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Intelligence demands AI-ready data

When teams can’t read what’s in a legacy silo, they have to apply broad, restrictive policies that often block AI agents from accessing that information. You can’t build smart AI on top of messy data silos.

“The ultimate cost of poor data management isn’t just a failed AI tool—it’s the total erosion of trust. When insights cannot be relied upon, organizations retreat to defensive, cautious decision-making.”

— AI leader, Banking1

Your AI is only as good as your records

Before AI, software was often built through a lens of extreme specialization, where systems like Enterprise Content Management (ECM) and Intelligent Document Processing (IDP) evolved as isolated, functional silos. ECM was designed as a passive filing cabinet, focused on long-term storage and compliance, while IDP functioned as an extraction engine meant to solve the data entry challenges. Today, this translates to added complexity for knowledge workers and a chasm where intelligence is easily lost or prone to manual error.

Companies spend significant project time on manual data preparation:

  • 2:10 are dedicating 60% of their project timeline on 
manual data prep
  • 4:10 are dedicating 41–60%
  • 3:10 are dedicating 21–60%
  • 1:10 are dedicating 1-20%

Gartner’s 2024 Digital Worker Survey found that 34% of digital workers struggle to find the information needed to effectively perform their jobs at least half of the time.2

Building resilience through continuous compliance

Knowing exactly what data you have is the best and cheapest way to protect your business. But most companies are operating under an illusion of AI readiness. Without strict compliance, enterprises expose themselves to hidden costs through:

Potential data exposure

You can’t protect what you can’t see. When sensitive information—from Personally Identifiable Information (PII) to proprietary IP—leaks into unstructured formats like email attachments or unindexed legacy PDFs, it creates an invisible attack surface bypassing standard security scanners and leaving breadcrumbs for attackers.

Regulatory drift and the AI blind spot

You can’t protect what you can’t see. When sensitive information—from Personally Identifiable Information (PII) to proprietary IP—leaks into unstructured formats like email attachments or unindexed legacy PDFs, it creates an invisible attack surface bypassing standard security scanners and leaving breadcrumbs for attackers.

The litigation tax

You can’t protect what you can’t see. When sensitive information—from Personally Identifiable Information (PII) to proprietary IP—leaks into unstructured formats like email attachments or unindexed legacy PDFs, it creates an invisible attack surface bypassing standard security scanners and leaving breadcrumbs for attackers.

Knowledge leakage

You can’t protect what you can’t see. When sensitive information—from Personally Identifiable Information (PII) to proprietary IP—leaks into unstructured formats like email attachments or unindexed legacy PDFs, it creates an invisible attack surface bypassing standard security scanners and leaving breadcrumbs for attackers.

If you unleash AI on messy, disconnected data, you’re not just getting bad results. You’re teaching AI to scale business operations on mistakes and hidden risks. AI readiness requires the right foundation, and most companies still can’t track what’s actually feeding their models.

To mitigate both cost and risk, organizations must shift from reactive cleanup to continuous compliance, gaining a clear, auditable view of their information from creation to destruction.

The illusion of AI readiness.

When asked to rank their confidence in the data used in their AI models, fewer than half were completely confident on any dimension.1

Mining the moat: Stop looking for filenames and start looking for answers

Traditional search relies on exact keyword matches, which fails in growing information estates plagued by inconsistent naming, varying file formats, and OCR degradation. “What the document is named" becomes "what the document means."

AI-powered natural language search redefines employees’ relationship with information, turning passive archives into actionable intelligence. This creates a psychological shift: data management becomes a value center, built for mining.

When leaders engage with intelligence extracted from previously dark data, the business case for information integration is reinforced. 
The archive stops being a graveyard and becomes the engine for 
strategic growth.

Create trust to power the agentic enterprise.

Even with a defined blueprint and the right technology, there’s still a gap between the value of content intelligence and the ability to deploy it at scale. Most organizations are stuck at departmental or localized implementations, trapped by a lack of confidence in their digital chain of custody.

Accessing the document isn’t enough. You have to be able to prove it hasn't been tampered with. To be legally defensible, it’s critical to know who handled information and when.

Becoming a truly Agentic Enterprise isn’t about having the flashiest AI model; it’s about rebuilding the foundation of enterprise truth. By moving past the limits of legacy formats and siloed systems, companies can create a unified, AI-enabled architecture that governs and activates their most vital assets.

A unified, governed information layer helps companies start to reclaim the 20% to 30% revenue loss typically caused by disconnected systems.3

Actions you can take today:

We’ll help you turn dark assets into AI intelligence.

Iron Mountain can help you activate your data as a strategic growth engine by establishing a modern information framework that automates governance and compliance. By connecting disparate systems and activating dark data, you’re one step closer to AI-ready intelligence.

When you’re ready to unlock what’s possible.

Iron Mountain’s expert digitization services and pre-configured AI solutions can transform your unindexed data silos into queryable, AI ready information. We’ll help make sure agentic AI is highly capable and deeply informed, drawing on your company's own records and institutional memory to deliver productivity.

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Sources

1 Iron Mountain, “The information supercycle: Aligning data, infrastructure, and capital for AI scale,” October 2026.

2 *Gartner, “Build a case for a DEX management office to overcome digital friction,” October 2025, citing the Gartner 2024 Digital Worker Survey.

3 IDC research, as cited in Data Science Society, “Managing the mess of fragmented financial data,” February 2026.

Sources reviewed October 2026.

*Gartner Build a Case for a DEX Management Office to Overcome Digital Friction; Dan Wilson, Robin Milton-Schonemann, Tori Paulman, Karl Rosander; October 9, 2025 GARTNER is a trademark of Gartner, Inc. and/or its affiliates. Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.