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Technology / SaaS
March 18, 2026
Technology / SaaS

Tectonic Shifts in India’s Enterprise Telecom: The 50% Migration from Core Connectivity to Cloud-Led Managed Services and AI-Ready Infrastructure

Analyzes enterprise telecom evolution, highlighting shift from connectivity to cloud and managed services, rising role of hyperscalers, and increasing importance of platform-led, multi-cloud network architectures.

35 Mins
Former Director
India
Public
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Companies Discussed
Alphabet (GOOGL), Amazon (AMZN), Bharti Airtel (BHARTIARTL), Equinix (EQIX), HDFC Bank (HDFCBANK), ICICI Bank (ICICIBANK), Jio (RELIANCE), Kotak Bank (KOTAKBANK), Megaport (MP1), Meta (META), Microsoft (MSFT), Oracle (ORCL), Sify (SIFY), Tata Communications (TATACOMM), Uber (UBER), Vodafone (VOD), Zomato (ZOMATO)
Executive Summary
Topics Covered
Methodology
Free Preview — Executive Summary

This transcript examines the transformation of enterprise telecom from connectivity-led services to integrated cloud, security, and managed service ecosystems. Post-COVID demand has shifted toward SD-WAN, multi-cloud connectivity, and data center interconnect solutions, reducing reliance on traditional MPLS networks. Revenue mix has evolved, with connectivity now contributing less than 50% while value-added services drive growth. Key demand is concentrated in BFSI, GCCs, and digital-native platforms. Competitive differentiation increasingly depends on network adaptability, cloud integration, and service orchestration, with hyperscalers and specialized providers reshaping enterprise deal dynamics.

Topics Covered
  • Shift from MPLS connectivity to SD-WAN and cloud-based architectures
  • Growth of multi-cloud, SaaS, and data center interconnect services
  • Decline in connectivity revenue share vs rise of value-added services
  • Key enterprise segments including BFSI, GCCs, and digital platforms
  • Role of hyperscalers in shaping enterprise telecom demand
  • Competitive dynamics between traditional telcos and new-age providers
  • Evolution of enterprise contracts toward flexible, platform-based models
  • Emergence of orchestration, security, and managed services as key differentiators
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Q: Can you walk us through the current GPU allocation framework at your organisation? How are you deciding between internal AI workloads and enterprise customer commitments? A: Sure. So the fundamental tension right now is that our internal AI teams — the ones building our own foundation models and inference services — are consuming GPUs at a rate that nobody anticipated even 18 months ago. We're talking about 3-4x the original projections. And that creates a real squeeze on what's available for enterprise customers. The allocation committee meets weekly now, which tells you everything. It used to be quarterly. We have a scoring matrix that weighs revenue potential, strategic importance, and internal capability gaps. But honestly, internal teams almost always win because the economics of our own AI services are so compelling compared to renting compute to enterprises...

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Expert Profile
Former Director at Global Cloud Xchange
Duration
35 Mins
Call Date
April 7, 2026
Geography
India
Transcript Tier
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