BI-301g · Module 3

Automated Trigger Monitoring

3 min read

Trigger monitoring at portfolio scale requires automation — manual monitoring across hundreds of accounts is unsustainable. The automation strategy focuses on the stages with the highest automation potential: collection is 90% automatable through scheduled API calls, RSS feeds, and alert services; filtering is 70% automatable through AI-assisted relevance scoring; classification is 60% automatable through rule-based categorization with AI-assisted edge case handling; routing is 95% automatable through predefined routing rules based on classification output. The human analyst's role shifts from monitoring to reviewing — confirming automated classifications, refining filter criteria, and handling the ambiguous cases that automation cannot resolve.

Do This

  • Automate collection and routing first — these stages have the highest automation potential and lowest quality risk
  • Use AI-assisted filtering with human review of borderline cases — the AI catches the obvious signals; the analyst catches the subtle ones
  • Build feedback loops from human review into the automation — every correction the analyst makes improves the filter for next time
  • Monitor false positive and false negative rates weekly — a filter that misses real triggers is worse than a filter that generates false alarms

Avoid This

  • Automate everything and trust the output without review — automated monitoring without human quality control produces silent failures where important triggers are missed
  • Keep monitoring manual because "automation misses nuance" — manual monitoring at scale produces fatigue, and fatigue misses more triggers than automation does
  • Deploy automation without calibration — spend two to four weeks running the automated pipeline alongside manual monitoring to verify that the automation catches what the manual process catches