The essential shift every ITOps leader must make to survive an unrelenting stream of incidents
High-profile IT incidents are becoming more frequent and more severe.
A single hour of downtime for a revenue-generating service could cost large enterprises between $100,000 and $249,999. Even that figure — from an IDC analyst brief — may be too low when accounting for customer churn and lost productivity. It also fails to consider the growing toll that incident management is taking on first responders.
“A single hour of downtime for a revenue-generating service could cost large enterprises between $100,000 and $249,999… [and] fails to consider the growing toll that incident management is taking on first responders.”
Given the increasing volume of incidents and the complexity of today’s IT infrastructures, modern incident management demands an AI- and automation-enabled approach. Without it, those working on the front line are subject to a continuous stream of outside-of-business-hours interruptions. This eats away at time that should be spent resting, increasing burnout, and reducing resilience.
However, many organizations still rely on traditional incident management, consisting of manual processes built for a simpler, less demanding era. This leaves IT operations teams, or ITOps, to sift through complex IT infrastructure to hunt for root causes and toil away on repetitive tasks.
The result is slower response times, but if organizations use machines to do more of the heavy lifting and manual toil, they could put their responders in a much better place. ITOps teams must embrace AI and automation to keep pace with the volume of modern incidents and IT complexity.
How to modernize incident management with AI and automation
Consider the following four methods for how AI and automation can transform incident management workflows:
1. Automate repetitive, low-risk response tasks
Automation reduces the time required to detect, diagnose, and resolve issues, thereby lowering incident management costs. Repetitive, low-risk tasks for SEV 1 or SEV 2 incidents are particularly well suited to automation, including automated alerts that reduce response times by rapidly notifying the relevant subject matter expert, and automated runbooks that provide context, diagnostics, and root cause analysis. Automation can also trigger common remediation steps, such as restarting a service or clearing a cache.
By automatically tracking key metrics, such as time saved or the number of errors reduced, ITOps managers can build a business case to adopt automation initiatives more widely. This is especially important for building momentum and gaining senior-level buy-in.
2. Deploy generative AI capabilities
Generative AI, or GenAI, is great at finding and summarizing important information from disparate sources. In doing so, it saves incident responders significant time in their day-to-day work, which might otherwise be spent sifting through logs and metrics. Incident triage summaries, including suggested investigation paths, provide incoming responders with the knowledge they need to hit the ground running. These could include contextual information from relevant previous incidents to apply targeted fixes more quickly.
Other contextual information that GenAI can retrieve might include recent changes and new or updated runbooks, which serve as a living knowledge base for future responders. Teams can also use GenAI to automatically create post-incident reviews from relevant chat transcripts, logs, action items, and other data. Taken together, these capabilities work to unlock data from enterprise silos and transform it into a clear narrative to improve communication and decision-making.
3. Use AI agents to add proactivity
AI agents are also changing the game for ITOps leaders by autonomously completing tasks to achieve specific goals, allowing human team members to move up the value chain. While GenAI chatbots generate and summarize content based on prompts, agents work independently to execute entire workflows.
They can proactively handle repetitive tasks and routine incidents by searching for runbooks, pulling key information from relevant tools, assessing prior incidents, and recommending remediation steps. Crucially, agents go beyond “if-then” logic to choose the right action from several possible options based on historical and current context. This means ITOps can move faster, and team members have more time to focus on strategic decision-making and problem-solving.
Before AI agents reach their full potential, leaders need to establish strict guardrails to minimize risk and keep humans in the loop for complex or high-risk cases.
4. Use AI agents to handle operational logistics
Organizations can also enhance coordination by applying AI agents to handle operational logistics. By delegating tasks to agents, human responders can devote more time and effort to incident resolution rather than to manual coordination between teams. These tasks could include drafting executive summaries and status updates for stakeholders, surfacing operational data into an incident channel, scribing during an incident conference bridge, and orchestrating workflows.
AI agents can also dynamically allocate incidents to the most relevant subject matter experts. By embedding these capabilities directly into communications tools like Slack, teams can coordinate and resolve incidents more efficiently without context switching.
Making change stick
Modern incident management has to keep up with complex, always-on infrastructure and an unrelenting incident stream. Optimizing for faster detection, smarter prioritization, and streamlined remediation is now essential. AI- and automation-enabled incident management makes that possible, cutting noise and toil, improving decision-making, and helping teams respond faster and with greater confidence.
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