Downtime is one of the biggest risks in enterprise IT today, and one of the most costly.
Enterprises today increasingly deploy infrastructure to the cloud, use more SaaS tools, and build on distributed infrastructure. That makes it harder to locate the source of an app or service interruption.
Traditional ITSM has been largely reactive. The enterprise responded to incidents only when something broke, until users reported a problem and an IT staffer responded. The monitoring tool may have given the IT team visibility into problems, but didn’t always avert an incident beforehand.
As a result, the growing interest in AI-generated predictions for ITSM is obvious. Predictive AI analyzes historical data, spots patterns, behavior, and anomalies that may lead to an app outage, and takes action to head it off before it affects business operations.
From Reactive to Predictive IT Operations
IT staff have historically functioned in the following reactionary pattern. Something breaks, alerts are triggered, staff members look into the matter, and they fix the service.
There is a challenge once teams reach a certain level of growth and complexity. Modern environments produce so much data that it is too large and hard to sift through manually.
AI for Prediction advances the ball by predicting events before they arise. It identifies slight changes that potentially could point to impending issues.
Such cases could be the following:
- Server temperature changes over time
- Increase in memory spike placement
- A growing number of latency spikes
- Storage wear and tear over time is a trend
This approach allows time to intervene before downtime.
Predictive Maintenance in ITSM
A common practice in industry, predictive maintenance forms an integral part of IT operational practices.
In the context of ITSM, this involves actively identifying potential points of failure through prediction, pinpointing systems that are “at risk,” and taking steps to maintain them before they break.
This is why many organizations are starting to look at tools like Predictive Maintenance with AI. Why? Because they need to understand how AI can be used as a tool to maintain service continuity.
Instead of thinking about an incident as something that’s already happened, and there’s little you can do about it, predictive ITSM encourages us to instead see preventable operational risks that are predictable.
Key Challenges
Data Fragmentation
Many use cases in the enterprise rely on disparate tools and locations, preventing a comprehensive view across systems and, hence, inaccurate prediction.
Alert Fatigue
Numerous alerts of variable priority can slow down operators’ finding the root causes of real issues. With Predictive AI, operators can train the model to discern signal from noise.
Legacy Systems
Many older systems do not have the ability to emit the telemetry required for advanced analytics and need to be modernized for predictive models.
Strategic Value of Predictive ITSM
The value of AI for predictions to ITSM organizations is not just in reducing the number of outages.
Better Resource Planning
IT leaders are positioned to make better decisions about staffing, budgets, and maintenance work based on artificial intelligence predictions of where infrastructure may be at higher risk for failure.
Improved Service Stability
Fewer service desk tickets becoming incidents means better service availability, leading to improved user experience and consistent service delivery across the organization.
Stronger Continuity Planning
For businesses in which digital services are an essential part of operations, avoiding outages helps ease compliance with regulations, build customer confidence, and create a steady stream of revenue.
In addition to using this information to make decisions about resource planning, organizations also benefit from the big picture look at where infrastructure is headed that only a predictive system can provide.
Conclusion
As the technology that powers businesses grows more complex, it’s no longer enough to simply react to problems when they occur within an organization’s IT environment. Predictive AI provides the capability for enterprises to predict potential faults on the network, instead of only being able to respond once an issue has reached a certain critical point.
This is part of a larger trend toward the development of IT workloads that are data-driven and proactive in nature. Being able to proactively avoid an outage is fast-becoming table stakes for a modern enterprise organization, rather than a competitive nicety.
