For Engineers, AI-Powered Observability Mesh Delivers ‘Clarity Through Connections'

The views and opinions expressed are those of Sai Krishna Cheemakurthi and do not reflect the views of any organization.

The biggest challenge in managing enterprise data is shifting from collection to interpretation. For years, organizations captured as much data as possible. But in large, complex systems, that approach often creates more confusion than clarity, leaving engineers to spend more time sorting through data than learning from it. Now, observability is transforming in response. Instead of seeing everything, the goal is recognizing what truly matters.

That’s the problem Sai Krishna Cheemakurthi, Vice President and Lead Infrastructure Architect at U.S. Bank, is working to solve. As an invitation-only member of the Forbes Technology Council and a Senior IEEE Member, Cheemakurthi is a technical architect and engineering leader specializing in AI and enterprise-scale observability, with over a decade of experience designing resilient platforms for global financial institutions. For Cheemakurthi, the solution is "observability mesh": an intelligent framework that actively interprets the meaning behind signals.

"The goal of the observability mesh is clarity through connections," Cheemakurthi said. In a recent Forbes Technology Council article, he explained how the mesh utilizes AI to unify an organization's data and identify hidden relationships—transforming millions of events into a handful of actionable insights with context.

But what about the impact on engineers? Cheemakurthi countered the common fear of replacement, explaining that AI’s role is to amplify human intuition, not replace it.

• Intuition, amplified: By freeing up teams from the tedious work of manual data correlation, the approach allows them to focus on bigger-picture decisions and amplify their expertise. "AI does not replace human intuition in observability. It amplifies it," Cheemakurthi said.

• Simple principle: Instead, he proposed a human-in-the-loop validation process with a simple principle: "AI suggests, engineer confirms."

Next, Cheemakurthi outlined a blueprint for putting the framework into practice. It all starts by tying every data point to customer impact and SLOs, he explained. From there, it requires shift-left instrumentation, a data correlation fabric to connect the three pillars of observability (logs, metrics, and traces), AI enrichment that can predict anomalies, human-in-the-loop validation, and finally, governance and cost optimization.

• Noise versus truth: The framework's integrity hinges on strong governance, Cheemakurthi continued. Without it, observability can descend into a "chaos situation" at scale. "Governance in observability is about intent, integrity, and impact. It defines what we collect, how we correlate it, and how we are accountable for acting on the insights. Without governance, AI observability becomes noise. With it, it becomes a trusted source of truth."

For Cheemakurthi, this elevates observability from a technical concern to a board-level priority. In his view, it's the engine that connects system reliability directly to business outcomes. "Observability is no longer a DevOps luxury. It is a business resiliency enabler," he said.

Ultimately, that changes how organizations value information, Cheemakurthi concluded. "The future of observability is not about creating more dashboards; it's about fewer dashboards with smarter insights." In this model, raw data is a commodity. The real value is the meaning derived from it. Or, as he put it, "Context is the new currency of observability."