Operations | Monitoring | ITSM | DevOps | Cloud

The Business Case for AI-Driven Observability in Network Operations

Modern network operations generate an extraordinary amount of telemetry. Metrics, logs, events, topology data, cloud signals, and service context all contribute to a richer picture of system behavior. As environments expand across cloud, data center, edge, and SaaS, the opportunity for operations teams is clear: when that telemetry is unified and understood in context, it becomes a powerful source of resilience, efficiency, and business insight.

How AI-Driven Automation Solves Patch Management Silos

"We see 10,000 critical vulnerabilities!" "We patched everything last week!" This conversation happens in enterprise IT departments every single day. Security teams present dashboards filled with red alerts. IT teams show deployment reports at 98% success. Both teams are looking at real data. Both are absolutely correct. And both are totally blind to what's actually happening across the endpoint environment. This isn't a people problem — your teams aren't incompetent.

AI Didn't Kill the SDLC. It Made It Harder to See

Whilst AI has compressed the visible stages of software delivery; requirements, validation, review and release discipline have not disappeared. They have been pushed into automation, runtime and governance. The real risk is not that the lifecycle is dead, but that organisations start acting as if accountability died with it.

90% AI Adoption. Still Failing. DORA Explains Why.

AI adoption is nearly universal. So why are most teams still struggling? In this session from GitKon, Nathen Harvey, head of DORA at Google Cloud, shares findings from the 2025 DORA State of AI-Assisted Software Development report, drawing on data from nearly 5,000 developers worldwide. The answer isn't more AI. It's what surrounds it.

Understand session replays faster with AI summaries and smart chapters

Datadog Session Replay gives teams a video-like view of what real users experienced in their applications. Engineers rely on replays to connect errors and slowdowns to actual user behavior, while product managers use them to understand friction and improve critical flows. But finding the right replay and the right moment often means manually scanning long sessions without knowing whether they contain relevant signals.

From Reactive to Proactive: AI-Driven Automation for Shopify Infrastructure Monitoring

Operations teams manage Shopify infrastructure with their eyes half-open most days. You're monitoring system health across multiple layers, responding to alerts when they fire, and hoping you catch problems before customers notice. The whole setup is reactive by design. Something breaks. You get paged. You investigate. You fix it. But here's what most ops leaders don't realize: your Shopify operation generates enough signals to predict problems hours (sometimes days) before they actually occur. The data's there. You're just not analyzing it at the right scale or speed.