iron Mountain tracks how enterprise leaders are modernizing fragmented data estates to build content intelligence into their operations and unlocking agentic AI.
Executive summary
Every enterprise IT leader has heard the same promise: artificial intelligence will transform decision-making, unlock productivity, and deliver revenue growth. Yet for most enterprises, high-value AI deployment remains stalled in the pilot phase. The reason is rarely the large language model (LLM)—more often than not, it’s the fragmented content feeding it.
New primary research from Iron Mountain tracks how enterprise leaders are modernizing fragmented data estates to build content intelligence into their operations. Benchmarking organizations across three distinct archetypes—Custodians, Connectors, and Accelerators—the study exposes a widening divide: market leaders (Accelerators) are pulling ahead of their peers (Custodians and Connectors), leveraging a more mature content intelligence foundation to unlock faster decision speed, AI readiness, and measurable ROI.
- Custodians (low content intelligence maturity - 17% of organizations surveyed): This group is characterized by manual processes, siloed data, and legacy technology. Because their data remains fragmented, their estate creates AI agent blind spots that stall adoption at the pilot stage.}
- Connectors (moderate content intelligence maturity - 75% of organizations surveyed): For these organizations, data and technology modernization is underway, but plagued by AI pilot fatigue, they are experiencing some gains or business impact. They can support basic AI use cases, but disconnected data, systems, and governance prevent scaling autonomous agents enterprise-wide.
- Accelerators (high content intelligence maturity - 8% of organizations surveyed): These are our market leaders who are furthest ahead in AI innovation, leveraging higher rates of modern cloud architecture and automated workflows to deliver enterprise insights and measurable business performance. By building an AI-ready content foundation, they have established an active trust layer required to scale specialized AI agents.
The divide between these groups is structural. Accelerators are not just managing content better; they are re-architecting how information is governed and fed directly into critical business workflows.
By investing in a consolidated information layer, Accelerators are breaking through the enterprise data ceiling to scale AI successfully and deliver higher revenue growth than their counterparts.
This paper details several key differences between these groups, exposing how data fragmentation levies a tax on enterprise capital, resources, and AI investments. It also reveals how Accelerators are breaking free from legacy drag to unlock higher performance and value.
The productivity tax silently draining the ROI from AI
While executives typically look for technical barriers to AI, a bigger bottleneck is often hidden in plain sight; it is the operational friction—such as manual wrangling, missing metadata, and legacy search hurdles—caused by a disconnected content estate. AI pilots that begin with promise often hit a wall when legacy systems fail to deliver clean, trusted, accessible data. As a result, internal teams get trapped in manual data work, stalling project momentum.
Across all IT leaders surveyed, 84% agree that highly skilled IT and data science personnel are burning hours acting as manual data wranglers instead of executing strategic work.
This disparity sharpens as you focus more deeply across the groups studied.
What is content intelligence?
For most enterprises, meaningful business information remains trapped inside siloed, disconnected legacy content. With years, or even decades of records locked inside non-digitized or unstructured files, businesses are stalled in activating that information for advantage.
How does it drive ROI?
Content intelligence maturity directly impacts decision-making and productivity and correlates with top-line growth. Our survey reveals that Accelerators (leaders with more mature content intelligence) are 53% more likely to scale AI beyond pilots and 36% more likely to report higher revenue growth than lower-maturity peers.