Operations | Monitoring | ITSM | DevOps | Cloud

6 Ways Ops Teams Can Align AI With Business Impact

AI adoption is at an all-time high, withover 70 percent of organizations are using AI in at least one core function. Despite the high rate of AI adoption, many operational teams continue to have difficulty answering the question 'Is AI actually benefiting our business?' The challenge lies in the gap between AI systems and actual business results. Bridging the gap requires aligning operational AI with revenues, customers, and growth metrics. Here are actionable steps to transform AI from a technical tool into a measurable business contributor.

How Autonomous Technologies Are Streamlining Financial Operations for Modern Businesses

Modern businesses are under constant pressure to move faster, reduce costs, and stay compliant in a shifting regulatory landscape. Financial operations sit at the center of that pressure. Tasks like invoicing, reconciliation, reporting, and forecasting have traditionally required heavy manual effort. That is starting to change. Autonomous technologies are stepping in to handle routine processes, reduce errors, and free teams to focus on higher value work.

Route OTel data from AI apps to ClickHouse and Datadog using Observability Pipelines

As organizations continue to heavily invest in AI and build more agentic workflows, their telemetry data volumes can surge quickly, and the associated costs can become unpredictable. To regain control of their data, many AI-forward teams are turning to high-throughput, low-latency pipelines to collect and route data to tools such as OpenTelemetry (OTel) and ClickHouse. But these self-hosted solutions come with drawbacks.

Auto-Generate Tests for Your Codebase with AI (CircleCI Chunk Tutorial)

AI coding tools help you ship features faster than ever, but test coverage often can't keep up. In this video, we show you how CircleCI's Chunk autonomous CI/CD agent finds untested code in your codebase, writes tests to cover it, and opens a pull request for your review. What you'll learn: Chunk works directly inside your CI/CD pipeline, giving it access to your build history, test results, and coverage reports. That means smarter tests, not just more tests.

Sentry Built AI Dashboards: Monitor Your AI Agents End-to-End

Building AI applications? There's a lot more to monitor beyond errors. With tracing enabled, Sentry's built-in AI Dashboards give you deep visibility into how your agents are actually performing. This video walks through three key dashboard views: You'll also see how to drill from a dashboard widget straight into the trace explorer to pinpoint the root cause of errors, how to duplicate and customize dashboards to fit your needs, and how to set up monitors with alert thresholds - like getting notified if your LLM calls exceed 20 seconds.

In the Age of AI, Taste Isn't About Aesthetics

AI can generate a UI in seconds. So what do designers actually bring to the table? Marcela, Principal Product Designer at Rootly and former Founding Designer at Ramp, has spent 20 years in design. Her answer: taste isn't about aesthetics or crafting pleasant interactions. It's about asking the uncomfortable questions, and choosing the right problem, not the easiest one.

The Edwin AI Agent Orchestrator: Coordinated Incident Investigation Across the Tools You Already Use

Edwin AI’s Agent Orchestrator keeps incident investigation, context, and response aligned as work moves across tools, eliminating the manual handoffs that slow resolution. Every major incident has two timelines running in parallel. The first is the incident itself—services degrading, users affected, business impact accumulating. The second is quieter and just as costly: engineers switching tabs, re-explaining context to new responders, moving notes from one tool to another by hand.