Report by SolarWinds
Observability Trends 2026: Where IT Lags and How AI Moves IT Forward
Key Findings
45% of IT professionals use AI to accelerate root cause analysis
45% of IT professionals use AI to predict capacity and performance issues
42% of IT professionals cite skills gaps as a barrier to fully operationalizing AI in observability
55% of IT professionals report using too many monitoring and observability tools
45% of IT professionals use AI to reduce alert noise and fatigue
64% of IT professionals say unified observability across all layers of the IT stack is very important to their team's success
47% of IT professionals cite security concerns as a barrier to fully operationalizing AI in observability
90% of IT professionals express confidence in AI’s ability to improve monitoring and observability operations
41% of IT professionals cite complexity of technology as a barrier to fully operationalizing AI in observability
77% of IT professionals cite limited visibility across on-premises and cloud environments
75% of IT professionals say lack of coordination between teams (e.g., network, infrastructure, applications, and database) hinders effective observability
47% of IT professionals use AI to automate incident prioritization
37% of IT professionals cite employee reluctance or resistance as a barrier to fully operationalizing AI in observability
33% of IT professionals cite budget constraints as a barrier to fully operationalizing AI in observability