Operations | Monitoring | ITSM | DevOps | Cloud

This article kicks off a 4-part series on leveraging AIOps to provide a more efficient, cost- and resource-saving, reliable, and agile IT infrastructure.

Many organizations approach Autonomous IT with the assumption that adding more tools, more data, or more automation will eventually produce self-governing operations. This assumption creates the illusion of progress. Complexity does not resolve itself when new systems are layered on top of existing ones. In most environments, each new tool adds another interpretation of the truth, which compounds the cognitive load on teams and forces more reconciliation, not less.

The 2025 Gartner Market Guide for Event Intelligence Solutions reflects a shift in how IT operations leaders evaluate AI-driven technologies. As AI hype gives way to more practical evaluation, we are seeing a natural departure from broad promises about AI capabilities toward clearly defined use cases and outcomes.

IT operations teams deal with growing system volume, faster release cycles, and tighter service expectations. Traditional monitoring tools generate large alert volumes, yet teams still spend hours sorting signal from noise under pressure. Manual triage slows response time, while disconnected tools stretch incident handling and service recovery.

Enterprise Management Associates found that major IT service outages are increasing in cost, frequency, and duration, with unplanned downtime costing large enterprises nearly $25,000 per minute, or $1.5 million per hour. When every minute costs $25,000, you can’t afford to waste engineering time on coordination tasks like creating channels, paging experts, typing summaries, and posting updates. An agentic AI-powered incident assistant can eliminate that waste and reduce bridge call costs.