As artificial intelligence moves from pilots into operational, mission‑critical use, legacy network architectures increasingly fail to meet new traffic patterns. Continuous inference, agent‑to‑agent communication and real‑time data pipelines generate persistent, unpredictable load that static, rigid networks were not built to handle. Consequently, the network has become an active control layer that directly affects AI performance, reliability and cost.
The infrastructure gap between ambition and reality
This transition is forcing organizations to rethink long‑standing assumptions. A Cisco study finds that 80% of executives believe their company’s competitive survival will depend on agentic AI, and consumer AI usage is already widespread. Yet a Bloomberg study commissioned by Tata Communications, The Future‑Ready Enterprise, reports that while three in four leaders (75%) treat AI as a board‑level priority, nearly two‑thirds (65%) of enterprises still run on transitional or legacy infrastructure. That mismatch between ambition and current infrastructure is a primary barrier to realizing value from AI investments.
Performance requirements have shifted by orders of magnitude
Performance expectations have changed dramatically: traditional business applications tolerated 100–500 milliseconds of latency, whereas mission‑critical AI workloads now commonly require latency below 10 milliseconds. Kapil, Vice President, Global Network Services at Tata Communications, says this constitutes a fundamentally different performance paradigm that invalidates many conventional network design assumptions.
Network performance drives AI reliability and cost
The gap between what legacy infrastructure delivers and what AI demands makes network performance a direct driver of AI reliability and total cost. Treating the network as a best‑effort transport layer introduces risks that often only become visible after a deployment underperforms in production. A model intended for real‑time fraud detection or supply‑chain optimization can be rendered ineffective if network congestion delays the data it depends on — and every millisecond of delay can carry a measurable financial or operational cost, Kapil warns.
He adds that many organizations underestimate the complexity of using the public internet as a global enterprise network: performance may appear acceptable within one country, but once data crosses borders or connects to international cloud platforms, the lack of end‑to‑end control becomes an operational barrier.
Distributed AI increases complexity and broadens the attack surface
Complexity grows as AI components are distributed across cloud, edge and enterprise environments. Organizations tend to prioritize compute and data infrastructure while overlooking the network fabric that ties them together; that blind spot frequently becomes a performance bottleneck, especially due to high‑frequency east‑west traffic between GPUs.
Distribution also expands the surface organizations must defend. Applications, users and partner ecosystems now span cloud, SaaS, edge and device environments. Kapil notes that AI‑driven malicious bots account for roughly 37% of online traffic, complicating the task of distinguishing legitimate users from automated threats. Many enterprises have responded by stacking siloed tools, which has produced fragmentation, inconsistent security and a lack of unified visibility rather than a coherent defense.
Kapil highlights Secure Access Service Edge (SASE) as a mitigation: "SASE helps mitigate these risks by converging networking and security into a unified, cloud‑delivered architecture. This convergence enables consistent policy enforcement across cloud, on‑premises and edge environments, while supplying the scalability and proximity needed to secure real‑time AI‑driven interactions."
The network must evolve from passive transport to an intelligent layer
Closing the gap requires far greater visibility into how AI traffic moves across distributed environments and the ability to steer workloads accordingly. Kapil argues that the network must be managed as an active, intelligent platform foundational to the entire AI stack, with real‑time observability into traffic flows and control to orchestrate workloads across the most efficient and secure available paths.
That intelligence distinguishes merely connecting systems from unlocking new capabilities — for example, a seamless shopping experience during peak sales or a global sports broadcast without buffering. It also changes infrastructure teams’ work: networks become software‑defined and API‑driven, shifting teams from incident response to designing systems that prevent outages.
"Instead of manually re‑routing traffic during an outage, the team must define the rules, policies and business outcomes for an intelligent fabric," Kapil says. "The network itself then executes those policies automatically and autonomously."
Practical implementations: Tata Communications’ approach
Tata Communications applies these principles with its IZO Data Centre Dynamic Connectivity platform. The software‑defined service creates a “self‑healing, intelligent network” using deterministic multi‑path routing to reroute traffic automatically within seconds during disruptions.
The company states that the platform converts resilience into an autonomous capability, delivering predictable, low‑latency performance required by mission‑critical AI applications while reducing operational costs by up to 30%.
Delivering this level of intelligence in practice requires giving critical workloads dedicated capacity rather than letting them compete for shared bandwidth. It also demands that enterprises define performance in deterministic terms — for example, committing that latency for a specific workload will not exceed 10 milliseconds 99.999% of the time — and adopt dynamic scalability so networks can absorb rapid shifts in demand without sacrificing performance or wasting resources on massive overprovisioning.
Tata Communications is collaborating with Amazon Web Services (AWS) to build one of India’s largest AI‑ready networks, connecting major AWS infrastructure locations in Mumbai, Hyderabad and Chennai. Such high‑capacity, resilient backbones are intended to accelerate generative AI adoption and cloud innovation across the region. The company also points to consumption‑based models where software enables bandwidth and network functions to scale instantly with demand, so organizations pay for usage while protecting performance during spikes.
Treat the network as a strategic investment
CIOs and infrastructure leaders are advised to reframe the network not as a cost center but as an insurance policy for AI investments. An intelligent network de‑risks those investments by:
- enabling dynamic scalability and removing the need for inefficient overprovisioning;
- strengthening security and governance through the visibility needed to protect data and models;
- providing a flexible, programmable foundation that can absorb future compute demands without a full architectural overhaul.
Achieving this does not require starting from scratch. Choosing partners with proven track records is critical: Tata Communications was named a Leader in the Gartner Magic Quadrant for Global WAN Services for the 13th consecutive year, a recognition the company cites as evidence of its vision and execution. The vendor continues to invest in SASE capabilities for AI‑driven security and high‑capacity 800G services aimed at AI‑scale infrastructure.
Kapil recommends a phased approach: assess the current network state, identify inefficiencies, then prioritize upgrades in AI‑ready technologies, seamless data exchange and advanced security. "Treating the network as a business enabler rather than overhead gives organizations the scalable, secure and resilient infrastructure the AI economy will continue to demand," he says.
Final note
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