AI E-commerce Needs Better Networks: Lessons from India

A lot of South African e-commerce teams blame “the algorithm” when AI personalisation falls flat—recommendations lag, chatbots time out, fraud checks spike false declines, and dashboards don’t update until the customer has already bounced.

Most of the time, the problem isn’t the model. It’s the plumbing.

Megaport’s acquisition of Extreme IX in India is a useful reminder that AI-powered digital services live or die on connectivity choices. By buying India’s leading Internet Exchange operator, Megaport instantly gained seven Internet Exchanges, access to 40+ data centres, and connections to 400+ customers across metros like Mumbai, Bengaluru, and Delhi. Megaport also accelerated its India entry by nearly three years. Those are business numbers, not networking trivia—and they map cleanly to what South African online retailers and digital service providers are trying to do with AI.

What Megaport’s India move actually signals (and why SA should care)

Answer first: This acquisition signals that the next wave of growth in digital commerce comes from reducing latency and complexity, not just adding more cloud services.

When a company buys Internet Exchange capacity and data centre reach, it’s not doing it for fun. It’s doing it because:

• Traffic is concentrating in metros where cloud regions, payment switches, and consumer eyeballs sit.
• Latency is a revenue metric for checkout, search, recommendations, and customer support.
• AI workloads are hybrid by default (some in cloud, some in data centres, some at the edge).

Megaport’s plan is to integrate Extreme IX into its global Network as a Service platform and roll out services like cloud connectivity, data centre interconnects, virtual edge, and compute. That’s basically a blueprint for how to support modern AI products: connect everything privately, route intelligently, and keep performance predictable.

For South African businesses, the lesson is straightforward: if you want AI to scale, you need to engineer the network path your AI depends on—from customer device to storefront, to payment provider, to cloud AI endpoint, to your data.

The hidden cost of “good enough” connectivity

Here’s what I’ve found: teams will spend weeks fine-tuning prompts and model settings while the customer experience is quietly being sabotaged by avoidable network issues.

Common symptoms in AI-powered e-commerce and digital services in South Africa:

• Chat and support AI that feels “dumb” because it can’t pull customer context quickly
• Personalisation models that update slowly due to batch data movement and congested links
• Fraud and risk scoring that causes higher declines during peak shopping periods
• Product search that returns stale results because indexing pipelines lag

The fix isn’t always “buy more bandwidth.” Often it’s better peering, smarter routing, and getting critical services closer together—which is exactly what Internet Exchanges and multi-data-centre reach are designed to enable.

Why AI in e-commerce is so sensitive to latency

Answer first: AI adds more “hops” to every customer interaction, and each hop punishes slow or unreliable network paths.

Classic e-commerce was already multi-step (browse → cart → payment → confirmation). AI adds layers:

• A recommendation call during browsing
• A vector search query for product discovery
• A fraud model call at checkout
• A customer data lookup for segmentation
• A support chatbot pulling order status and policies

Even when each call is “only” a few hundred milliseconds slower than it should be, the combined delay becomes noticeable. And noticeable becomes expensive.

The three AI moments where milliseconds matter

1) Product discovery (search + recommendations)
If your AI search relies on embeddings and vector databases hosted in cloud, but your catalogue data sits somewhere else, you’re inviting lag. Lag reduces pages viewed per session. That reduces conversion.

2) Checkout (fraud + payment orchestration)
Fraud tools increasingly use AI scoring and device signals in real time. If your scoring service is slow, you either:

• add checkout friction (drop-offs), or
• loosen rules (more chargebacks)

Neither is acceptable during peak season.

3) Customer support (AI agent + back-office systems)
An AI agent is only as good as the systems it can reach. If it can’t retrieve order, delivery, or refund context quickly, it becomes a scripted FAQ machine—customers notice immediately.

Snippet-worthy truth: “When AI feels slow, customers don’t blame the network—they blame your brand.”

Network as a Service (NaaS): the infrastructure pattern behind scalable AI

Answer first: NaaS matters because it turns connectivity from a static project into a configurable product—exactly how AI teams operate.

Megaport positions itself as a Network as a Service provider: you create private connections and routes quickly, across locations and partners, without months of procurement and physical provisioning.

Why that matters to South African e-commerce and digital services:

• AI projects iterate fast; networks usually don’t.
• AI data pipelines need consistent paths between systems.
• Security teams want private connectivity instead of public internet exposure.

Where NaaS directly supports AI-driven commerce in South Africa

Use case A: Multi-cloud AI without chaos
It’s normal to run:

• your app in one cloud,
• analytics in another,
• and AI services wherever they’re cheapest or fastest.

Without intentional connectivity, you get unpredictable performance and growing egress costs.

Use case B: Private paths to payment, identity, and logistics partners
South African digital services rely heavily on partner ecosystems—payments, couriers, ID verification, messaging. Private, well-peered paths reduce jitter and improve reliability.

Use case C: Bringing inference closer to customers
As AI features mature, some inference moves closer to the edge (for speed, cost, or compliance). That only works if your edge environment has strong, manageable connectivity back to your data and control plane.

What South African businesses can learn from Megaport’s acquisition play

Answer first: The strategy isn’t “expand everywhere.” It’s “buy time and proximity”—and apply AI where those advantages convert into revenue.

Megaport didn’t just announce an India market entry; it acquired an operating platform with existing exchanges, locations, and customers, and pulled forward its timeline by almost three years. That’s a mature growth move: reduce risk, gain local expertise, and integrate into a global platform.

South African e-commerce and digital services can mirror this thinking in AI adoption.

1) Don’t start with AI models—start with the bottleneck

If your biggest revenue leaks are in checkout failures, delivery exceptions, or support backlog, then AI should focus there. But you’ll only see the gains if the data and systems are connected well enough to act in real time.

A practical way to map this:

1. List the top 5 customer journeys that drive revenue (search, product page, checkout, delivery tracking, returns).
2. For each journey, write down every system call involved.
3. Identify the slowest two dependencies (often partner APIs or cross-cloud calls).
4. Fix the path first (peering, private connectivity, data locality), then tune the AI.

2) Treat “local presence” as a performance feature

Megaport gained a footprint across Indian metros—Delhi, Kolkata, Hyderabad, Chennai, Bengaluru, Mumbai, Pune. That’s about proximity to demand.

In South Africa, the parallel is choosing architecture that respects where your customers and partners are:

• keep frequently accessed customer and catalogue data near the services that need it,
• cache aggressively where it makes sense,
• avoid dragging every AI call across long, unpredictable routes.

3) Integrate in phases, not big bangs

Megaport is doing a phased rollout of services after integrating Extreme IX. That’s the right way to deploy AI too.

A phased AI rollout that works in e-commerce:

• Phase 1: Instrumentation and data quality (event tracking, product data hygiene)
• Phase 2: “Assisted” AI (agent suggestions, marketing content drafts, support triage)
• Phase 3: Real-time AI (dynamic offers, fraud optimisation, personalised search)
• Phase 4: Autonomous workflows with guardrails (refund automation, replenishment)

Each phase increases the need for reliable, low-latency connectivity.

Practical checklist: making AI-ready connectivity real (without boiling the ocean)

Answer first: Focus on three things—data movement, partner dependencies, and reliability.

If you’re a South African online retailer or digital service provider aiming to scale AI, here’s a grounded checklist:

Data movement

• Put your top AI features on a diagram and mark where data is produced and consumed.
• Reduce cross-region and cross-cloud transfers for high-frequency events.
• Prefer near-real-time streams for behavioural events over nightly batch jobs.

Partner dependencies

• Identify which third-party APIs sit on your critical path (payments, couriers, ID).
• Measure latency and error rates by provider and by time of day.
• Add redundancy where it pays: two providers, failover routing, or cached fallbacks.

Reliability and security

• Move sensitive system-to-system traffic off the public internet where possible.
• Build for outage reality: retries, timeouts, circuit breakers, and graceful degradation.
• Treat network monitoring as a product metric: checkout latency and support response time should be on the same dashboard as conversion.

Another quotable line: “AI doesn’t remove operational work—it makes weak operations louder.”

Where this fits in our South Africa AI commerce series

This series is about how AI is powering e-commerce and digital services in South Africa—from content generation and marketing automation to customer engagement and fraud prevention. The Megaport–Extreme IX story adds an essential layer: AI improvements don’t stick unless the infrastructure can carry them at speed and at scale.

If you’re planning 2026 initiatives—personalised shopping, AI-driven customer support, real-time risk scoring—treat connectivity as part of your AI roadmap, not a separate “IT concern.” Your models can be excellent and still underperform if the network path is fragile.

A good next step is to pick one revenue-critical AI feature and run a simple test: measure end-to-end latency across peak hours (including all dependencies). If the result surprises you, that’s your roadmap. What would your conversion rate look like if that entire journey was 300 milliseconds faster—consistently?