AI Meets DEX: How Smart Analytics Make Outlier Devices Visible
One thing is certain in companies with hundreds or even thousands of devices: there are plenty of IT issues (e.g., long boot times, app crashes, etc.) – but they all don’t warrant the same
amount of – or sometimes even any -- IT support. Conversely, many problems that deserve more IT attention –leading indicators of bigger underlying problems -- go unreported because
submitting a ticket is too much effort. So, how can IT teams distinguish an isolated run-of-the-mill incident from a genuine systemic problem?
This is exactly where artificial intelligence (AI) and Outlier Detection in modern DEX solutions comes into play.
AI & DEX – short & sweet:
- The role of AI for Outlier Detection: AI-powered DEX solutions detect patterns and identify root causes — making outlier devices more visible.
- Proactive IT operations: Instead of just reacting to tickets, AI identifies issues early or even before users report them, and recommends preventive actions.
- Context matters: Outliers are evaluated against peer groups, time-based trends, and other factors (e.g., app crashes + RAM usage).
- Automation: Based on the analysis, remediation workflows can be triggered automatically, including updates, policy adjustments, or user notifications.
The data flood is real — and growing daily
Modern devices generate enormous amounts of telemetry data related to boot times, login patterns, hardware usage, network latency, application crashes, user feedback, and security events.
Each of those signals can be relevant and worthy of analysis and follow-up. However, they are often not meaningful in isolation or as a sequence of uncorrelated events. That makes
evaluating them a real challenge.
IT teams don’t need more data; they need more insight that helps them answer the central question: Which data or devices are truly outliers or signs of unusual
behavior that deserve more scrutiny?
Outlier Detection – the new gold of IT operations
DEX solutions with integrated AI analyze patterns, detect outliers, and group similar problems. Most importantly, they help IT admins focus on the right devices and
problems.
For example, a device takes 85 seconds to boot. Is that bad? Maybe – or maybe not if the median of all devices is 82 seconds. But if other devices with similar hardware, in the same
department, and on the same network segment are fully booted in 30 seconds, then the slow-booting device stands out.
AI helps separate the IT wheat from the chaff. It answers questions like:
- Does a device deviate significantly from normal behavior?
- Do devices with the same hardware show similar problems?
- What is the most likely cause of the anomaly?
From reactive to proactive IT operations through anomaly detection
Without AI, many IT teams get caught in a reactive cycle, taking action only when enough users complain about poor performance or problems accumulate. Prioritization is difficult because
many users never report issues, while others submit a ticket for every minor system hiccup.
AI-powered DEX solutions reverse this cycle. They detect issues and suggest preventive measures before they escalate and produce tickets. That means that errors are
captured faster and more accurately, increasing user satisfaction and reducing IT workloads. It also re-focuses IT resources on problems that truly need expert attention.
Here’s another scenario that highlights where AI and DEX can produce fast results:
Several employees complain about frozen applications and slow systems. Without AI and DEX, IT struggles using tedious, time-consuming manual methods to determine whether specific apps or
insufficient memory are the cause. But with AI and DEX, the combined solution identifies which devices regularly use nearly all of their available memory, alerts the admin before apps
freeze or systems crash, and reports the issue with suggested solutions before a ticket is even created.
How to combine DEX & UEM successfully
What is the best way to integrate DEX analytics and UEM solutions? Our Best Practices Guide provides practical insights on how IT teams can bring the two solutions together for maximum
transparency, efficiency, and employee satisfaction.
Download the Free Best Practices Guide Now
What “Outlier” really means: contextual awareness through AI
A device isn’t “bad” because it crashes once. Context is key. Modern AI models in DEX solutions examine multiple factors in the context of outlier detection and develop heuristics that consider:
- Time-based trends (e.g., gradual performance decline)
- Comparison with peer groups (similar devices in similar contexts)
- Correlation with other signals (e.g., app crashes + high RAM usage + negative user feedback)
- Specific hardware (e.g., RAM configuration or CPU design)
Results for individual devices are also used in further analyses. Only with this holistic view can IT determine whether a device is truly an outlier or just “having a bad day."
From endpoint analytics to automation – the next step
The future of DEX solutions like baramundi perform2work is even more promising. If AI flags a device as an outlier, remediation workflows can be triggered automatically. Examples include:
- Initiating an update
- Adjusting group policies
- Sending solution instructions to the end user
Combined with tools like n8n or Power Automate, the DEX platform can become a hub for intelligent IT automation with AI as the brain.
Conclusion: AI + DEX = an IT management gamechanger
In an era where IT teams are flooded with data, drawing the right conclusions by identifying relevant problems, detecting outliers, and taking appropriate actions is
crucial.
AI in DEX solutions enables exactly that. It doesn’t just detect errors; it understands context, prioritizes intelligently, and can even act automatically. That end-to-end solution will
transform a patchwork of reactive IT processes into a future-ready, proactive device management system that boosts efficiency and digital employee satisfaction.
