5 Ways IT Leaders Are Using AI to Improve Operations in 2026

As AI-generated code and autonomous agents accelerate software delivery, organizations face growing operational complexity, incident risk, and cost pressures that can undermine AI’s return on investment. PagerDuty argues that AI is most effective when applied selectively to operational resilience, citing examples from Intuit, Roche, Cursor, and its own platform: predicting deployment risk from historical incident patterns, mapping hidden service dependencies to assess incident blast radius, automating triage and diagnostics with governed SRE agents, providing real-time incident documentation and stakeholder updates, and generating post-incident analyses. These approaches combine automation with human oversight, feedback loops, permissions, and domain-specific knowledge to reduce toil while maintaining trust and accountability. By using incident data to improve future decisions and prevent recurring failures, engineering teams can shift effort away from repetitive firefighting toward building more reliable systems.

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