India’s Rise in Observability and AI: New Relic’s Engineering Outlook

Full-stack observability is becoming essential as enterprises move to complex, distributed and AI-driven systems. Traditional APM is no longer enough to ensure reliability or deliver the seamless digital experiences customers expect. In this interview, Ved Antani, SVP Engineering and MD India at New Relic, explains how observability is evolving, why AI is central to its future, and how India’s engineering ecosystem is emerging as a global innovation driver.

Application Performance Monitoring, or APM, has long helped enterprises keep software reliable by tracking performance, tracing user journeys, and spotting bottlenecks. As observability expands beyond APM into a full-stack discipline, how do you see this shift influencing the way enterprises build and manage technology in the next few years?

The shift from relying solely on Application Performance Monitoring (APM) to embracing full-stack observability has fundamentally changed how enterprises approach reliability. Full-stack observability is a unified, platform-centric approach that addresses the complexity of modern, distributed architectures, many of which leverage multi-cloud and microservices.

This shift is moving engineering teams from being reactive to predictive. Full-stack observability unifies telemetry data and removes blind spots that lead to costly outages. For organisations that implement full-stack observability, the median cost of a high-impact outage drops by 50%, from $2 million to $1 million per hour. This tangible financial impact—and the ability to maintain superior digital experiences—will make full-stack observability a non-negotiable foundation for building and managing technology. While 73% of organisations globally still lack full-stack observability, this gap is where competitive advantage will be won or lost.

India is increasingly seen as more than a cost-efficient talent hub, with engineering centres now driving innovation at scale. From your perspective leading the Hyderabad Innovation Center, how can India move from being a contributor to becoming a true driver of global product strategy?

India’s evolution from a cost-effective talent hub to a strategic innovation engine is complete and irreversible. For the country to become a true driver of global product strategy, it requires full ownership and autonomy within local engineering centres. This means teams aren’t merely contributing code but helping to define product roadmaps, owning global features, and pioneering new product innovations.

A powerful example of this shift is the deep, independent work emerging from New Relic’s Hyderabad and Bangalore offices, especially in the AI space. This high level of trust allows teams to focus entirely on customer-first innovation at a global scale. The market confirms this momentum: New Relic’s 2025 State of Observability Report found a very high adoption rate of observability, with 73% of Indian organisations already using AI monitoring capabilities—far exceeding the APAC average. This shows India is not just adopting but actively leading the development of cutting-edge, AI-strengthened observability worldwide.

Mobile service providers face constant pressure to deliver uninterrupted experiences to millions of users, where even short outages can affect customer trust. How can intelligent observability help telcos anticipate issues earlier, reduce downtime, and link operational stability to customer experience?

For telcos, uninterrupted service is the foundation of trust. Intelligent observability directly addresses this pressure by leveraging AI to move operations beyond threshold alerting and into true predictive analytics. Rather than waiting for an outage to hit, intelligent systems correlate massive volumes of telemetry data—from infrastructure and network metrics to real user monitoring (RUM)—enabling telcos to forecast failures before they impact service level objectives (SLOs).

This allows telcos to automatically trigger remediation workflows such as container restarts or load balancer reconfigurations, effectively enabling self-healing systems. Crucially, the latest systems connect operational data directly to digital customer experience. This link ensures engineering efforts are always prioritised based on their financial and reputational impact, transforming stability from a cost centre into a core competitive advantage that directly strengthens customer loyalty.

AI and automation are now central to enterprise technology roadmaps. In the context of observability, how do you see AI being applied to cut through alert fatigue, speed up root cause analysis, and ultimately help teams focus on higher-value innovation?

AI and automation are foundational to solving the scaling crisis created by modern complexity. In observability, AI’s primary role is to filter noise, provide context, and accelerate efficiency. Practitioners surveyed in the 2025 Observability Forecast cite reduced alert fatigue (59%) and faster root cause analysis (RCA, 58%) as the top benefits they see from AI implementation.

GenAI and AIOps are especially useful in two areas. Firstly, alert reduction: AI can automatically group and correlate millions of disparate events into a few consolidated, actionable issues, cutting through the fatigue that leads to missed critical alerts. Secondly, AI is dramatically accelerating RCA. New Relic’s capabilities—such as AI-assisted troubleshooting and automatic RCA, cited as the most impactful features by executives—immediately provide context by correlating logs, traces, and metrics across the full stack. By automating detection and diagnosis, engineering talent can shift its focus from reactive maintenance to higher-value activities, namely building and deploying new, innovative products.

As enterprises embrace multi-cloud, edge computing, and open-source stacks, complexity is becoming the norm. What do you see as the biggest challenges for the observability ecosystem as a whole, and where can companies like New Relic play a differentiated role in addressing them?

The biggest challenge is perhaps the unmanaged proliferation of technology complexity and tool sprawl, driven by multi-cloud adoption, serverless functions, and edge computing. Fragmentation prevents engineers from achieving a unified, correlated view of their systems. In fact, nearly half of global organisations struggle with complex technology stacks, and many still deal with an average of four separate monitoring tools.