Breaking through the data ceiling: How content intelligence accelerates business value

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 minimal 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.