AI promises to revolutionise everything, including operations. After a year of experimentation, we’ve found where it genuinely helps and where it’s still hype. Spoiler: it’s not replacing engineers anytime soon.
The Problem
Alert fatigue persisted despite tuning. Hundreds of alerts daily, mostly noise. Engineers developed blindness, occasionally missing genuine issues among the false positives. Traditional correlation rules helped but couldn’t adapt to changing patterns.
Log analysis at scale was impractical. Millions of log lines during incidents. Engineers searched with keywords and intuition. Relevant entries often went undiscovered, extending resolution time.
Documentation queries consumed time. “How do I configure the VPN for the staging environment?” required finding the right wiki, hoping it was current, and parsing dense technical content.
Our Solution
Anomaly detection in metrics moved beyond static thresholds. ML models learn normal patterns for each service and alert on deviations. Seasonal variations, gradual trends, and deployment-related changes are understood automatically.
Log clustering groups similar entries. During incidents, instead of scrolling through millions of lines, we see grouped patterns. “These 50,000 entries are variations of the same error” focuses investigation.
Conversational documentation access through AI assistants. Engineers ask questions in natural language; the assistant retrieves relevant documentation and synthesises answers. Still experimental but promising for common queries.
Incident summarisation generates initial incident reports. The AI ingests alerts, communications, and timeline data, producing a first draft that engineers refine. Reduces post-incident documentation burden.
Code review assistance for infrastructure configurations. AI catches common mistakes in Terraform, Kubernetes manifests, and CI pipelines. Not replacing human review but providing an additional check.
The Benefits
Alert volume reduced through intelligent correlation. Similar alerts group together. Known patterns suppress automatically. Engineers see fewer, more meaningful notifications.
Incident resolution accelerated when AI surfaces relevant information quickly. Log clustering and anomaly detection point investigators toward causes rather than requiring manual correlation.
Knowledge accessibility improved. Junior engineers find answers faster through conversational interfaces. Tribal knowledge encoded in documentation becomes discoverable.
Documentation burden decreased for incidents. AI-generated drafts capture timeline and technical details, letting engineers focus on analysis and lessons learned.
AI in operations isn’t about replacement—it’s augmentation. The technology handles tedious pattern matching, letting engineers focus on judgment and creative problem-solving.