Three shifts CIOs should prioritise to prepare their networks for the next generation of enterprise workloads.
Something has quietly changed about enterprise networks in India. For years, the conversation was about bandwidth and uptime. Keep the pipes running, make sure email works, don’t let the VPN drop during a town hall. That was enough.
It isn’t anymore.
AI workloads, cloud-native applications, and the rapid growth of Global Capability Centres are fundamentally changing what enterprise networks need to do. According to Broadcom’s 2026 State of Network Operations report, while 99% of organisations have cloud strategies and are actively adopting AI, only 49% say their networks can actually support the bandwidth and low latency that AI requires. That’s a staggering gap. And for CIOs operating in India’s fast-expanding GCC ecosystem, where the market is projected to grow from roughly $70 billion to over $130 billion by 2033, it’s a gap that directly affects how quickly you can scale operations, onboard global workloads, and deliver on the promise of your capability centre.
The network is no longer just infrastructure. It’s becoming the execution layer for AI, the connective tissue across multi-cloud environments, and the backbone of distributed operations that span campuses, branches, home offices, and data centres. If your network wasn’t designed for this, it will hold everything else back.
This blog explores three shifts that CIOs and IT leaders should prioritise to make their networks ready for what’s coming next.
Shift 1: Rethink Bandwidth and Latency Planning for AI Inference
Most enterprise networks were built for human-paced traffic. Someone opens a browser, loads a dashboard, joins a video call. The traffic is predictable, bursty in familiar ways, and manageable with standard capacity planning.
AI changes the equation entirely. Machine-generated traffic doesn’t follow the same patterns. A single AI-powered feature, say an inference engine running customer support or a real-time fraud detection model, can trigger millions of additional requests per hour. Those requests are heavier than traditional web traffic, require higher concurrency, and demand GPU-accelerated compute on the other end. The traffic doesn’t sleep. There are no off-hours.
For GCCs running AI workloads out of India, whether it’s model fine-tuning, inference at scale, or feeding data pipelines across geographies, this creates a very specific problem. The network needs to handle massive east-west traffic, support high-speed switching at 400 to 800 GbE, and maintain predictable, low-latency pathways for tightly coupled GPU-to-GPU and GPU-to-storage communication. Legacy three-tier architectures simply cannot deliver this.
What this means practically is that CIOs need to start treating AI workloads as a fundamentally different category of application, not just a heavier version of what already runs on the network. That means mapping out where AI traffic originates, understanding how inference workloads create continuous and globally distributed demand, and planning for two to five times usage spikes instead of steady-state capacity. The organisations that get ahead of this will be the ones running dedicated AI fabrics alongside their existing network, using leaf-spine architectures in the data centre, and actively managing latency at every hop.
The ones that don’t will find their AI pilots stalling the moment they try to scale.
Shift 2: Redesign Network Segmentation for Hybrid and Multi-Cloud Environments
Here’s a reality most enterprises are living with but few have properly addressed: their workloads are scattered across on-premises data centres, private clouds, multiple public cloud providers, SaaS platforms, and edge deployments. Often, this hybrid environment wasn’t designed deliberately. It evolved. A team adopted AWS for one project. Azure made sense for another. The legacy ERP stayed on-prem. And somewhere along the way, the security model, which was built around a clear network perimeter, stopped making sense.
The numbers tell the story. Enterprises now run over 55% of their workloads across multiple cloud providers. Yet traditional firewalls and perimeter-based security can’t keep up with the complexity. When workloads move fluidly between environments, the attack surface expands, and every connection between on-prem and cloud, between one cloud and another, becomes a potential vulnerability. Data from Fidelis Security shows that 52% of IT leaders report troubleshooting difficulties stemming directly from poor visibility across their hybrid networks.
For GCCs handling sensitive data, regulated workloads, and mission-critical systems, this is not an abstract concern. It’s an operational risk.
The shift here is from coarse, perimeter-based segmentation to microsegmentation that follows the workload, not the physical infrastructure. Software-defined networking allows you to create secure, isolated segments across your entire hybrid environment. Zero-trust policies get applied at the workload level, so a container running in Azure gets the same security posture as a VM sitting in your on-prem data centre. Security boundaries travel with the application, regardless of where it lives.
Getting this right also means having a unified view. You need a single management plane that gives you consistent policy enforcement, correlated monitoring, and shared incident response playbooks across every environment. Without that, your security team ends up managing five different tools with five different dashboards, and the gaps between them are exactly where threats slip through.
Shift 3: Rearchitect the Campus-to-WAN Connection for Distributed Operations
The way people work has changed, and most campus and branch networks haven’t caught up. In a typical GCC or enterprise setup today, employees work from the office some days, from home on others, and occasionally from client sites or co-working spaces. Applications have moved to the cloud. Collaboration tools live in SaaS. And yet many organisations are still backhauling traffic from branch offices through a central data centre before routing it out to the internet. It’s like driving to your neighbour’s house by first going through the next city.
This legacy hub-and-spoke model was designed for a time when applications lived in the data centre and users sat at desks in the office. Neither is consistently true anymore. The result is latency that frustrates users, bandwidth waste that inflates costs, and a management burden that keeps network teams firefighting instead of improving the environment.
SD-WAN has been the go-to answer for this, and rightly so. It intelligently routes traffic based on real-time network conditions, prioritises critical applications, and provides direct internet breakout so cloud traffic doesn’t take unnecessary detours. But in 2026, standalone SD-WAN is no longer the full answer. With AI workloads generating new traffic patterns, with agentic workflows creating continuous cross-system dependencies, and with the security perimeter effectively dissolved, organisations need SD-WAN integrated into a broader SASE (Secure Access Service Edge) architecture.
SASE combines networking and security into a unified, cloud-delivered framework. Firewall, secure web gateway, zero-trust network access, CASB, and SD-WAN all converge into one platform where security follows the user and the application, not the physical location. For enterprises expanding GCC operations across multiple Indian cities, or managing distributed workforces that span Bengaluru, Hyderabad, Pune, and beyond, this is the architecture that makes consistent performance and consistent security achievable without exponential complexity.
The campus-to-WAN redesign isn’t about replacing cables. It’s about rethinking what connectivity means when your users, applications, and data are everywhere.
Where Should CIOs Start?
None of these shifts happen overnight, and they don’t need to. The practical starting point is an honest assessment of where your network stands today against where your business needs it to be in 12 to 18 months.
Start by mapping your current traffic patterns. Where is AI-generated traffic already emerging, and what does it look like compared to your traditional workloads? Then look at your cloud architecture. How many environments are you running across, and do you have unified visibility and consistent security policies spanning all of them? Finally, audit your campus and WAN design. Is traffic flowing efficiently to where your applications actually live, or is it taking unnecessary detours that add latency and cost?
The answers to these questions will tell you which of the three shifts is most urgent for your organisation. For some, it’s the AI fabric. For others, it’s hybrid cloud segmentation. For many, it’s all three, tackled in phases.
What matters is that the conversation moves from “our network works fine” to “our network is ready for what’s next.” Because the workloads aren’t waiting.
Ample, as a partner by your side, works with CIOs and IT leaders across India to assess network readiness and design infrastructure that supports AI, multi-cloud, and distributed operations at scale. If you’re evaluating where your network stands against these shifts, our team can help you build a clear, phased roadmap.
