FROM THE INDUSTRY
And that’s the visibility gap. The biggest challenge in observability is the visibility gap. It’s not enough to know that systems are connected—you need to understand how they communicate and the critical dependencies that sit between them. Many outages occur outside a customer’s own infrastructure, whether across telecom networks, cloud providers or SaaS platforms, making them difficult to identify using conventional monitoring tools. Our platform addresses this by combining infrastructure context with advanced telemetry and triangulation. Once an issue is detected, our Pulse module correlates data from our global monitoring network with the customer’s own environment to determine the true root cause, rather than simply reporting where the symptoms appear. This approach also identifies the real blast radius of an incident—showing which services, users and dependencies are actually affected—so teams can prioritise the right response. Instead of spending hours proving where a fault lies or coordinating between multiple providers, customers receive actionable intelligence that pinpoints the source of the issue and significantly accelerates resolution.
How is AI helping you perform better in this area? We’ve been applying machine learning for more than six years and now process over 18 trillion data points a day. Rather than using AI to fill gaps in understanding, we use it to automate complex tasks and simplify the customer experience. Because our platform already understands infrastructure relationships and dependencies, AI accelerates workflows. For example, our AI-powered asset builder enables customers to create entirely new asset types in around a minute— something that can take months with traditional platforms. A healthcare provider, for instance, can simply ask the platform to add MRI scanners as managed assets. AI automatically identifies the relevant attributes, manufacturers and models, creating a ready-to-use asset class. Customers then decide how to import devices and what to monitor, from performance and availability to cybersecurity. This approach enables organisations with complex operational technology to onboard specialised assets quickly and identify issues in seconds rather than hours.
Fast forward a few years, where do you see this going? Presumably if you’ve done your job right, there won’t be a need for WanAware in the future. In an ideal world, there would be no need for a platform like WanAware. But as organisations continue to adopt more cloud services, applications and interconnected infrastructure, complexity is only increasing, and we don’t see that changing anytime soon. When outages occur, businesses typically have two options: rely on highly skilled Tier 3 and Tier 4 engineers, who are both expensive and in short supply, or equip their existing teams with better intelligence and automation. We built WanAware to do the latter. The platform isn’t designed to replace experienced engineers; it’s designed to amplify their expertise. By providing context, identifying root causes and highlighting the real impact of an incident, it enables teams to resolve issues much faster and more efficiently. As infrastructure becomes increasingly distributed and interconnected, giving engineers the tools to work smarter—not simply harder—will become even more important.
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SEPTEMBER 2026 Volume 48 No.3
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