AI Shifts Incident Management From Reactive to Proactive

Maintaining seamless operations and delivering exceptional customer experiences are critical to any business. Yet, traditional IT incident management approaches often struggle to keep up with increasingly complex IT infrastructures. As the limitations of traditional, manual methods become more glaring, organizations need to consider how trusted AI-powered, intelligent automation can help them stay more competitive and better ensure uninterrupted business operations.

IT Operations and DevOps teams rely on incident management to respond to and resolve any number of unplanned events, from the mundane (Wi-Fi connectivity issues) to the profound (network downtime and cybersecurity attacks). Traditional IT incident management tools typically have manual processes to identify these incidents and react only after they have occurred, as well as to log and classify, contain, diagnose, remedy, and review them. As IT environments become larger and more complex, conventional approaches to incident management become untenable.

Imagine that a global business might have 10,000 servers across dozens of countries in addition to the networking devices connecting them, thousands of apps, databases, and other assets.  Manual processes are difficult to scale, often leaving teams overwhelmed and leading to delays in identification, prioritization, and resolution of incidents. What follows is significant downtime and disruptions to critical business processes, which can lead to revenue loss, customer dissatisfaction, and tarnished brand reputation.

Traditional incident management approaches are often also hindered by siloed data scattered across various systems and teams. That makes it challenging to gain comprehensive insights into the underlying causes of incidents and inhibits collaborate with other business units, slowing down incident resolution and increasing Mean Time to Repair (MTTR). Moreover, conventional approaches to managing incidents tend to rely on historical data and predefined thresholds, requiring manual inputs in order to detect and address issues. As a result, they lack the capability to anticipate and proactively prevent incidents before they impact the business. This reactive nature prevents organizations from achieving true operational resiliency.

AI Automates and Improves Incident Management

As IT organizations evolve, so too must their approach to incident management for the modern era of digital business. Adopting AI-powered incident management technology can allow IT organizations to:

Address Issues Proactively.

AI-based intelligent automation can detect anomalies, predict potential issues, and take proactive measures to prevent incidents before they impact the business. With the power of AI, the technology can provide valuable information that will help identify and solve future issues, potentially uncovering answers to questions that were never thought to be asked. Proactive maintenance or capacity adjustments also help enhance operational efficiency by reducing the likelihood of incidents occurring in the first place. This fundamental shift from reactive to proactive incident management enables organizations to maximize uptime, reduce revenue loss, enhance customer satisfaction, and safeguard their brand reputation.

See a Holistic Data View.

To combat siloed data and inefficient processes, companies can use AI and automation to correlate data from various sources and provide a comprehensive view of the entire IT landscape. When IT operations teams can identify the underlying causes of incidents quickly and collaborate effectively with other business units, they are able to expedite incident resolution. The result: a reduction in the Mean Time to Repair (MTTR) and improved overall operational efficiency.

Scale for Business Transformation.

It’s important for IT teams to have a platform that can handle the increasing volume and variety of incidents found in modern IT infrastructures. Intelligent automation makes it possible to analyze vast amounts of data in real-time. This scalability ensures that organizations can more effectively manage incidents, regardless of the size and complexity of their IT environment. By leveraging advanced analytics and machine learning algorithms, the platform can analyze vast amounts of data from disparate sources, enabling IT operations teams to gain more actionable insights and make data-driven decisions across incredibly complex environments.

Electrolux AB, for example, manages a large, global infrastructure of servers, devices, apps, and databases. The Sweden-based appliance manufacturer is turning to AI to automate a menial task that consumes 1,000 hours a year of employee time. Not only can the company recoup much of that time, but its employees’ expertise can be applied to more valuable, higher-level tasks, such as identifying new correlation criteria that can be fed to the AI system, or refining rules and actions based on local conditions. In this way, automation saves time that can be reallocated to improving the automation.

The need to better ensure uninterrupted business operations and maintain a competitive edge in the digital landscape demands a shift toward AI-based intelligent automation solutions. AI and AIOps solutions are fundamentally changing how IT professionals manage increasingly complex environments. By leveraging advanced AI and automation capabilities, organizations can better proactively detect, prevent, and resolve incidents, and position themselves for success.

Stay ahead of the competition, embrace the future of incident management and remember: If you’re reacting, that means the system is already broken.