From Silicon Valley to Bharat: Global AI Infrastructure Principles for India’s Growth

Introduction: A Moment of Global Convergence

Artificial intelligence is undergoing a profound transformation. What was once confined to centralised cloud data centres and academic models is now being operationalised across real-world systems—from retail environments to fast food chains and smart factories. AI is no longer just a concept; it’s embedded into infrastructure and powering customer-facing experiences in real-time.

This shift has been made possible by the convergence of three foundational enablers: DevOps, observability, and edge computing. Together, they provide the backbone required to deploy, scale, and manage intelligent systems with speed, reliability, and visibility.

As this global transition unfolds, it presents a powerful opportunity for fast-growing digital economies like India to not only adopt but shape the future of infrastructure-grade AI. With a vast developer ecosystem and a rapidly digitalising market, India stands at the cusp of leapfrogging into a new generation of intelligent, distributed IT.

The Emerging Era of Edge AI and DevOps

A few years ago, deploying AI models meant spinning up GPU workloads in the cloud. Today, we’re embedding AI at the edge—in retail stores, restaurants, and warehouses. At IBM Watsonx Orders, our AI inference models run on edge clusters inside QSR (Quick Service Restaurant) locations, enabling real-time, low-latency interactions with customers. These deployments are closer to users, more cost-effective, and increasingly essential for a seamless user experience.

Supporting this evolution is a tightly integrated stack of DevOps practices, SRE disciplines, and observability instrumentation. We rely heavily on OpenTelemetry to trace model behaviour, monitor latency, and ensure performance at scale. Traditional CI/CD pipelines have also evolved to support multi-cluster, low-connectivity environments. 

Globally, organisations are moving toward distributed AI systems that prioritise speed, resilience, and cost-efficiency. This trend has profound implications for India’s IT roadmap.

Lessons from Scaling AI at the Edge

Scaling edge AI deployments for McDonald’s and Watsonx Orders has revealed key infrastructure lessons. Real-time inference systems are incredibly demanding—they require not only optimised models but also a robust platform to orchestrate and monitor them.

One of the biggest challenges is maintaining developer velocity across a complex AI deployment lifecycle. Tooling alone isn’t enough. What makes scale sustainable is a strong DevOps culture— one that brings together software engineers, MLOps experts, SREs, and platform engineers into tightly aligned teams. 

Observability has emerged as the backbone of reliability. Without deep insight into what models are doing in production, it’s nearly impossible to diagnose failures or ensure trust. We use trace-based instrumentation to understand the behaviour of each AI call—from prompt processing to model output—across every device.

Automation and platform consistency is key: whether you're managing 10 devices or 10,000, repeatability and resilience must be built into every layer.

The Opportunity for India

India is at an inflection point. With one of the world’s largest pools of cloud-native developers, India is already a global powerhouse in software engineering. What comes next is a transition from backend services and web apps to infrastructure-grade AI engineering.

Startups in India are making bold strides in AI, fintech, health tech, and logistics. These companies often operate in bandwidth-constrained, distributed environments—exactly the context where edge AI and DevOps best practices shine. By adopting edge-aware infrastructure models, Indian firms can lower operating costs, deploy faster, and serve remote geographies more effectively.

However, to truly lead in this space, India must invest in product-grade tooling, developer efficiency platforms, and deep observability early in the product lifecycle. The opportunity isn’t just to follow global trends—it’s to define a new model of digital-first infrastructure that meets the needs of emerging markets with scale, agility, and trust.

What Needs to Happen Next

For Indian organisations building AI-enabled systems, here are a few key imperatives:

• Invest in observability early: Integrating telemetry into AI systems from day one ensures visibility, reliability, and faster iteration.

• Build cloud-agnostic, edge-aware infrastructure: Don’t assume all inference or orchestration will happen in centralised clouds. Plan for hybrid environments.

• Adopt platform engineering and automation: Internal developer platforms (IDPs) can streamline AI/ML workflows and reduce operational toil.

• Engage with open-source ecosystems: Contributing to and adopting projects like Kubernetes, Prometheus, OpenTelemetry, Argo CD, and Apache Kafka can accelerate product maturity and innovation readiness.

• Strengthen partnerships and upskill talent: Academia, industry, and government must work together to train the next generation of platform engineers and DevOps leaders.

These efforts will not only enhance India’s technical edge but also build a more self-reliant and innovation-driven digital economy.

Conclusion

India has the scale, talent, and urgency to lead the next wave of AI infrastructure innovation. By embracing edge AI, DevOps, and observability as core principles—not afterthoughts—India can leapfrog into a new era of intelligent, resilient, and distributed IT. The path from Silicon Valley to Bharat is not one of imitation—but of transformation.

Written By -- Kunal Khanna, Head of Developmental Operations and Edge Platform at IBM Watsonx Orders, Recognised by Marquis Who's Who in the USA

Read More:

Challenges of Indian System Integrators: Adapting to Survive and Thrive

How Vendors Empowering System Integrators for AI & Data Transformation

The Evolving Challenges for India's System Integration Ecosystem

Check Point's India Channel Strategy for Cybersecurity Growth