IBM is sounding the alarm on one of the biggest barriers slowing down the rise of enterprise artificial intelligence (AI): data silos. While AI technology has matured and is ready to scale, most organizations’ data infrastructure hasn’t caught up, according to a new global study from the IBM Institute for Business Value.
Ed Lovely, IBM’s Vice President and Chief Data Officer, called data silos the “Achilles’ heel” of modern data strategy. He explained that when finance, HR, marketing, and supply chain data operate in isolation, AI initiatives grind to a halt.
“When data lives in disconnected silos, every AI project becomes a drawn-out, six-to-twelve-month data cleansing exercise,” Lovely said. “Teams spend more time finding and fixing data than generating insights.”
The study, based on responses from 1,700 senior data leaders, found that fragmented data remains the single greatest obstacle to AI deployment. While 92 percent of chief data officers (CDOs) agree their success depends on delivering measurable business value, only 29 percent feel confident in their ability to quantify that value.
From Data Custodian to Value Creator
IBM’s report highlights a shift in the CDO’s role—from data custodian to business value driver. As AI systems evolve, organizations must transition from simply collecting data to deploying it strategically.
Real-world examples show how this transformation can pay off. Medtronic, a global medical technology firm, used AI to automate invoice and purchase order matching, cutting processing time from 20 minutes per document to just eight seconds with over 99 percent accuracy. Renewable energy company Matrix Renewables saw similar gains, achieving a 75 percent reduction in reporting time and a 10 percent drop in downtime after implementing a centralized data platform.
These cases underline a key point: when data is unified and accessible, AI delivers tangible results.
Rethinking Data Architecture
IBM’s research shows that the traditional model of moving all data into a central “lake” is being replaced by more agile approaches. In fact, 81 percent of CDOs now favor bringing AI to the data rather than moving data to AI.
Modern frameworks like data mesh and data fabric allow companies to access data where it resides while maintaining control and compliance. They also promote the concept of “data products”—standardized, reusable assets like customer profiles or financial forecast datasets that can be shared across departments.
However, more open data access brings new governance challenges. The partnership between Chief Data Officers and Chief Information Security Officers has become essential to balance innovation with protection. Data sovereignty—ensuring data stays within compliant jurisdictions—has become a top priority for 82 percent of leaders surveyed.
The People Problem: A Widening Skills Gap
Despite the technology advances, IBM warns that talent shortages could stall progress. In 2025, 77 percent of CDOs reported difficulty attracting or retaining skilled data professionals—up sharply from 62 percent in 2024.
The problem isn’t just supply—it’s the pace of change. IBM found that 82 percent of organizations are hiring for data roles that didn’t exist a year ago, many tied to generative AI. As Hiroshi Okuyama, Chief Digital Officer at Yanmar Holdings, put it, “Changing culture is hard, but people are becoming more aware that decisions must be based on data and facts.”
Unlocking the Power of Enterprise AI
IBM’s findings make clear that overcoming data silos is both a technical and cultural challenge. Enterprises must invest in federated data architectures that allow secure data sharing while promoting “data literacy” across all departments.
According to the study, 80 percent of CDOs believe that democratizing data—making it easier for employees at all levels to access and interpret—helps organizations move faster and make smarter decisions.
Lovely summarized the opportunity succinctly: “Enterprise AI at scale is within reach, but success depends on powering it with the right data. Organizations that integrate their data effectively will not only improve their AI—they’ll transform how they operate, make faster decisions, and gain a competitive edge.”
As AI technology races ahead, the companies that will lead the next era of intelligent business are those that tear down their data silos and build unified, secure, and accessible data ecosystems. IBM’s message is clear: the future of enterprise AI doesn’t depend on smarter algorithms—it depends on smarter data.
