AI isn’t hallucinating—but your data might be
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Organizations blame AI models when outputs are inaccurate, but many hallucinations originate from incomplete, fragmented, or poorly governed data. Better data beats a better model.
AI isn’t hallucinating—but your data might be
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Organizations blame AI models when outputs are inaccurate, but many hallucinations originate from incomplete, fragmented, or poorly governed data. Better data beats a better model.
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Credit: PaeGAG
“Hallucination” has become the catch-all term for AI getting things wrong. It describes a real phenomenon: Large language models can generate confident, fluent answers that are simply untrue.
But in IT operations, many errors labeled as hallucinations have a more ordinary explanation. The AI isn’t inventing facts. It’s faithfully reporting information that happens to be outdated, incomplete, or contradictory. I’ve seen this play out across enough environments to know the cause usually sits upstream, in the data the system learned from and the picture of the estate it read.
Fix the data foundation under your AI systems →
That distinction matters, because the fixes are different. You don’t tune the model. You fix the information it relies on.
When the answer is wrong, but the logic is right
Imagine a service desk analyst asking an AI assistant whether a user’s laptop has received the latest security update. The assistant says yes. The analyst closes the ticket. Two days later, the device is flagged in a vulnerability scan. What happened? The assistant pulled from an endpoint record last updated before the device went offline for a week. The data was accurate when it was written. It wasn’t accurate when it was used.
Or consider a knowledge article describing a VPN troubleshooting procedure that was retired after a network change. An AI assistant retrieves it, summarizes it neatly, and recommends steps that no longer apply. The model performed well. The source didn’t. In each case, a human reviewer reading the same record would likely have made the same mistake.
This is not a rare edge case. In a 2026 survey of 3,900 employees across six countries, including 1,500 IT professionals, 68% of IT pros reported personally seeing AI produce hallucinations with potential operational impact. Just over half caught the errors before they caused problems, while 16% did not. That’s a significant chunk of wrong answers making it into real workflows, and those are just the ones people noticed.
3 data problems behind unreliable AI
Firstly, most trust issues with AI in IT operations trace back to a few recurring patterns. Poor data quality (missing fields, inconsistent naming, duplicate records) makes it hard for any system to produce accurate answers.
Stale information is the second pattern. IT environments change constantly. Devices move, users change roles, software is updated. Data that was correct last month may be misleading today, especially when AI presents it with no indication of age. Autonomous endpoint management can help keep records current, but many organizations still rely on periodic manual cleanups that leave gaps.
The third is conflicting records. When two systems disagree, AI may pick one arbitrarily, blend them, or produce an answer that matches neither. Without a clear rule for which source wins, the output becomes unpredictable.
Explainability and governance build trust
For IT teams, a useful AI answer isn’t just correct. It’s checkable. When an assistant recommends an action, practitioners need to see where the recommendation came from. Which record did it use? When was it last updated? Did other sources disagree? Explainability turns AI from a black box into a colleague whose reasoning can be reviewed. It also speeds up correction: If an analyst can see that an answer came from a stale knowledge article, they can flag the article instead of losing faith in the entire tool.
The governance measures that reduce unreliable output are less about tuning models and more about maintaining the information they rely on, as the Ivanti Neurons platform is designed to do. Set freshness thresholds so data older than a defined window is flagged or excluded. Establish source-of-truth rules for when systems conflict. Assign owners to knowledge content with scheduled review dates. Log AI recommendations and outcomes so recurring errors can be traced to their source. And give practitioners an easy way to report incorrect answers.
Trust is earned in small moments
Adoption of AI in IT operations rarely fails in one dramatic incident. It erodes gradually, one questionable answer at a time, until practitioners quietly stop using the tool. The reverse is also true. Trust builds when answers are consistently accurate, sources are visible, and errors are easy to trace and fix.
Reliable AI starts with reliable information. The instinct when a system produces a confident wrong answer is to blame the model. Most of the time, the technology did exactly what it was told. It just wasn’t told the truth.
Make your data trustworthy before you trust your AI with it →
Artificial Intelligence
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