The data discipline gap: Why some AI initiatives scale and others stall

Artificial intelligence is now a business imperative, but most organizations are struggling to see results. One core reason? They have enough data to feed AI models, but their data is not high-quality and it’s not accessible.

Untapped data prevents AI initiatives from performing well and certainly from delivering at scale. And it’s happening across industries, around the world.

A recent McKinsey survey found 88 percent of organizations say they are stuck in early AI experimentation or limited deployments. Another global survey of 500 large organizations found a significant percentage suffer from ill-equipped information management systems.

What’s at risk is more than just missed opportunity. As one IT executive put it, “AI without good data is like a car without fuel.”

Establishing an AI-ready data foundation

To prevent AI initiatives from becoming that car without fuel, leaders can take these five practical steps:

1. Inventory and catalog data: Conduct a comprehensive data inventory across your systems, sources, and formats—including structured and unstructured, digital and physical. A well-organized inventory is the starting point to deciding which information should be retained, defensibly destroyed, or digitized. For physical records, identify high-value, high-risk content to digitize and govern the rest defensibly.
2. Assess and address data quality: Technology like intelligent document processing (IDP) extracts and validates data against predefined rules, databases, or reference documents. Once standardization is in place, any discrepancies or errors can be flagged for manual review and resolution. Data requires continuous monitoring. Never underestimate how quickly data quality erodes and how damaging that is to AI trust. Ongoing quality controls will be essential to preventing reputational and regulatory risk.
3. Implement governance and security: Guardrails ensure data is accessed and used appropriately, protected against breaches, and compliant with shifting regulations. A data retention schedule defines requirements and helps employees answer key questions such as how long to keep records, when to review, and when to dispose of them.
4. Integrate data silos: Create a unified foundation for AI models by bringing together data from disparate customer relationship management (CRM) and enterprise resource planning (ERP) systems, physical document archives, and email stores. This integration paves the way for advanced capabilities like natural language processing and predictive analytics. AI-readiness is about systems, not projects. Once data inventory, quality, and governance are addressed, your organization can leverage its integrated data to create scalable, reusable AI pipelines.
5. Commit to a cultural shift: AI-readiness isn’t a project owned by IT; it’s a strategic priority that spans the enterprise. Leaders must commit and collaborate across business units, aligning data governance and elevating data readiness to the level of corporate goals and key performance indicators (KPIs). When AI-readiness is part of your organization’s systems and culture, innovation abounds and the results become much more difficult for competitors to replicate.

Unlocking real value from untapped data

Organizations that have successfully laid an AI-ready data foundation are already seeing transformational results by leveraging the information they already have. For example, the European government agency e-EФKA now distributes pensions in a matter of weeks instead of years. Similarly, the GRAMMY museum has secured long-term digital accessibility and data integrity, implementing AI-powered search capabilities across its vast historical archives.

These organizations shared a common strategy: they partnered with Iron Mountain to bridge the critical gap between raw information and AI-ready data.

The power of Iron Mountain InSight DXP

It makes sense that businesses and agencies trust Iron Mountain—a company known for secure information management. Iron Mountain InSight® DXP is an intelligent content platform that turns digital, physical, and media content into a competitive advantage by governing information intelligently and compliantly, streamlining manual document and data processes, and unlocking actionable insights from unstructured data. As a single unified solution, InSight DXP provides the capabilities companies need to follow the five steps to AI readiness.

Looking ahead: AI-ready organizations for the win

Competitive advantage in the AI era begins with clean, organized data and a scalable infrastructure. This is why the next 12 to 18 months will be defined by data discipline; the winners of the AI race will be those who have prepared their information most effectively.

Our ongoing partnership with the McLaren Mastercard Formula 1 Team exemplifies this future-forward approach. By protecting and digitizing thousands of hours of film, footage, and blueprints, we are not just preserving their legacy—we are unlocking it. This collaborative effort continues to transform their vast archives into a searchable, actionable powerhouse of insight, ensuring their historical data fuels the next generation of racing innovation.

Ultimately, AI-readiness is not a destination, but a continuous commitment to excellence in information management. By mastering data discipline today, your organization ensures that every piece of legacy content is no longer a static record of the past, but a high-velocity asset fueling the innovations of the future.

Discover how global enterprises are transforming their data from chaotic and siloed to trusted and AI-ready. Read the report, Responsibly Sourced Data: AI’s Crucial Ingredient, from Iron Mountain and FT Longitude.

Get support from Iron Mountain by contacting us today.