Loading full navigation.

Autonomous IT is here. The alert loop ends now. LEARN MORE

Partners

Docs

LM Academy

LM Community

Pricing

Platform
Solution
Industry
Role
There is no result.

LOGICMONITOR BLOG

Incident management has outgrown its playbook. Here’s what comes next.

Incidents now unfold across more connected systems and at greater speed. Here’s what Forrester says IT leaders should rethink as AI takes a larger role in investigation, response, and recovery.

6–8 minutes
Margo Poda

NEWSLETTER

Subscribe to our newsletter

Get the latest blogs, whitepapers, eGuides, and more straight into your inbox.

SHARE

The quick download:

As incidents become faster and more interconnected, IT teams need stronger operational context, better governance, and a more deliberate approach to AI-assisted response.

  • Traditional incident response is struggling to keep pace with the speed and complexity of modern environments.

  • Observability and trusted operational knowledge give AI the context needed to investigate effectively.

  • Greater autonomy should follow confidence, starting with lower-risk work and expanding from there.

Modern incidents rarely stay neatly contained.

An application problem can cross cloud services, infrastructure, third-party dependencies, automation, and business systems before an engineer has finished assembling the initial picture. Software changes continuously, and the systems teams rely on to respond are becoming more interconnected themselves.

That complexity creates a number of problems for an incident management model still heavily dependent on humans manually gathering evidence, interpreting alerts, searching documentation, and determining what changed.

During our recent webinar, Incident Management Has Outgrown Its Playbook: How IT Leaders Are Adapting for the AI Era, Forrester Principal Analyst Julie Mohr joined LogicMonitor Vice President of Product & Solutions Marketing Andrew Keating to discuss how AI is changing that model—and what IT leaders need to get right before handing more operational work to agents.

One theme surfaced repeatedly: the opportunity is bigger than automating the incident process teams already have. And the underlying operating model needs to evolve with it.

The speed of failure is changing the incident response equation

Incident management has traditionally been organized around a familiar sequence: 

  1. Detect something.
  2. Determine its impact. 
  3. Investigate.
  4. Resolve the issue. 
  5. Restore service.

That workflow assumes the organization has enough time for people to work through those steps.

Increasingly, that assumption is under pressure.

Mohr described a widening gap between how quickly a failure can propagate through a modern environment and how quickly people can diagnose and respond to it. Distributed architectures, cloud dependencies, continuous software updates, and automated systems all increase the speed and potential blast radius of an issue.

Manual investigation still matters. But when the war room fills quickly and engineers are moving between alerts, logs, tickets, dashboards, changes, and runbooks, the response itself becomes a bottleneck.

Here is where AI can materially change the work.

AI agents can help correlate signals, collect diagnostic information, support triage, surface likely root causes, and retrieve relevant knowledge before an engineer would otherwise have assembled that context manually. The value is reducing how much manual interpretation has to happen before the team understands what is going on.

Better AI starts with better context

For AI to reduce that manual work, it needs enough operational context to understand what is happening and why.

An agent cannot investigate an incident well if all it knows is that an alert fired. It needs to understand what the affected resource supports, how it relates to other systems, what else changed recently, whether similar incidents have happened before, and what the organization already knows about resolving them.

Observability provides a critical part of that picture.

Mohr described observability as the context engine that helps agents understand what is actually happening across the environment. Telemetry provides the current signals. Historical incidents, problems, changes, and requests provide another layer of operational history.

Those sources have often lived in separate worlds. Monitoring and IT operations teams manage one side. Service management teams manage another. An alert eventually becomes a ticket, and humans bridge the gap between them.

AI makes that separation harder to sustain. An agent investigating an incident may need both the live operational picture and the historical record to understand what action makes sense. That brings observability and service management much closer together—and turns the quality of the connections between those systems into part of the AI strategy.

Knowledge is becoming part of the operational stack

The same is true for knowledge management. Organizations already have enormous amounts of useful operational knowledge. The problem is that much of it was created for humans.

An engineer might know which version of a runbook is current. They may remember who resolved a similar issue six months ago. They can recognize that “fixed it” in an old incident record hides several important steps.

Agents do not have that institutional shorthand.

For AI to work reliably, operational knowledge needs to be findable, current, trustworthy, and accessible within the right permissions. That means improving documentation where it is weak, reducing conflicting versions, capturing useful resolution details, and making institutional knowledge easier to reuse.

It also makes the people who hold that knowledge especially important.

The path toward more AI in operations depends on experienced practitioners contributing what they know, evaluating results, and helping determine where automation can be trusted. Treating AI primarily as a mechanism for removing people from the process risks undermining the participation required to make the technology useful in the first place.

Control should come before autonomy

The webinar also pushed back on the idea that organizations need to make a sudden jump from human-led operations to autonomous remediation. A more practical approach is to increase autonomy according to risk.

Mohr proposed looking closely at two factors: the potential blast radius of an action and how easily that action can be reversed.

Work with a narrow impact and high reversibility is a much better starting point for automation than a change capable of disrupting a critical production service.

That could include tasks such as alert routing, classification, diagnostic data collection, ticket creation, or routine updates. These activities consume time but generally present much less operational risk. From there, teams can expand what agents are allowed to do as confidence grows.

Governance therefore becomes part of the architecture. Organizations need clear controls around what data agents can access, which actions they can take, what requires human approval, and how those decisions are recorded. The objective is not maximum autonomy. Instead, it is giving AI enough authority to remove operational toil without giving it more authority than the organization can confidently govern.

Traditional metrics will not tell the whole story

The way organizations measure incident management will need to evolve as well.

Metrics such as mean time to detect and mean time to repair were built around a largely human operating model. They remain useful, but they may not fully capture what changes when AI begins taking on more investigation and response work.

Mohr suggested looking at signals such as alert quality, knowledge reuse, escalation patterns, and the share of work resolved with little or no support intervention.

That matters because many organizations are already experimenting with agents but struggle to demonstrate enough value to expand their use.

The cost of a new AI capability is straightforward to identify. Its operational impact can be harder to show if the organization is still measuring success entirely through the same framework it used before AI entered the process.

Teams need to establish what improvement looks like before they scale.

The bigger opportunity may be preventing incidents altogether

One of the most interesting parts of the discussion came when Keating asked Mohr what part of today’s incident management process might seem surprisingly manual three years from now.

Her answer moved beyond incident response. Many incidents begin with change. As agents become better at analyzing historical incidents, system dependencies, operational signals, and previous changes, AI can increasingly help teams understand the risk of a change before it creates an outage.

That insight opens a broader opportunity. Instead of applying intelligence only after something breaks, organizations can begin using it earlier—to identify risky changes, surface likely impact, and prevent avoidable incidents from reaching production.

Incident response will still matter. Modern environments are too interconnected, and too dependent on systems outside any single organization’s control, to eliminate failures entirely.

But the best incident may increasingly be the one an IT team never has to manage.

Start with the operating foundation

Organizations do not need to automate everything to begin modernizing incident management.

They can start by identifying the repetitive work consuming engineering time today. They can improve the quality of operational knowledge and make observability context available across workflows. They can establish permissions and governance before agents are allowed to take consequential action. And they can define how they will measure whether AI is actually improving operations.

Those foundations are what allow AI to progress from assisting an investigation to participating in it—and eventually, where appropriate, taking action.

Watch the on-demand webinar for the full conversation with Forrester Principal Analyst Julie Mohr and LogicMonitor’s Andrew Keating.

By Margo Poda

Sr. Content Marketing Manager, AI

Margo Poda leads content strategy for Edwin AI at LogicMonitor. With a background in both enterprise tech and AI startups, she focuses on making complex topics clear, relevant, and worth reading—especially in a space where too much content sounds the same. She’s not here to hype AI; she’s here to help people understand what it can actually do.

Disclaimer: The views expressed on this blog are those of the author and do not necessarily reflect the views of LogicMonitor or its affiliates.

© LogicMonitor 2026 | All rights reserved. | All trademarks, trade names, service marks, and logos referenced herein belong to their respective companies.

Related Blogs

Incident management has outgrown its playbook. Here’s what comes next.
Blog AIOps & Automation

Incident management has outgrown its playbook. Here’s what comes next.

Incidents now unfold across more connected systems and at greater speed. Here’s what Forrester says IT leaders should rethink as AI takes a larger role in investigation, response, and recovery.
October 23, 2026
Learn more
What Is an AI Dashboard? A Guide for IT Operations
Blog AIOps & Automation

What Is an AI Dashboard? A Guide for IT Operations

AI dashboards use artificial intelligence to surface relevant metrics, generate visualizations, and add context to complex data. Learn how AI dashboards work, how they compare with traditional dashboards, and what they can mean for IT operations.
October 20, 2026
Learn more
LogicMonitor Embeds Edwin AI Intelligence in ServiceNow with New Agent-to-Agent Capabilities
Blog AIOps & Automation

LogicMonitor Embeds Edwin AI Intelligence in ServiceNow with New Agent-to-Agent Capabilities

LogicMonitor and Work4Flow extend Edwin AI into ServiceNow with A2A, bringing alert context, metrics, root cause analysis, and diagnostic guidance into the incident workflow.
October 19, 2026
Learn more