According to IBM, the primary barrier holding back enterprise AI isn’t the technology itself but the persistent issue of data silos.
Ed Lovely, VP and Chief Data Officer at IBM, describes data silos as the “Achilles’ heel” of modern data strategy. Lovely made the comments following the release of a new study from the IBM Institute for Business Value that found AI is ready to scale, but enterprise data is not.
The report, which surveyed 1,700 senior data leaders, found that functional data remains stubbornly isolated. Finance, HR, marketing, and supply chain data all operate in isolation, with no common taxonomy or shared standards.
This fragmentation is having a direct, negative impact on AI projects. “When data lives in disconnected silos, every AI initiative becomes a drawn-out, six-to-twelve-month data cleansing project,” said Ed Lovely, VP and Chief Data Officer at IBM. “Teams spend more time hunting for and aligning data than generating meaningful insights”.
This is a direct threat to competitive advantage. For CIOs and CDOs, the mission is no longer just to collect and protect data, but to deploy it effectively to power these new AI systems.
From data janitor to value driver
The consensus from the study is that data leaders must be relentlessly focused on business outcomes, with 92 percent of CDOs agreeing their success depends on this focus.
Herein lies the central tension: while 92 percent are aiming for business value, only 29 percent 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 are expected to help. Leaders are showing a growing confidence in these tools, with 83 percent of CDOs in IBM’s research stating the potential benefits of deploying AI agents outweigh the risks.
At global medical technology company Medtronic, teams were bogged down matching invoices, purchase orders, and proofs of delivery. By deploying an AI solution, the company automated this workflow. The result was a drop in document matching time from 20 minutes per invoice to just eight seconds, with an accuracy rate exceeding 99 percent. This allowed staff to be redeployed from low-value data entry to higher-value work.
Similarly, renewable energy company Matrix Renewables implemented a centralised data platform to monitor its assets. This led to a 75 percent reduction in reporting time and a 10 percent reduction in costly downtime.
IBM finds the AI hurdles: Architecture, governance, and the talent gap
Achieving these results requires a new approach to data architecture while avoiding silos. The old model of costly, slow data relocation into a central lake is being replaced. IBM’s study finds 81 percent of CDOs now practice bringing AI to the data, rather than moving data to AI.
This approach relies on modern architectural patterns like data mesh and data fabric, which provide a virtualised layer…
Source link
Disclaimer
We strive to uphold the highest ethical standards in all of our reporting and coverage. We blogs.grocliq.com want to be transparent with our readers about any potential conflicts of interest that may arise in our work. It’s possible that some of the investors we feature may have connections to other businesses, including competitors or companies we write about. However, we want to assure our readers that this will not have any impact on the integrity or impartiality of our reporting. We are committed to delivering accurate, unbiased news and information to our audience, and we will continue to uphold our ethics and principles in all of our work. Thank you for your trust and support.
Website Upgradation is going on for any glitch kindly connect at [email protected]
