Incident management engineers are extremely busy people, and the “busy-ness” is only worsened when they are forced to spend their working hours resolving incidents that are high volume, but low priority. The downside of this constant workload is that when a major incident hits, incident management teams are already tired, stressed and potentially unavailable. These factors combine to delay incident resolution, which can hurt revenue.However, there is an alternative model organizations can take to reduce manual toil for engineers.AI- and automation-supported incident management tools are becoming much more common and can break the cycle of manual response to constant, low-priority incidents. To avoid any business disruption, it is crucial that organizations quickly identify which processes and workflows are safe to resolve with AI and automation, and which still need a human to lead.Understanding Incident PriorityThe first step is to gain an in-depth understanding of incident categorization and prioritization. The industry-standard approach is to categorize incidents on a scale based on their priority. This typically ranges from P1 to P5, but could also be SEV-1 to SEV-5 (with SEV standing for severity). P1s are considered the most potentially damaging incidents, while P5s sit at the bottom of the scale.From most to least severe, incidents must be categorized based on their impact on both the organization and customers. Above all, when categorizing incidents, organizations must always assume the worst to ensure incidents are fully resolved.P1 should be reserved for critical issues that warrant public notification and liaison with executive teams. These incidents result in large-scale customer impacts, including severely impaired functionality in breach of SLAs. These top-priority incidents may also expose customer data and must be rapidly contained.Similarly, P2s are critical system issues that affect many customers’ ability to use a product. These can include web app unavailability or performance degradation for most, or all, users.P3 incidents are minor issues for customers that require immediate attention from service owners. If these are left untreated, they can escalate into P2s.P4 is used to denote minor issues that require action, but do not affect customers’ ability to use the product. These can be performance issues, individual host failures or delayed job failures.Finally, P5s are the lowest-priority incidents. These include cosmetic issues or bugs, but do not affect a customer’s ability to use a product.P1 and P2 represent major incidents. Whenever one of these occurs, human-led remediation must be the default, and comprehensive incident response processes with a human in the loop must be triggered to avoid severe reputational or financial damage. However, engineers often spend their time responding to low-severity incidents, which still require manual intervention, such as raising tickets before an issue is resolved. These manual workflows present a major opportunity for organizations to introduce AI and automation to allow engineers to focus on high-priority work.The Automation and AI AdvantageWhile AI and automation capabilities are becoming more common in operations management tools and platforms, they must deliver meaningful benefits to engineers to deliver value. Human-led remediation will always have a role to play in incident management, particularly for severe and high-priority incidents. However, operations management tools can be used to stop the cycle of engineers manually chasing P5s every time they occur.When an issue is detected, AI tools can be used to reduce noise for responders by suppressing duplicate or low-priority alerts. This ensures engineers are able to focus solely on actionable events, allowing them to focus their time strategically on higher-priority fixes. Leading operations management platforms also include AI operations (AIOps) features to automate the early stages of each incident, including triage, noise reduction, alert grouping and change correlation. Relieving engineers from the burden of these workflows directly reduces alert fatigue while also improving operations via more streamlined incident detection.Automation tools can also be used to improve incident response and remediation. For example, runbooks can be tied to AI systems so that common issues, such as restarting a failed service or scaling resources, are resolved without human intervention. The increasing availability of agentic AI tools will also help reduce engineers’ workloads, autonomously managing routine tasks to reduce operational costs and speed up incident resolution.Automation can also be used to enhance observability across an organization’s entire stack. The process provides an additional system for analyzing contributing factors and helps engineers identify correlations across multiple systems. Engineers can also use AI tools for triage by linking signals across logs, traces and metrics. Together, these capabilities help engineers quickly pinpoint the contributing factors to an incident without needing to manually search across multiple parts of their system.AI tools can even bring value during post-incident learning reviews. Generative AI (GenAI) capabilities support content creation for incident summarization or to generate timelines of incidents for faster post-mortems.All of these use cases demonstrate the value of AI and automation in supporting engineers to resolve incidents in a more time-efficient manner.Free Your Engineers To Build ValueAI and automation are the future of operations management. Put simply, engineers can’t be expected to manually resolve issues across the entire incident management pipeline. They need the support of tools that can reduce their toil.By offloading monitoring, troubleshooting, scaling and routine operational tasks to AI, organizations will help their engineers spend less time firefighting and more time focusing on high-value work. This shift reduces burnout, improves service reliability and increases operational efficiency, all while helping to improve the engineer’s day-to-day experience.The post Stop Chasing Low-Stakes Incidents: Let AI Do It appeared first on The New Stack.