Why responsible data is the real driver of AI advantage

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Data readiness is now a defining factor in whether organizations realize the full value of their AI.

October 7, 20257 mins

Data has always been a source of competitive advantage. But as AI becomes central to business strategy, the quality, integrity, and transparency of that data are more important than ever. Without responsibly sourced data, AI systems risk producing hallucinations and misleading insights.

To assess how prepared organizations are for this shift, Iron Mountain partnered with FT Longitude to survey 500 senior leaders from large enterprises worldwide. This research reveals common gaps in current data practices and spotlights a small group of leaders whose approach offers a blueprint for building AI-ready information management.

AI readiness as a performance indicator

Data readiness is now a defining factor in whether organizations realize the full value of their AI. On average, large enterprises gained $1.9 billion in additional revenue over the past year by improving how they collect, manage, and use data. This “good data dividend” is felt widely, with nine in 10 organizations reporting stronger profitability, productivity, and new business generation as a direct result.

However, the risks of poor data management are just as real. Data integrity flaws have cost organizations over $300,000 in the same period, while also eroding customer experiences, agility, and trust in AI outcomes.

Many organizations also admit to performance gaps, noting factors such as weak data integrity and an inability to quickly integrate data to support real-time decision-making. Only a small group of leaders are effectively overcoming these challenges, showing what it takes to build truly AI-ready information systems.

AI without good data is like a car without fuel.

Swami JayaramanSenior Vice President and Chief Enterprise Architect at Iron Mountain

Lessons from data leaders

Fewer than 10% of surveyed organizations qualify as true leaders in using information management to create a competitive advantage, but their practices point the way forward.

What leaders do differently:

Integrity: Audits and AI-powered quality controls are embedded into workflows to ensure information accuracy.
Transparency: Data lineage is tracked across systems and “AI nutrition labels” are used to build trust.
Modernization: Investments are made in systems that reduce technical debt and support faster integration.

For others, the path forward doesn’t require perfection. As Narashimha Goli, Chief Technology Officer of Iron Mountain, advises: “Focus on the problem you need to solve and identify the datasets you need to tackle that issue.” From there, build around outcome-driving data, make integrity non-negotiable, and prioritize quality over sheer volume. These shifts both reduce risk and accelerate AI readiness.

Closing the readiness gap

AI readiness is no longer optional, and good data is the crucial ingredient. Leading organizations prove that responsibly sourced, high-integrity data doesn’t just fuel AI, it drives growth while reducing risk. With the right foundation, any organization can close readiness gaps and capture the next wave of AI-driven advantages.

Iron Mountain InSight^® DXP helps you build that foundation, turning scattered information into high-quality, trusted data and positioning your organization to lead.