Scaling AI with Confidence: Visibility, Control & Efficiency

AI initiatives are driving a sustained surge in telemetry and log data across applications, infrastructure, cloud services, and new generative workloads. That growth creates immediate operational pressures: rising observability costs, fragmented toolchains, and blurred accountability when incidents involve models, data, and downstream business outcomes. For many organizations the question is not whether telemetry will grow, but how to live with that growth without losing control of cost, visibility, or compliance.

Organizations are responding through a mix of architectural, operational, and governance changes. Some are consolidating monitoring and log management to reduce duplication and simplify incident resolution. Others are introducing smarter retention and sampling policies to preserve high-fidelity signal where it matters while throttling low-value noise. Teams are also formalizing AI observability practices that connect model and data telemetry to business metrics, and are automating routine workflows so SRE and platform engineers can focus on emergent failures rather than signal triage.

Join industry peers to explore the trade-offs, governance choices, and operating practices that let teams support AI at scale without runaway costs or blind spots. We will discuss practical decisions you can make in the next quarter around telemetry retention, tool rationalization, and accountability for AI-driven outcomes. The sponsor will be present to share how they think about observability patterns and tooling at scale, and to listen to the hard questions you bring from the field.

Dynatrace is advancing observability for today’s digital businesses, transforming the complexity of modern ecosystems into powerful assets. By leveraging AI-powered insights, it enables organizations to analyze, automate, and innovate faster, driving their business forward.