Operations | Monitoring | ITSM | DevOps | Cloud

Operations has always been a headcount game. More systems mean more people, with human judgment as the irreplaceable element at the end of every alert chain. This fundamental relationship between complexity and operators has defined how we’ve built and run operations infrastructure for decades. But modern product velocity and complexity outpace any organization’s ability to hire and train operators.

Businesses across industries have fully embraced AI, looking to 10x productivity and supercharge profits. Most companies—78%, according to McKinsey—use AI in at least one business function. But a recent survey by IBM found that only 1 in 4 AI pilots brought about the ROI leadership expected. Even fewer (16%) had been scaled across organizations. The gap is real. Many AI efforts remain stuck in pilot mode or isolated at the edges of businesses.

AI’s Operational Complexity Crisis is Real The AI revolution is transforming how we build and operate software, but it’s also creating a perfect storm of operational challenges that are keeping engineering teams up at night.

Automation has transformed incident response within individual teams. Diagnostic scripts, runbooks, and alert systems help engineers troubleshoot and resolve issues more efficiently. Translating those gains across the organization remains a challenge. Most automations are built in silos and not designed to work together. The result: disconnected workflows, inconsistent outcomes, and too much manual effort, leaving teams with less time for the strategic work that drives innovation and resilience.

Something fundamental needs to change in the way we run operations. Organizations are deploying AI to optimize everything from coding and deployment to resource planning and incident management. But they’re discovering that managing AI-powered systems requires a completely different operational mindset. AI models hallucinate. Data pipelines degrade silently. Algorithms develop bias without warning.

Too many incidents waste valuable engineering time on the basics: collecting logs, pulling system data, and tracking down the right person to fix the issue. Meanwhile, customers experience delays, SLAs are breached, and critical work gets pushed aside. The real kicker? Those L3 and L4 severity incidents that could actually prevent future fires get labeled as “nice to have” and collect dust in your backlog. Automated diagnostics and triage eliminates these bottlenecks.

Summer is in full swing, and we’ve been busy cooking up some exciting updates to make your operations life easier (and less stressful). This quarter has been all about bringing AI agents into the mix to handle the heavy lifting—whether that’s fixing those pesky recurring issues automatically or surfacing the exact context you need when something totally new breaks. We’re excited about the impact this will have on your day-to-day operations.

State and local governments face an increasingly complex challenge: delivering reliable digital services to the public while operating under tighter budget constraints and reduced federal funding. As taxpayers demand more efficient operations, government leadership must ensure every technology purchase can show clear return on investment (ROI) value.