The sheer volume of data and the need for fast, accurate troubleshooting can overwhelm even the most experienced network operations center (NOC) teams. Stress levels increase when response times lag — as do costs, customer frustration, and risks to revenue. AIOps can help. Deploy AIOps to automate data analysis and correlate alerts in real time, filter alerts to reduce noise, and pinpoint incident root cause faster than traditional methods.

Your organization probably relies on a collection of observability tools to track specific elements of its IT stack. You’re not alone; a recent survey from Enterprise Strategy Group showed that most organizations have six or more observability solutions. Our research found that the average BigPanda customer uses 20 observability and monitoring data sources!

ITOps teams are inundated with a significant volume of alerts each day. Sifting through these alerts to discern which ones are harmless and which could lead to major incidents is a time-consuming and tedious task. This process often involves hunting for information across disparate data sources, tools, and workflows. As a result, the investigation can slow down incident response times, negatively affecting service reliability and customer satisfaction.

Advances in artificial intelligence are rapidly transforming the IT operations landscape. According to Enterprise Strategy Group, 85% of organizations use or plan to deploy AI across many functional areas, including IT operations. Among its many benefits, AI can help ITOps teams: AI has immense potential to transform how IT operations, service management, and infrastructure teams function. Adoption is the first step toward creating organizational change.

Handling IT alert data can feel like you’re drowning in information. The average BigPanda customer uses more than 20 observability and monitoring tools. Between system logs and user reports, an overwhelming amount of information is coming from all directions. That’s why normalizing data is such a critical part of IT operations. Data normalization in IT incident management involves putting data from various tools into a standard format.

The primary objective of an IT incident response plan is to clarify roles and responsibilities, communication protocols, escalation scenarios, and technical steps to minimize further damage and safeguard business operations. The plan formally defines guidelines, procedures, and activities for identifying, evaluating, containing, resolving, and preventing IT incidents. Whether they cause intermittent errors or global service crashes, IT incidents can severely disrupt service quality and cause outages.

Effective IT event management is about more than restoring services. Managing and mitigating threats involves a comprehensive approach with five incident response phases: It’s crucial to take a structured approach to addressing disruptive events. Incident response involves multiple phases to minimize the impact and prevent service outages. An “incident” is any event that disrupts normal operations or threatens your information systems.

A runbook is a structured document detailing standardized procedures for completing routine IT operations processes. Runbooks are comprehensive guides that outline the steps and dependencies required to manage infrastructure, applications, and services within your IT operations. Runbooks bring order and organization to ITOps. These guides offer simple instructions for your team to handle challenges confidently and efficiently.

AIOps monitoring is a proactive process that uses AI to anticipate and identify IT infrastructure issues. Going beyond traditional troubleshooting, it enables your systems to detect anomalies in advance to prevent potential disruptions. AIOps uses advanced technology like AI and machine learning to simplify IT operations. AIOps monitoring collects and analyzes large data sets from diverse sources, such as logs, metrics, and events.

Generative AI has immense potential to transform how IT operations, service management, and infrastructure teams function. However, integrating GenAI technologies, like copilots, often brings significant challenges, such as ensuring accuracy, addressing job displacement concerns, and demonstrating tangible value. Navigating the landscape of various vendors and implementation hurdles can be time-consuming and resource-intensive.