If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

UK IT leaders are reaching a critical inflection point in how they manage observability, according to research from LogicMonitor. As infrastructure complexity grows and AI adoption accelerates, fragmented monitoring environments are driving organizations to rethink their operational strategies and consolidate tools ...

For years, many infrastructure teams treated the edge as a deployment variation. It was seen as the same cloud model, only stretched outward: more devices, more gateways, more locations and a little more latency. That assumption is proving costly. The edge is not just another place to run workloads. It is a fundamentally different operating condition ...

Having more observability data doesn't guarantee better insight. Without a refined alerting strategy, more data means more noise. The teams that sleep well at night aren't the ones with the most dashboards; they're the ones with the clearest alerting logic. Here is exactly how the best ones do it ...

In 2026, AI-native automation is fundamentally reshaping telemetry pipeline management. As a result, around 80% of configuration tasks currently hand-built by enterprise teams, whether they tackle security or pull insights from observability, will be automated. This transforms the roles of those teams from builders to strategic drivers. The acceleration of this shift was made possible by the alignment of several elements, namely the convergence in the standardization of OpenTelemetry, rapidly maturing AI, increasing competition between platform choices, and economic pressure ...

While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed ...

Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...

Enterprises are not stalling because they doubt AI, but because they cannot yet govern, validate, or safely scale autonomous systems, according to The Pulse of Agentic AI 2026, a new report from Dynatrace ...

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

For years, the success of DevOps has been measured by how much manual work teams can automate ... I believe that in 2026, the definition of DevOps success is going to expand significantly. The era of automation is giving way to the era of intelligent delivery, in which AI doesn't just accelerate pipelines, it understands them. With open observability connecting signals end-to-end across those tools, teams can build closed-loop systems that don't just move faster, but learn, adapt, and take action autonomously with confidence ...

In APMdigest's 2026 Observability Predictions Series, industry experts offer predictions on how Observability and related technologies will evolve and impact business in 2026. Part 9 covers Observability of AI ...

In APMdigest's 2026 Observability Predictions Series, industry experts offer predictions on how Observability and related technologies will evolve and impact business in 2026. Part 7 covers Observability data ...

In APMdigest's 2026 Observability Predictions Series, industry experts offer predictions on how Observability and related technologies will evolve and impact business in 2026. Part 6 covers OpenTelemetry ...

In APMdigest's 2026 Observability Predictions Series, industry experts offer predictions on how Observability and related technologies will evolve and impact business in 2026. Part 5 covers APM and infrastructure monitoring ...

AI continues to be the top story across the industry, but a big test is coming up as retailers make the final preparations before the holiday season starts. Will new AI powered features help load up Santa's sleigh this year? Or are early adopters in for unpleasant surprises in the form of unexpected high costs, poor performance, or even service outages? ...

In APMdigest's 2026 Observability Predictions Series, industry experts offer predictions on how Observability and related technologies will evolve and impact business in 2026. Part 4 covers user experience, digital performance, website performance and ITSM ...

In APMdigest's 2026 Observability Predictions Series, industry experts offer predictions on how Observability and related technologies will evolve and impact business in 2026. Part 3 covers more predictions about Observability ...

In APMdigest's 2026 Observability Predictions Series, industry experts offer predictions on how Observability and related technologies will evolve and impact business in 2026. Part 2 covers predictions about Observability and AIOps ...

The Holiday Season means it is time for APMdigest's annual list of predictions, covering Observability and other IT performance topics. Industry experts — from analysts and consultants to the top vendors — offer thoughtful, insightful, and often controversial predictions on how Observability, AIOps, APM and related technologies will evolve and impact business in 2026 ...

Developers building AI applications are not just looking for fault patterns after deployment; they must detect issues quickly during development and have the ability to prevent issues after going live. Unfortunately, traditional observability tools can no longer meet the needs of AI-driven enterprise application development. AI-powered detection and auto-remediation tools designed to keep pace with rapid development are now emerging to proactively manage performance and prevent downtime ...