AI didn’t create your IT data problem, it just exposed it
When an AI pilot in IT operations underdelivers, the first instinct is to question the technology. Was the model wrong? Did the tool overpromise? Did the team pick the wrong use case? Those are fair questions. But in many organizations, the answer sits somewhere less glamorous: in a configuration management database that hasn’t been reconciled in two years, in asset data in spreadsheets, or in a…
Artificial intelligence doesn't generate IT data issues, but it does illuminate existing problems. Often, organizations face fragmentation in their data systems, with each department using different tools and platforms that don't share information effectively. For example, an endpoint management tool might list 9,400 devices, while a vulnerability scanner detects 8,700, and a configuration management database shows 10,200, including retired devices.
No single system has the complete picture, and employees learn to work around these discrepancies by relying on their own experiences and intuition.
However, AI struggles with this lack of alignment. If an AI system is asked to identify laptops missing critical updates, it may double-count or overlook devices if the data from different systems is inconsistent. Similarly, an automated ticket-routing system trained on conflicting historical data may learn to perpetuate those inconsistencies. In both scenarios, the AI is merely reflecting the organization's lack of shared understanding about its own data.
On the other hand, organizations that treat AI as a critical part of their strategy tend to see better outcomes. Nearly half of IT professionals in such organizations report that AI makes their work faster and more effective, more than double the rate at companies still in the pilot phase. The key to these positive results lies in building trusted context—information that is both consistent and current enough for both humans and AI to act upon confidently.
To achieve this, organizations should map where their key data resides and identify areas of disagreement between systems. They should assign clear ownership for each critical data domain, agree on common definitions for assets and statuses, and automate reconciliation between systems instead of depending on occasional manual cleanups. Treating data quality as an ongoing operational metric, rather than a one-time task, is also crucial.
Starting with small, manageable AI use cases can help lay the groundwork for broader adoption. By tracing every data source an AI system depends on for a single use case, such as vulnerability reporting or ticket triage, organizations can see the immediate impact of fixing alignment issues. These visible results can then serve as a model for tackling data alignment challenges in other areas.
In essence, AI serves as a mirror, revealing where an organization's data alignment breaks down. Teams that view these findings as an opportunity to improve their data structures rather than a reason to abandon AI will be better positioned to scale AI responsibly and reap its benefits.
Written by urgent.news from Computerworld's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
