Is your organization data-ready for AI? (and how to get there if you aren’t)

Whitepaper

There’s data. Then there is quality data. And when it comes to artificial intelligence (AI), knowing the difference is a game-changer.

January 7, 202512 mins

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AI is at the top of almost every business leader’s priority list: 86% of financial services IT and business executives say that AI is critically important to their business’ success in the next two years.

There are dozens upon dozens of use cases for AI, from real-time contract analysis to invoice processing to predicting delinquencies based on historical payment information to regulatory reporting. But even though AI is becoming a must-have for financial services institutions, the track record for AI success isn’t great.

Gartner reports that 49% of organizations struggle to estimate and demonstrate the return on investment (ROI) of AI projects.

AI challenges often boil down to issues surrounding data integrity. “When it comes to AI, the quality of the product you get is only as good as the data you feed into the models,” explains Josh Langley, Iron Mountain's CIO.

Yet there’s a disconnect: Even though data integrity is key to successful AI implementation, only 17% of business leaders consider a robust data strategy as the most effective way to ensure ROI on AI.

Why this white paper is a must-read

AI ROI isn’t guaranteed, but your upfront work will lay a foundation for success. This white paper explores how data quality impacts AI initiatives, the barriers to data integrity, how to overcome them, and best practices for addressing data readiness.

Perceived AI data readiness vs. reality

When asked, many organizations believe their data is AI-ready, but once you dig a bit deeper, you’ll find areas of concern, with more than half of organizations saying they have AI implementation challenges that include data quality, data categorization, unstructured data, and data silos.

For example, although 88% of organizations say they have an information management strategy, 44% admit that their strategy lacks basic components such as data archiving and retention policies, lifecycle management solutions, and inadequate strategy leading to data quality issues.

You are not ready for AI without getting a handle on data integrity. Data integrity refers to the accuracy, consistency, and reliability of data, including structured and unstructured data and data that exists in physical documents.

“AI insights are reliant upon data integrity,” notes Langley.

When it comes to AI, the quality of the product you get is only as good as the data you feed into the models.

Josh LangleyCIO, Iron Mountain

Addressing the significant data challenges impacting AI success

Various factors impact data integrity, including unstructured data, dark data, and increasing data volumes.

Unstructured data accounts for at least half of all data—and probably more. Forrester found that an average of 31% of enterprise data is semi-structured, and 27% is unstructured.⁶Unstructured data typically doesn’t make it into AI models, meaning institutions base important decisions on less than half their data, creating business blind spots.

While financial institutions have progressed in managing unstructured data, they need to do more to extract, contextualize, and make it accessible. The effort is well worth it since institutions that invest in mining unstructured data are nearly three times more likely to experience double-digit revenue growth than those that don’t make the investment.⁷

Dark data—data that is accessible but not knowable—is another challenge. It poses risks by adding to business blind spots but also opens financial institutions up to regulatory risks since unknown data may not be compliant.

Two-thirds (64%) of organizations manage at least 1PB of data, and 41% manage more than 500PB.⁸Data volumes will continue to increase, so kicking the data integrity problem down the road only means that you’ll have a more significant challenge when AI project failures force you to address data integrity issues.

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