By Sanjiv Verma, Vice President, RUCKUS Networks, Asia Pacific, CommScope
Since 2025, AI has been high on the agenda of boardroom discussions across Delhi, Mumbai, Bengaluru, and Hyderabad. But now, in 2026, AI has moved out of the boardroom and into operational reality, not as a pilot, nor a proof-of-concept, but as the nervous system of any network infrastructure. With its vast, heterogeneous enterprise base, India is discovering that this move demands far more than off-the-shelf AI solutions.
Fueled by the rising number of industrial automation projects and IIoT deployments, and the need for reliable, secure, and scalable networks, the country’s industrial networking solutions segment is expected to grow in value from USD 12.8 billion annually in 2025 to USD 31.6 billion annually by 2031. These numbers reflect how Indian enterprises across manufacturing, energy and logistics sectors are not just scaling their digital infrastructure, but fundamentally rearchitecting how their networks think, respond, and self-correct.
Indian enterprises’ ambition received a powerful global endorsement in February 2026, when New Delhi hosted the India AI Impact Summit with the world’s leading AI companies converging and signaling India’s transformative journey of becoming a primary architect of AI movement.
More than peripheral curiosity, AI is increasingly embedded in how enterprises manage networks. Yet in India’s uniquely complex enterprise environment, fast-growing capabilities and multiplying use cases are only part of the picture.
The Reality in Indian Enterprises
Generalized AI models deliver a solid baseline for incident detection, root cause analysis, and KPI monitoring. This ‘80 percent’ utility has been a useful starting point; however, the remaining 20 percent is where the real challenge is beginning to emerge for Indian enterprises.
Consider the sheer diversity of Indian enterprise networks: a manufacturing conglomerate in Pune running private 5G on the shop floor alongside legacy operational technology (OT) systems or a private hospital chain in Chennai integrating IoT-enabled medical devices with secured Wi-Fi. These networks are not homogenous constructs; instead, each comes with unique KPIs, compliance obligations, and ways of operating. Due to its level of complexity, off-the-shelf AI is just insufficient.
Another example – India’s smart manufacturing industry is growing fast and is expected to be worth USD 21.5 billion annually by 2034. As more factories adopt advanced Industry 4.0 technologies—supported by government programs like Production Linked Incentive (PLI) and Make in India—they need AI systems that truly understand how their OT systems work. A specialized AI management solution that is properly trained in domain data and can proactively suggest and execute operational changes to deliver measurable reductions in cost, risk, and even carbon footprint, becomes a critical requirement.
The Infrastructure Moment: From Summit Pledges to Enterprise Reality
The India AI Impact Summit 2026 was a procurement signal for enterprise India. IT Minister Ashwini Vaishnaw announced plans to scale India’s national GPU capacity while projecting that cumulative AI investment could exceed USD 200 billion over the next two years. Microsoft’s USD 17.5 billion commitment to Indian AI infrastructure and Google’s USD 15 billion America-India Connect, all showcase deep investment plans to boost AI deployment and connectivity in India.
For enterprise network managers, these commitments are significant—they unlock greater sovereign compute capacity, lower latency, and a rapidly expanding ecosystem for AI inference. Yet infrastructure is only the starting point. The summit highlighted a crucial point; the most meaningful AI is domain specific, and there are several examples to support this. NPCI launched FiMI, an AI model for India’s payments ecosystem. Airtel demonstrated AI embedded directly into its telecom network management and data centre operations. The message from India’s enterprises reaffirmed that general-purpose AI is the starting point, not the destination.
The Cost Challenge Shaping Enterprise AI
As AI deployments scale, Indian CIOs are confronted with the reality that ongoing cost is not in training, but in answering millions of real-time queries from assistants and agentic systems. India’s enterprise agentic AI market is forecasted to reach $1.73 billion in value annually by 2030, with more than 80% of organizations in India already actively exploring autonomous agents. Given the scale of investment, enterprises must ensure AI delivers real ROI. The observation is especially resonant for Indian enterprise network environment, where strict regulations, multilingual interfaces, and hybrid OT-IT architectures demand exactly the kind of specialized, auditable AI that general-purpose models cannot deliver.
Indian enterprises must plan for Domain-Specific Language Models (DSLMs) or AI agents with deep networking knowledge which could predict outages or reroute traffic in real time, changing network management from reactive to proactive. Agentic AI trained on specialized network knowledge can simulate changes on a digital twin, propose RF and SD-WAN policy adjustments, and seek human approval before execution, all within safety guardrails. For Indian IT teams already stretched thin, this is not a future aspiration; it is rapidly becoming an operational requirement.
The Talent Gap Driving AI Imperative
India is one of the world’s largest STEM talent pools, yet 82% of Indian employers report difficulty filling AI-skilled roles. Industry estimates suggest AI-related job demand will cross one million positions by 2026, but as of today, only around 16% of IT professionals are AI-skilled. For a country where 51% of AI and ML roles remain unfilled, specialized AI network management is a structural solution. It will reduce the skill threshold for routine operations while freeing engineers for higher-order architecture work and deliver greater productivity.
This brings us to the question of build-vs-buy. Since late 2025, as global vendors began offering AI-native networking solutions in Asia, Indian startups and tech teams have been racing to build custom models. Meanwhile, whether a firm develops in-house or partners with a vendor, responsible AI remains an imperative. Indian policymakers has emphasized that ethical use and trust is critical for scaling AI.
2026: India’s Year of Specialization
The IndiaAI Mission’s decision to allocate 45% of its funding toward compute and data infrastructure has laid a strong national foundation. With this groundwork in place, the focus now shifts to enterprises. CIOs and network architects must answer a pivotal question: how do you transform abundant compute resources into true network intelligence?
The answer lies in specialization and AI that is tailored to your network’s topology, your industry’s operational rhythms, and your organization’s risk tolerance. Enterprises that move early will build compounding advantages in network performance, resilience and operational efficiency.