According to IBM, the main barrier to enterprise AI is not the technology itself, but the persistent problem of data silos.
Ed Lovely, vice president and chief data officer at IBM, describes data silos as the “Achilles heel” of modern data strategies. Lovely made the comments following the publication of new research from the IBM Institute for Business Value that says AI is ready to scale, but enterprise data still cannot.
The report, which surveyed 1,700 senior data leaders, found that functional data remains stubbornly siled. Financial, human resources, marketing, and supply chain data all operate independently, with no common classification or shared standards.
This fragmentation has a direct negative impact on AI projects. “When data resides in isolated silos, any AI initiative becomes a long-term data cleansing project that takes six to 12 months,” said Ed Lovely, vice president and chief data officer at IBM. “Teams spend more time exploring and tweaking data than generating meaningful insights.”
This is a direct threat to competitive advantage. The mission of CIOs and CDOs is no longer just to collect and protect data, but to effectively deploy it to power these new AI systems.
From data custodian to value driver
The consensus emerging from this research is that data leaders must maintain a constant focus on business outcomes, with 92% of CDOs agreeing that success depends on this focus.
There is a central tension here. While 92% are aiming for business value, only 29% are confident they have “clear measures to determine the business value of data-driven outcomes.”
This gap between ambition and reality is where AI agents that can learn and act autonomously to achieve goals hold promise. Leaders are increasing their confidence in these tools, with 83% of CDOs in an IBM survey saying the potential benefits of deploying AI agents outweigh the risks.
At Medtronic, a global medical technology company, teams were stuck reconciling invoices, purchase orders, and proof of delivery. By implementing an AI solution, the company automated this workflow. As a result, document matching time was reduced from 20 minutes per invoice to just 8 seconds, with accuracy exceeding 99%. This allowed staff to be redeployed from low-value data entry to higher-value tasks.
Similarly, renewable energy company Matrix Renewables has implemented a centralized data platform to monitor its assets. This reduced reporting time by 75% and costly downtime by 10%.
IBM finds AI roadblocks: architecture, governance, and talent gaps
Achieving these results requires a new approach to data architecture while avoiding silos. The old model of costly and time-consuming data relocation to a central lake is being replaced. An IBM study found that 81% of CDOs are currently deploying AI to data, rather than migrating data to AI.
This approach relies on modern architectural patterns such as data mesh and data fabric, which provide a virtualization layer to access data where it resides. We also support the concept of “data products” (packaged, reusable data assets designed for specific business purposes, such as “customer 360-degree” views or financial forecasting datasets).
However, making data more accessible creates governance challenges. Collaboration between CDO and CISO is now essential to balance speed and security. Data sovereignty is of particular concern, with 82% of CDOs seeing it as a core part of their risk management strategy.
However, the biggest hurdle may be human resources. The report reveals that the talent gap is widening and threatens to slow progress. In 2025, 77 percent of CDOs will report difficulty attracting or retaining top data talent, up from 62 percent in 2024.
This shortage is further exacerbated by the fact that the required skills are a constantly moving target. IBM found that 82% of CDOs are “seeking and hiring for data roles that didn’t exist last year as they relate to generative AI.” This culture and skills challenge is often the most difficult part.
Hiroshi Okuyama, chief digital officer of Yanmar Holdings, explained: “Culture is difficult to change, but people are becoming more aware that decisions must be based on data and facts, and that evidence needs to be collected when making decisions.”
Open up data silos and launch enterprise AI
On the technology side, enterprise leaders must move away from siled data assets. This means investing in modern federated data architectures and encouraging teams to develop and use “data products” that can be securely shared and reused across the organization.
Second, on the cultural front, data literacy needs to become a business-wide priority, not just an IT issue. 80% of CDOs say that democratizing data helps organizations move faster, and they’re right. This means fostering a data-driven culture and investing in intuitive tools that make it easy for non-technical employees to interact with data.
The goal is to move organizations from running individual AI experiments to extending intelligent automation across core business processes. Successful companies are those that treat data as their most valuable asset, not as a byproduct of their applications.
“Enterprise AI at scale is within reach, but success depends on organizations leveraging the right data to power AI,” said Ed LaBrie, vice president and chief data officer at IBM. “For CDOs, this means establishing a seamlessly integrated enterprise data architecture that drives innovation and unlocks business value.”
“Organizations that get this right will not only improve AI, but transform the way they do business, make faster decisions, adapt to change faster, and gain a competitive advantage.”
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