AI for Network Leaders — Powered by Selector

Join us in NYC on March 25th

AI for Network Leaders — Powered by Selector

Join us in NYC on March 25th

On this page

What are the key reasons organizations are adopting AIOps?

Artificial intelligence (AI) has been around for years, and we’ve all experienced firsthand some real-world examples such as self-driving cars, smart assistants, marketing chatbots, and more. But now, AI as part of network monitoring has gained considerable traction and has become a key element in AIOps. With ongoing digital transformation and IT environments becoming more complex, today’s businesses are turning to AIOps to help manage their networks for improved visibility and performance. Let’s explore some of the reasons why today’s organizations are adopting AIOps.

Too many monitoring tools make data analysis challenging

An industry report surveyed over 100 IT professionals and found that nearly 72% of organizations rely on seven to nine different IT monitoring to support modern applications. The use of so many different monitoring tools makes it extremely difficult to obtain end-to-end visibility across the entire business. This is especially true when IT teams are faced with analyzing large amounts of data from various tools and devices. It becomes nearly impossible to be able to correlate and analyze all this data by human intervention alone.

Integrating AIOps into IT operations helps to improve automation by triggering actions and workflows without manual intervention. By leveraging the data collected and analyzed by AIOps, predicting future incidents becomes possible which can help IT teams proactively address any issues before they arise.

Delivering the best customer service with predictive analytics

One key objective for any enterprise is to provide exceptional customer experience. Traditional IT tools simply can’t keep up with the volume and don’t allow for scalability based on the demand and lack of insights to correlate data across different but independent systems and environments. In short, real-time insights become next to impossible in traditional IT operations and make it difficult to resolve issues before the customer is affected.

AIOps can help collect and aggregate large volumes of data generated by multiple IT infrastructure applications as well as customer usage patterns that would typically take IT operations teams countless hours/days/weeks to manage. Furthermore, AIOps can analyze and manage complex data to predict future events and outages before they arise. With customized reports and dashboards, IT teams have enhanced visibility of the overall infrastructure allowing them to take a proactive approach to solving network problems. As a result, outages can be prevented before they impact the end user.

Improved collaboration between teams

Before big data and the cloud revolutionized business, it was the norm for different departments to create and manage their data. Teams developed their ways of working with data and analyzing it to best suit their needs. But the demands of today’s digital economy have changed how organizations collect, process, and analyze data. We are now seeing an increase in data silos which creates barriers to information sharing across teams and departments. Not only do silos prevent relevant data from being shared, but they also discourage collaboration and waste resources like time and money.

AIOps encourages collaboration and workflow activities between different teams, different departments, and even team members working in different time zones. It helps get everyone involved on the same “data” page by facilitating the sharing of comprehensive reports and data presentations in a single pane view.

Improved Return on Investment

Gartner reported that the average cost of network downtime for businesses is $5,600 per minute. However, this cost depends on various factors such as company size and industry vertical. For example, a 2016 study found that higher-risk industries such as banking/finance, healthcare, government, and media and communications average $5 million per hour. Also, there are intangible costs such as loss of customers, loss of employee productivity, and a negative effect on brand reputation.

By integrating AIOps, enterprises can reduce mean time to repair, prevent outages, and decrease IT operational costs which ultimately improves the bottom line.

In sum, with the need for greater IT visibility and better performance, organizations are turning to AIOps to handle the complexity of their IT environment and improve overall business operations to gain a competitive edge.

More on our blog

Beyond the Dashboard: Selector’s Patented Approach to Conversational Observability

For years, IT operations teams have been trapped in a frustrating paradox: the data they need to solve critical issues is right at their fingertips, yet entirely out of reach. Accessing it requires engineers to master complex, platform-specific query languages, dig through endless layers of dashboards, and hunt for the exact visualization that holds the answer. Under the intense pressures of modern speed, scale, and complexity, this rigid model is breaking down. At Selector, we recognized a fundamental opportunity to change how teams interact with their data. Our recently published U.S. patent application (US20250278401A1, filed March 2, 2024, and published September 4, 2025), titled “Dashboard metadata as training data for natural language querying,” outlines a transformative solution. By utilizing dashboard metadata, aliases, and user interaction data as training material, we empower operators to bypass structured queries entirely and obtain infrastructure insights using plain, natu

The Business Case for AI-Driven Observability in Network Operations

Modern network operations generate an extraordinary amount of telemetry. Metrics, logs, events, topology data, cloud signals, and service context all contribute to a richer picture of system behavior. As environments expand across cloud, data center, edge, and SaaS, the opportunity for operations teams is clear: when that telemetry is unified and understood in context, it becomes a powerful source of resilience, efficiency, and business insight. That is why AI-driven observability has become such an important priority for IT and operations leaders. Its value comes from helping teams move through complex environments with greater clarity. Correlated signals, contextual awareness, and shared operational understanding help teams identify issues faster, coordinate more effectively, and resolve incidents with greater confidence. For business leaders, the conversation is increasingly practical. They want to understand how observability investments contribute to uptime, team productivity, op

Solving the Ticket Noise Problem: What We Learned from Our ServiceNow Webinar

On March 18th, we hosted a session focused on a challenge that continues to undermine even the most mature IT operations teams: ticket noise.  It’s easy to dismiss noise as just “too many alerts”. But as we explored in the webinar, the real issue runs deeper. Ticket noise is a symptom of something more fundamental — a lack of correlation, context, and shared visibility across the stack.  If you weren’t able to attend, this blog walks through the key ideas, examples, and takeaways. And if any of this feels familiar, it’s worth watching the full session.  View “Solving the Ticket Noise Problem: Bringing Intelligence to ServiceNow”.  The Hidden Cost of Tickets Most organizations don’t struggle because they lack monitoring. In fact, the opposite is true — they have too much of it. Over time, teams adopt specialized tools for every layer of the environment: Each tool does its job well within its domain, but incidents don’t respect those boundaries. As discusse

このサイトは開発サイトとして wpml.org に登録されています。remove this banner のキーを使用して本番サイトへ切り替えてください。