AI-Ready Infrastructure Shows Enormous Potential, but Faces Scaling and Adoption Barriers

Tata Communications has announced plans to deploy an 18,000-Kilometer (km) fiber backbone with a total capacity of 7.2 Terabits per Second (Tbit/s) to interconnect AWS’s major data center regions in Mumbai and Hyderabad with edge infrastructure in Chennai. The US$50 million project, slated for completion by March 2026, will be one of Tata’s largest-ever network deployments in India and is explicitly positioned as an AI-ready network capable of supporting Generative Artificial Intelligence (Gen AI), high-performance computing, and data-intensive workloads.

What makes this noteworthy is not simply the scale, but the explicit co-design between connectivity and compute. Tata will provide high-capacity, ultra-low latency express routes across India, while AWS will deploy its custom networking stack to ensure security, availability, and workload-optimized performance. This marks a step beyond conventional data center interconnect projects: the partnership is designed to create a purpose-built environment for training and deploying Artificial Intelligence (AI) models.

The effort is also part of a larger global trend. In Europe, Neos Networks in the United Kingdom and Arelion in the Nordics are investing in deterministic transport for the cloud and AI, and in Asia, HKT in Hong Kong is launching similar improvements. These developments highlight that telcos are beginning to see AI not just as a workload to be carried, but as a driver of network design and investment strategy.

The significance of AI-ready networks lies in how they change the role of connectivity. Instead of being a neutral transport layer, networks become active enablers of automation, intelligence, and real-time decision-making. By delivering deterministic performance and tight integration with distributed compute, they have the potential to resolve one of the biggest historical barriers to edge AI adoption: inconsistent and unpredictable connectivity. Nevertheless, while the benefits of AI-ready networks listed below are compelling, they remain more aspirational than realized. Current deployments are early-stage, vendor-led proofs of concept, with little evidence of repeatable, large-scale enterprise adoption.

• Moving from Reactive to Predictive and Autonomous Operations: By providing ultra-low latency and consistent data flow, networks can support AI systems that can not only react to events, but can predict them. These capabilities allow for predictive maintenance, real-time operational changes, and even non-human operated autonomous processes to occur.
• Process Change Based on Industries: Networks that are AI-ready provide the opportunity to automate processes unique to that domain like AI-based visual inspection in manufacturing, dynamic rerouting of autonomous vehicles in logistics, and immediate imaging analysis at the bedside in healthcare.
• New Ecosystem Dynamics: Hyperscalers, telcos, and applications vendors may need tightly-aligned operating models. Businesses will have to create shared Service-Level Agreements (SLAs), orchestrate for integrated outcomes, align their commercial terms, and be able to translate deterministic performance into enterprise value.
• Changing the “Return on Investment (ROI)” Lens: Any business case for AI-ready networks cannot be based on bandwidth and latency. Adoption will necessitate measurable protection from errors, downtime, throughput speed, or cost recovery.

Despite the potential, many challenges are currently limiting progress. Multi-vendor integrations are still nascent, making it difficult for enterprises to adopt AI-ready networks across heterogeneous environments. Data fragmentation further undermines AI precision and consistency, particularly when workloads move across edge, cloud, and sovereign environments. Commercial models between telcos, hyperscalers, and application providers are also unsettled, including questions about how revenue will be shared and who owns the customer relationship. Finally, much of the industry's marketing around AI-ready networks is ahead of operational delivery, leading to a crisis of credibility with enterprise buyers.

For telco operators, the way forward begins with sharpening their focus on their Total Addressable Market (TAM). AI-ready networking theoretically applies to all industries, but practically the highest-value opportunities exist in a small number of mission-critical verticals where deterministic networking has a direct impact on operations. Manufacturing, logistics, healthcare, utilities, and critical infrastructure, including oil & gas, represent the most immediate near-term TAM as their AI use cases—predictive maintenance, real-time routing, bedside diagnostics, grid management, and offshore automation—require high-reliability and ultra-low latency connectivity. These also tend to be slow, capital-intensive procurement cycles, with relatively aligned investment cycles. In taking a vertical-specific go-to-market tack, operators can optimize value from each deployment, develop referenceable case studies, and gain better visibility on the economics of scaling their AI-ready networks.

Similarly, it is also important to embed governance, orchestration, and workload lifecycle management into the AI-ready proposition from the outset. Enterprises in mission-critical sectors will not spend money on networks that only offer speed or bandwidth. They need to know that sovereignty, compliance, and operational continuity have been built into the solution. Consequently, operators should approach data governance, compliance frameworks, and orchestration tools as part of the AI-ready network, not as value-added features. With such an approach, operators are not only a connectivity provider; they are a trusted partner for securely and compliantly deploying AI. With this idea, operators remove friction for enterprise customers and provide a greater likelihood of adoption, virtualizing AI performance outcomes with the operator's connectivity infrastructure.