Is your network holding back your AI plans?

Leaders in every industry are pushing hard to turn AI ambition into real outcomes. Interest is high, the pressure to scale is growing and AI roadmaps are getting bolder. But there’s one issue threatening to slow all this momentum: the network.

This was evident in a recent conversation with a client. They were piloting a small AI tool to speed up visual checks on their factory line. It worked smoothly in their test setup, but on the shop floor it sometimes took a few seconds to load images. The issue wasn’t the AI, however: A weak wireless local area network (WLAN) signal in one section of the plant was delaying image requests to the server.

According to NTT DATA’s 2026 Global AI Report: A Playbook for AI Leaders, nearly one-third of AI leaders — based on their AI maturity, AI strategy and AI-related profit — say infrastructure bottlenecks are holding back their AI ambitions.

This points to a simple fact: AI is moving faster than the networks supporting it.

• ALSO READ → From AI ambition to real impact: Why infrastructure makes the difference

4 network challenges holding back AI

Unlike the enterprise applications of the past, AI doesn’t sit still. It’s dynamic, distributed and hungry for data. It involves real-time inference at the edge, frequent model updates, millisecond-level decisions and continuously changing user, device and sensor data. Yet, many networks still operate like they did in the early 2000s. They are centralized, with heavily manual operations, and not dynamic enough to support AI.

This mismatch creates four major problems.

1. AI needs continuous data flow. The network still moves in change windows.

AI is always learning and adapting. It moves as fast as new data arrives. Most enterprise network teams, however, still manage change through tickets, human-driven approvals and scheduled maintenance windows.

When AI keeps moving and the network pauses for every adjustment, the gap between them becomes the bottleneck.

2. AI needs consistent performance. Network performance still fluctuates under load.

AI is now on shop floors, in hospitals, in vehicles, in retail stores, in warehouses and at the edge. These workloads expect consistently low latency. So, when network performance dips, even briefly, inferences slow, computer vision stutters and user experiences suffer.

AI doesn’t handle hesitation well. Small delays quickly become expensive.

3. AI needs deep visibility. Networks offer only fragments.

AI models running in the cloud and in distributed environments depend on a clear, end‑to‑end view of network behavior, including how traffic moves, how applications perform, and where latency or failures may emerge. But most networks still provide only fragments of that picture through partial logs and siloed monitoring.

With so much missing, AI is left working with blind spots, making it harder to anticipate issues or operate reliably.

4. AI needs a steady foundation. Networks remain inconsistent.

AI relies on things working the same way everywhere, but many networks don’t. Years of one‑off fixes, regional variations, device quirks and legacy scripts have created an environment where nothing behaves uniformly. And when the underlying fabric is inconsistent, AI has nothing stable to use as a base for automation. It soon hits a ceiling.

In short, inconsistent network environments create the friction that stops AI from moving forward.

So, what does an AI‑ready network look like?

AI can’t scale on a shaky foundation. The network supporting it needs to be more predictable, more responsive and far more resilient. This isn’t about ripping everything out, though; rather, it’s about strengthening what you already have. And that starts with a few critical capabilities:

Standardize and version your network configurations

A consistent network is the backbone of AI success. Structured, validated, documented and version‑controlled configurations make the network environment predictable and easier to automate. Templates replace device‑level commands, version histories replace siloed team knowledge, automated testing replaces trial and error, and instant rollback replaces hours of recovery. The more consistent the network is, the more confidently AI can operate.

Translate business requirements into clear network policies

AI performs best when the network understands intent. Start with defining outcomes such as keeping inference traffic at low latency, enforcing data residency in specific regions or applying zero trust security controls consistently. Then, encode these outcomes as policies so they can be applied uniformly across clouds, edge environments and platforms to improve predictability.

Let AI augment day‑to‑day network operations

To improve the network, AI consumes network-performance data. Modern network operations use AI to spot anomalies early, recommend performance changes, troubleshoot in real time and make routine adjustments. This leads to fewer outages and escalations, leaving more time for engineers to focus on work that moves the business forward.

Reduce network risk with a digital twin

If AI is going to help operate the network, it needs a safe place to learn and test. A digital twin provides exactly that. It mirrors the live network so teams can model topologies, simulate changes, test failovers, validate policy impacts and train AI agents, all without affecting production. It turns guesswork into confident, data-driven change.

• ALSO READ → Modernize your network to power an AI-ready enterprise

The payoff: AI that finally performs as promised

When your network becomes consistent, observable, automated and simulation-ready, everything changes:

• AI works everywhere, from the cloud and the edge to data centers, branches, sites and factory floors.
• Security becomes continuous, because compliance is enforced automatically.
• Opex drops as outages are eliminated and the need for firefighting decreases.
• Real‑time decision-making becomes a reality.

However, if your network can’t keep up, your AI can’t either. Your AI pilot projects won’t scale and ROI will be lower than expected.

At NTT DATA, we help you build a network foundation that lets AI take off. Let’s talk.

• ALSO READ → AI is raising the bar, and your network has to rise with it