Bridging the AI Data Readiness Gap

The initial rush to adopt AI as rapidly as possible is over, with 2025 being the year when organizations across all industries started moving from pilots to production. Yet scaling AI across the enterprise in a way that’s adequately managed and governed continues to elude many leaders who are faced with a difficult reality—most organizations’ data simply isn’t ready for prime time AI.

A recent IBM survey found that 81% of Chief Data Officers (CDOs) are prioritizing investments in AI capabilities, but only 26% expressed confidence in their data’s readiness to support AI projects.

“Enterprise AI at scale is within reach, but success depends on organizations powering it with the right data.” Ed Lovely, VP and Chief Data Officer at IBM, said in a release.

While business executives broadly see AI-driven analytics and automation as increasingly important competitive differentiators, there’s also growing awareness of the fact that those systems are only as good as the data that’s fed into them. However, data quality and trustworthiness are increasingly hard to assure. In many organizations, data remains scattered across myriad cloud services and enterprise systems in many different structures and formats. At the same time, regulators continue to tighten expectations on data privacy and governance, further putting the brakes on AI innovation.

One major development is how companies handle metadata—or data about their data. According to a recent report by Gartner, data management solutions are shifting from simple data cataloguing to “metadata anywhere orchestration platforms” that support a more dynamic approach to governance. These next-generation platforms themselves use AI and automation to discover new data sources and suggest relationships between datasets, and even detect data quality issues. Traditionally, these tasks required laborious manual effort by data stewards, an approach that is now thoroughly impractical given the sheer scale of data and the enormously data-hungry demands of AI use cases.

As ironic as it might sound, AI is itself fundamental to bridging the gap between data readiness and AI. Another trend exemplifying that truth is the fusion of master data management (MDM) practices with AI capabilities, where AI automates the matching and merging of records or infers connections between multiple data points. The value of AI is, of course, its ability to do that orders of magnitude faster than human curators can.

Closing this gap is pushing data leaders to change their organizational mindsets—chief data officers are increasingly viewed as business strategists, meaning they must focus on business outcomes and deploy data for competitive differentiation. This requires a two-pronged approach: improving data quality and access internally by revisiting data governance programs, and bringing AI closer to the data so that data doesn’t have to be constantly moved around. This approach is vital for reducing friction, as well as keeping up with governance, risk management, and compliance (GRC) demands.