How AIOps Is Transforming Observability in Cloud-Native Environments
Varma Kunaparaju, Senior Vice President/GM, Cloud Platform & OpsRamp at HPE, explains how artificial intelligence for IT operations (AIOps) is transforming how observability is applied across complex cloud-native computing environments.
Kunaparaju points out that while early AIOps relied heavily on pattern recognition and historical data, today’s transformer-based models and foundation models allow operators to get much closer to real root cause analysis—even in situations the system has never encountered before. That capability, paired with advances like eBPF and OpenTelemetry, is reducing the friction of instrumentation and enabling more consistent, usable telemetry across environments.
Observability, he argues, isn’t just the next iteration of monitoring—it’s a superset. Where monitoring sets thresholds and raises alerts, observability provides context, traces problems across networks and applications, and helps IT teams reason through why a failure occurred. The convergence of legacy monitoring, modern observability, and AI-driven insights is leading toward what Kunaparaju describes as a “digital operations command center”—a unified layer where IT can act more like a service provider, ensuring outcomes for the business rather than just tracking infrastructure health.
For organizations balancing legacy systems with new cloud-native applications, Kunaparaju advises an “integrate to consolidate” strategy: don’t rip and replace; instead, bring traditional instrumentation and modern observability stacks together, then gradually modernize. From the data center to the edge, the end goal is one operations console that can surface business-critical insights in real time.
