AI and the Transformation of Network Operations Management
With the complexity of networks today, IT managers spend outsized portions of their time monitoring application performance and troubleshooting service. Moreover, Gartner estimates that greater than 65% of enterprise networking activities are manual.
IT has become heavy-lifting work, as manual oversight struggles to keep up with the mountains of network traffic data amidst the rising demand for cloud application services delivered everywhere.
Serving as a workforce multiplier, Artificial Intelligence (AI) promises to be a powerful tool for the future of network operations.
AIOps (AI for IT Operations) brings the power of AI and machine learning to IT operations and network management, and when leveraged effectively, it can be transformative. Using advanced analytics and data science, AIOps has the potential to offer greater network visibility, optimize network performance, and prevent service outages—freeing up humans to focus elsewhere in the process.
Let’s explore how AI works to the advantage of IT management.
What is AIOps?
AIOps takes advantage of advanced data analytics, including machine learning, predictive analytics, and behavioral analytics, to observe, analyze, and interpret massive amounts of network data in real-time.
Using AIOps is like having the services of a virtual network engineer monitoring 24/7. It can produce insights like:
- Increased awareness of issues and anomalies on the network.
- Root causes for degradation in service quality.
- Bandwidth resource problems, helping teams respond quickly and effectively to changes in network behavior or load.
- Automated network management functions based on a historical record of performance.
AIOps is a compelling technology to adopt for several reasons. It can operate 24 hours a day, 365 days a year. Done right, AIOps can reduce stress levels among network managers, elevating them to do more interesting work.
Another significant benefit is improving network performance and reliability without overspending or stressing the team. With AI, machine learning, and network behavioral analysis, IT teams can build a complete view of network activity and trends, understanding “normal” network activity, along with the ability to detect and remediate performance or security issues.
If anything, AI-powered virtual network engineers should be better suited at managing networks than their human counterparts. This should make sense, given an AIOps solution’s ability to see millions of traffic data points and thousands of network elements simultaneously—something a person cannot do.
How AIOps works: Insights become automation
AIOps works through virtual network engineers, or “virtual assistant” entities. These virtual network engineers use AI-powered algorithms to perform intelligent pattern recognition and root-cause analysis on network traffic and other related data streams. This process enables them to detect and predict anomalies. More importantly, they can share insights and recommended action plans with network managers.
But they can also go a step further, turning insights into a fully automated or totally autonomous network.
There is a distinct difference between insights and automation. Producing insights, AI engines can detect issues using pattern recognition and make recommendations to fix problems using root-cause analysis. But to enable full automation, AI engines must be able to solve problems themselves, acting alone to remediate the situation they find. For instance, the virtual network engineer might observe sub-par performance and respond by rerouting traffic — allocating bandwidth according to demands. In some cases, the AIOps virtual network engineer might even change network configuration settings.
Getting to this advanced usage level takes time, training, and trust. This explains why forward-leaning IT executives are getting started early. They want to accelerate the path to value, as they recognize that success requires building a playbook of rules and guidance for AIOps to work well in their customized IT environment.
AIOps adoption: Manual processes spur strong interest
According to a Gartner study, the rate of network automation today is relatively low, meaning there’s a substantial opportunity for AIOps to deliver value. Gartner found that 72% of respondents automated fewer than 25% of their network activities. Just 10% automated over 51% of network activities. Industry research reveals a strong interest in AIOps adoption, with Gartner pinning AI Networking at the top of its annual Networking Hype Cycle.
How to get started with AIOps
AIOps doesn’t come without challenges. Naturally, some employees may fear they’ll be automated out of a job, but with exposure, most will recognize that AIOps serves as more of an assistant, making life easier and giving them more interesting work to do. Change is hard, though, so it may make sense to find a few “quick wins” that will garner trust and ease the transition. It might seem obvious, but start simple with repetitive manual tasks.
Interest in AIOps is high amongst IT leaders, and the potential for these tools to become a workforce multiplier is real. While most companies are currently benefiting from AI-powered insights, the key is to have the right AIOps capabilities in place to cross the chasm from insights into true automation. When an AI engine can identify problems, recommend solutions, and act alone on those recommendations, it provides more meaningful value. The benefits of the technology are clear, and there are several strategies to accelerate the transition to full network automation.
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