Predict Before It Breaks: AI in AV Monitoring & Maintenance

This month Utelogy featured in Systems Integration Asia, Utelogy explores how AI-powered predictive analytics and intelligent diagnostics can help organizations identify emerging issues before users are affected. As AV, UC and workplace technology environments grow increasingly complex, waiting for equipment to fail is no longer an effective approach.

For years, the standard approach to AV support has been largely reactive: a device fails, a user reports the problem, and a technician begins investigating. In mission-critical environments, however, waiting for something to break is no longer acceptable.

Meeting rooms, control centers, teaching spaces and operational facilities increasingly depend on interconnected AV, UC, network and workplace technologies. A fault in one component can disrupt an entire room, delay an important meeting or affect business-critical operations. As these environments grow in scale and complexity, organizations need to identify emerging problems before users experience them.

 Utelogy sees predictive analytics and intelligent diagnostics as fundamental to this transition—helping AV and IT teams move from responding to incidents towards anticipating and preventing them.

Turning operational data into early warning signals

Most equipment provides some indication that its performance is deteriorating before it fails. The challenge is recognizing those signals among the vast amount of data generated across an AV estate.

A single change may not be significant. A gradual increase in device temperature, for example, could reflect changing environmental conditions. When that increase is combined with more frequent connection losses, rising error rates, and a history of similar incidents, it becomes a much stronger indicator of an emerging fault.

AI-supported analytics can continuously examine these relationships and identify patterns that would be difficult for a human operator to detect manually. Instead of relying exclusively on fixed thresholds—such as issuing an alert when a device reaches a predefined temperature- the system can establish a baseline of normal behavior and recognize when performance begins to deviate from it.

This distinction is important. Traditional monitoring tells teams when something has crossed a limit. Predictive monitoring helps them understand whether a combination of small changes is likely to lead to future service interruptions.

Utelogy’s approach is to bring real-time monitoring, historical performance data, and operational context together so that teams can move beyond isolated alerts and build a clearer picture of device and room health.

The indicators that matter most

The value of predictive diagnostics depends on the quality, consistency, and context of the available data. Useful indicators can include:

• Device temperature and changes over time
• Signal quality and latency
• Connection stability and repeated disconnections
• Hardware and software error rates
• Usage hours, restart frequency and power cycles
• Firmware versions and configuration changes
• Peripheral availability and status
• Network-port and Power over Ethernet performance
• Historical incidents and previous remediation actions
• Room occupancy, booking information and patterns of use

None of these metrics should be considered in isolation. A device that has accumulated a high number of operating hours is not necessarily close to failure. However, high usage combined with rising temperatures and intermittent signal loss may justify inspection or replacement before the device affects a live session.

Context also changes the meaning of an alert. The same fault has a different operational impact in an infrequently used huddle space than it does in an executive boardroom, lecture theatre or emergency operations center. Intelligent diagnostics must therefore consider not only the technical condition of the equipment, but also the importance and current use of space.

From more alerts to better decisions

One of the unintended consequences of large-scale monitoring is alert fatigue. If every minor status change generates a notification, support teams can quickly become overwhelmed. Important warnings are then at risk of being lost among hundreds of low-value alerts.

AI can help by correlating events across the complete technology chain.

Consider a meeting room in which the display, video conferencing system, and control interface all appear to lose connectivity at approximately the same time. A conventional monitoring system may create three separate incidents. An intelligent system can recognize the relationship between them, identify the network switch or shared connection as the probable root cause, and present the support team with a single prioritized incident.

This is a key area of focus for Utelogy: turning large volumes of device and room data into actionable intelligence. By providing a unified view of the AV and UC environment, the Utelogy Platform helps teams investigate related events together rather than treating every symptom as a separate problem.

Alerts can then be prioritized according to:

• The likelihood of complete failure
• The number of devices or spaces affected
• The operational importance of the room
• Whether a meeting is currently in progress or due to begin
• The availability of alternative equipment
• Whether remote remediation is possible
• The potential impact on users and business operations

The objective is not to remove human expertise. It is to give technicians clearer evidence, reduce diagnostic time, and allow them to focus on the incidents where their knowledge has the greatest value.

Mixed-brand visibility is essential

Predictive maintenance becomes more difficult when operational data is fragmented across multiple manufacturer portals. Most enterprise AV estates contain equipment from numerous vendors, often deployed at different times and managed through separate applications.

A fault that appears to originate in a video conferencing endpoint may be caused by a display, USB peripheral, network connection, or control component from another manufacturer. Without visibility across the complete environment, teams can see individual symptoms but struggle to understand the wider chain of events.

Vendor-agnostic monitoring platforms can provide a unified operational view by bringing data from AV, UC, network, and workplace systems into a common environment. This creates a more complete foundation for analysis and enables patterns to be recognized across devices, rooms, buildings and regions.

This principle underpins the Utelogy Platform and its extensive AV and UC driver ecosystem. U-Manage provides centralized monitoring, analytics, alerting and remote management across mixed-brand technology estates. Predictive AI capabilities, developed in collaboration with Intel, introduce anomaly detection and predictive-maintenance intelligence to help organizations identify potential issues before they affect users.

Utelogy also gives teams the ability to combine device data with room, location, and usage of information. This additional context helps organizations understand not only what is happening, but where it is happening, how frequently it occurs, and what the likely operational impact will be.

These capabilities are most valuable when they form part of a wider operational strategy—not when AI is treated as a standalone feature.

Enabling condition-based maintenance

Predictive monitoring also changes the commercial model for integrators and managed service providers.

Under a traditional break-fix arrangement, service activity begins after a failure has occurred. Preventative maintenance improves this approach, but scheduled inspections can still result in technicians visiting fully functioning rooms or replacing equipment solely because it has reached a certain age.

Condition-based maintenance uses actual performance and health data to determine when intervention is required. A device is inspected because its behavior indicates deterioration, not simply because a date has appeared on a maintenance calendar.

For service providers using a platform such as Utelogy, this creates an opportunity to offer outcome-focused services built around availability, room readiness and user experience. Instead of measuring success only by the number of tickets closed or site visits completed, providers can demonstrate how effectively they have prevented disruption and maintained agreed service levels.

Remote monitoring and diagnostics can also help service teams arrive with a clearer understanding of the problem, the equipment involved, and the likely remedial action. This makes it easier to allocate the right technician, prepare the necessary replacement parts, and improve the likelihood of first-time resolution.

The operational benefits

When implemented effectively, predictive monitoring can deliver benefits across the technology lifecycle:

• Reduced downtime through earlier intervention
• Fewer unnecessary site visits and scheduled inspections
• Faster diagnosis and improved first-time resolution
• Lower maintenance and support costs
• Better prioritization of technical resources
• Longer equipment life through timely maintenance
• More informed repair-versus-replace decisions
• Improved room readiness and user confidence
• Stronger evidence for future technology investment

With Utelogy, the goal is to help customers and service providers translate these capabilities into measurable operational improvements. Monitoring is not valuable simply because more data is available; its value comes from using that data to make faster, better-informed decisions and improve service outcomes.

From one incident to estate-wide learning

Imagine a global organization experiencing intermittent display failures across several meeting rooms. The individual incidents initially appear unrelated and are resolved through device restarts.

By analyzing historical performance across the estate, the monitoring platform identifies that the incidents share a common pattern: the displays are running the same firmware version, connection errors increase after extended periods of operation, and each failure is preceded by a similar sequence of signal interruptions.

Using a centralized platform such as Utelogy, the organization can identify the widely affected device population, test a firmware update, and schedule remediation before further rooms fail. What began as a series of reactive tickets becomes an estate-wide preventative action.

This type of intelligence also becomes more valuable over time. Each incident, diagnostic action, and successful resolution contributes to a stronger operational knowledge base. Patterns can be compared across rooms and locations, allowing organizations to apply what they learn from one event across their entire estate.

Prediction is the next step—but not the final one

The direction of travel is clear. AV management is moving from reactive support to proactive monitoring, and from proactive monitoring towards predictive and increasingly autonomous operations.

The most mature systems will not only recognize that failure is becoming more likely. They will determine the probable cause, assess the operational impact, recommend the appropriate response, and, where it is safe to do so, initiate an automated remediation workflow.

Utelogy Predict has been designed to apply AI-driven predictive maintenance and anomaly detection to AV, UC and IoT operations. It continuously analyses operational data to identify emerging issues, predict potential failures, and help organizations intervene before services are disrupted.

The solution builds on the Utelogy Platform’s monitoring and management capabilities, using the data generated by connected devices to provide a more forward-looking view of system health. Rather than simply reporting that a device has failed, the objective is to identify the conditions that indicate failure is becoming more likely.

Utelogy Predict can support organizations by:

• Detecting abnormal device or system behavior
• Identifying patterns that may precede equipment failure
• Supporting earlier investigation and preventative action
• Improving device performance and operational reliability
• Helping uncover potential vulnerabilities or unusual activity
• Reducing the cost and disruption associated with emergency repairs
• Providing predictive insight across large, distributed estates

Utelogy Predict has also been optimized for Intel architecture using Intel’s oneAPI AI tools. This technical foundation matters because predictive intelligence must operate quickly and efficiently across potentially vast quantities of operational data. If analysis is too slow, an early warning may arrive only after the service has already been affected.

Success will depend on more than an AI algorithm. It will require high-quality device data, broad interoperability, operational context, trusted automation, and clearly defined service processes.

For organizations responsible for mission-critical AV, the question is therefore changing. It is no longer simply: “How quickly can we respond when something fails?”

It is: “How much disruption can we prevent from happening at all?”

To read this article online visit the Systems Integration Asia site.