Operations | Monitoring | ITSM | DevOps | Cloud

Frontline Truths: 100+ Network War Stories on the Path to Autonomous Operations - Eric Chou

The path to intelligent network operations isn’t a straight line. In this session from AI for Network Leaders – Powered by Selector, Eric Chou shares hard-earned lessons from over 100 conversations with network engineers and operators navigating automation, complexity, and the shift toward AI-driven operations. He covers: This session is a practical field guide for teams looking to move from reactive firefighting to building an AI-ready network foundation.

You Don't Have an AIOps Problem-You Have a Data Opportunity - Michael Wynston

AI can’t fix bad data. In this session from AI for Network Leaders – Powered by Selector, Michael Wynston breaks down a critical truth: the success of AIOps depends on the quality, consistency, and trustworthiness of your network data. Using real-world lessons from Fiserv’s large-scale network transformation, he explores how teams can build a strong data foundation that enables AI to deliver meaningful, low-noise outcomes.

Inside the AI Agents Transforming Network Operations - Joby Rudolph & James Schnebly | Selector

AI agents are becoming a core part of modern network operations — but what does it actually take to build and deploy them effectively? In this session from AI for Network Leaders – Powered by Selector, Joby Rudolph and James Schnebly break down how AI agents are designed, implemented, and applied in real-world network environments. They cover: This session provides a practical look at how AI agents are moving from concept to production — and what it takes to make them work at scale.

From Tools to Teammates: A Practical Framework for AI Agents in Network Operations - Du'An Lightfoot

AI agents are quickly moving from experimentation to real-world deployment in network operations — but how do you adopt them without introducing unnecessary risk? In this session from AI for Network Leaders – Powered by Selector, Du’An Lightfoot shares a practical framework for building and deploying AI agents in production network environments. He covers: This session cuts through the hype and provides a clear, actionable model for teams looking to move from AI as a tool to AI as a teammate.

Beyond the Dashboard: Selector's Patented Approach to Conversational Observability

For years, IT operations teams have been trapped in a frustrating paradox: the data they need to solve critical issues is right at their fingertips, yet entirely out of reach. Accessing it requires engineers to master complex, platform-specific query languages, dig through endless layers of dashboards, and hunt for the exact visualization that holds the answer. Under the intense pressures of modern speed, scale, and complexity, this rigid model is breaking down.

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.

Solving the Ticket Noise Problem: What We Learned from Our ServiceNow Webinar

On March 18th, we hosted a session focused on a challenge that continues to undermine even the most mature IT operations teams: ticket noise. It’s easy to dismiss noise as just “too many alerts”. But as we explored in the webinar, the real issue runs deeper. Ticket noise is a symptom of something more fundamental — a lack of correlation, context, and shared visibility across the stack.

Cloud Observability Is Broken - Hybrid Operations Need a New Intelligence Model

Cloud adoption was supposed to simplify operations. Infrastructure would become programmable, scalability would become elastic, and distributed architectures would enable resilience at global scale. In practice, cloud has delivered extraordinary flexibility, but it has also introduced a level of operational complexity that traditional observability approaches were never designed to handle.

Full-Stack Observability Is Becoming a Business Imperative

As enterprises accelerate digital transformation, technology performance has become inseparable from business performance. Customer experiences, revenue streams, and operational efficiency increasingly depend on the reliability of complex, distributed systems. In this environment, full-stack observability is no longer a technical aspiration — it is a strategic necessity.