AI in Incident Response: The Leftover Principle and the New Human Role
The more AI automates routine incident response, the more critical human expertise becomes for the strange, ambiguous failures that remain. That was the central paradox explored during Uptime Labs' Incident Fest, a discussion that pulled together engineers from Chime, Rootly, and Uptime Labs itself. The conversation, later written up by Craig Risi for InfoQ, landed on a sharp conclusion: automation does not retire the human responder. It changes the job into something harder.
AI can now summarize incident channels, analyze unfamiliar code, suggest remediation steps, generate pull requests, and assist with diagnosis. That removes significant cognitive load from responders. But it also carries a cost. Automation may erode skills, situational awareness, and decision-making capabilities. The discussion framed this through what Uptime Labs calls the Leftover Principle: as automation takes over routine tasks, humans are left with unusual, ambiguous, and difficult problems.
Incident response is particularly vulnerable to that principle. Engineers may encounter fewer routine incidents and get less practice. The risks pile up quickly. Remaining incidents become harder. Human skills atrophy. Responders lose situational context. And an accountability gap emerges when no one fully understands what the system did.
The Ironies of Automation
The Leftover Principle is not new. It echoes decades of research into the ironies of automation, a body of work that has long warned about the hidden costs of handing control to machines. The Incident Fest discussion drew directly on that lineage, arguing that the same dynamics now apply to AI-assisted incident response.
The discussion also cited researcher J. Paul Reed, whose work examines how AI diagnostic recommendations affect human performance. The findings are stark. When AI recommendations are correct, human users perform substantially better than they would without AI. When the recommendations are wrong, human performance degrades significantly compared with using no AI at all. The lesson, according to the discussion, is to understand when AI output can be trusted, how to challenge it, and when humans need to take control.
That is easier said than done. The discussion stressed that organizations must be deliberate about introducing AI. They cannot simply bolt it onto existing workflows and hope for the best. They need to know where AI helps, where it hurts, and where it quietly shifts responsibility onto humans who may not be ready for it.
NIST Flags Monitoring Gaps
The concerns extend beyond individual incidents. The National Institute of Standards and Technology (NIST), the U.S. government agency known for research and standards, identified related problems in its 2026 research on monitoring deployed AI systems. NIST highlighted insufficient research into human-AI feedback loops, difficulty scaling human-driven monitoring, and the ongoing balance between automated and human-validated monitoring.
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Those findings matter because incident response depends on monitoring. If organizations cannot effectively watch their AI systems, they cannot catch failures early. And if the feedback loop between humans and AI is poorly understood, then the Leftover Principle becomes even more dangerous. The 2026 NIST research effectively confirmed what the Incident Fest participants were saying from the field: the human-machine boundary is not well mapped, and that is where incidents will hide.
AI Changes the Volume of Change
The discussion also looked at how AI-assisted development may increase the volume of software created and changed. That is not a neutral observation. Incident frequency is influenced by two factors: the volume of changes and the probability of failure per change. AI could increase the volume of changes entering production. At the same time, the quality of AI-generated code, tests, dependencies, and configurations will determine the failure probability.
That combination could push incident rates in either direction. More changes mean more chances for failure, even if each individual change is less likely to break something. The discussion argued that established engineering practices become more important than ever. Deployment controls, observability, feature flags, automated testing, resilience engineering, and rapid rollback mechanisms all move from best practices to survival tools.
The objective should not necessarily be to prevent every AI-generated mistake. That is neither realistic nor desirable. Instead, the objective should be to detect mistakes quickly, contain them effectively, and reverse them safely. That framing shifts the burden from perfect prediction to fast recovery, which is exactly where incident response already lives.
The Responder Role Evolves
AI could fundamentally change the role of the incident responder without eliminating the need for them. That was the through-line of the entire Incident Fest discussion. Engineers may become responsible for rare, ambiguous, high-consequence failures that sit beyond automated diagnosis. These are the leftovers. They are the incidents that do not fit the patterns, the ones that require judgment, context, and the ability to question the machine.
Organizations may need to invest in game days, simulations, tabletop exercises, chaos engineering, and regular incident-response practice. These are not optional extras. They are the training ground for the new human role. If routine incidents disappear, responders lose the repetition that builds instinct. Deliberate practice becomes the only way to keep those instincts sharp.
The discussion did not offer a simple answer. It offered a warning and a direction. AI will take the routine work. Humans will get the strange, hard, high-stakes failures. The teams that prepare for that shift will cope. The teams that do not will find themselves accountable for systems they no longer understand.