As AI capabilities continue to evolve, AI is becoming central to managing the growing complexity of distributed, hybrid enterprise environments, enabling more effective analysis, correlation, and automation across interconnected systems. Traditional infrastructure and specifically network monitoring approaches, often built around siloed tools and static thresholds, struggle to keep pace with the scale, velocity, and interdependencies of modern systems. Further blurring the boundaries between network, application, and infrastructure domains makes it harder to isolate root causes and maintain operational resilience. In this context, AIOps platforms have emerged as one response to the growing need for integrated observability, automation, and data-driven decision-making. At AI Field Day, Selector AI presented an AIOps platform, which can be considered a foundation for co-creating more adaptive and data-driven network operations. Rather than positioning it purely as a product choice,...
A survey of 504 DevOps practitioners finds 63% working for organizations that will be making additional investments in observability over the next two years, with 21% describing those investments as significant.
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