Making space for specialized AI in enterprise networks

The number of AI use cases in enterprise network management is growing. Driven by competitive pressures to improve performance and reduce costs across their IT and OT networks, enterprises are fully embracing the power of AI and ML within their organizations. 

GenAI is no longer just a peripheral issue; it’s now increasingly embedded in how we operate networks, from natural language troubleshooting to configuration synthesis and multi-modal telemetry. Yet, fast-growing capabilities and multiplying use cases are only part of the picture. 

For specialized enterprise networks, the value of equally specialized AI management is now becoming critical if they want to stay ahead of the competition. We stand at a crucial junction in the journey of fully embracing AI in network management. Customized AI solutions, trained to address a network’s unique KPIs and prioritize the business’s top concerns, can improve network performance and reduce costs to such an extent that adoption is not really a matter of preference, but indeed one of commercial survival. Those organizations that don’t adopt a specialized approach will fall behind and find it harder to catch up. 

The 80/20 solution for AI training

All enterprise networks share some broad characteristics in their AI applications. This fungibility is, in large part, responsible for the diminishing cost barriers to AI-based solutions. Such generalized, off-the-shelf models are typically able to provide about 80 percent utility for basic functions, such as:

• Network incident and anomaly detection
• Prioritization of incidents, root cause analysis, and the automation of some of the processes that those efforts require
• Network analytics that ensure KPIs are met, and that incident impacts are minimized

However, when one considers all the different types of enterprise networks operating today—unique not only in their markets but also in size, scale, and maturity—adding the remaining 20 percent of utility becomes all the more critical. This specialized subset of capabilities reflects unique network needs, bringing accurate domain data to the table to align with the enterprise’s technological and operational priorities.

In most cases, an enterprise network isn’t homogeneous; it’s often composed of component networking technologies integrated into a common platform infrastructure to connect employees, customers, vendors, devices, data center back ends, and so forth. When an enterprise runs Wi-Fi 7 access points for employees or customers, private 5G for communication across a supply yard or campus, Zigbee® connectivity for IoT devices and Bluetooth® for A/V devices, in addition to the physical cabling and switching infrastructure of the wired network, it’s clear that the complexity is beyond the effective administration by human eyes and hands alone. And this is all before one considers the necessary monitoring of the wired network’s health—port utilization, authentication and security, thermal management, PoE utilization, and so forth.

AI models for enterprise networking use cases must meet all these needs and provide the accuracy and consistency that off-the-shelf models lack. That’s where the 20 percent becomes all-important, and custom models with additional validation logic are necessary, though they can unfortunately increase latency in response times and costs. 

As deployments scale, the ongoing cost is not centered on training; it’s on inference—answering millions of real-time queries from assistants and agents to achieve that prized degree of specialization. We see costs declining to reach a given benchmark score, driven by algorithmic efficiency and specialized silicon. CIOs should budget for DSLMs (domain-specific language models), right-size inference stacks (quantization, batching/caching), and consider workload-aligned hardware (GPUs vs. TPUs/ASICs) to continue reducing the total cost of ownership.

The OT case for specialized AI

In addition to this complex web of different connectivity technologies, the growing number of IoT applications on the operational technology (OT) side also demands a specialized approach to AI management. There is no single model for an enterprise’s physical plant, even within the same vertical. Unique characteristics of scale, occupancy, and so forth all require deep domain knowledge that doesn’t typically come with the 80 percent utility of a generally-trained AI deployment—at least, not to the most significant possible effect.

A specialized AI management solution can—if properly trained—seek, suggest, quantify, and execute operational changes to maximize the value of OT management. In addition to the generalized AI benefits of reduced incident reaction time, anomaly detection, and documentation of configuration changes, a specialized AI management solution can take proactive steps to improve overall efficiency in the physical plant, delivering benefits such as reduced cost, risk, and even environmental impact. 

For instance, a resort hotel’s network can automatically correlate booking information to in-room Wi-Fi® connectivity, lighting and even HVAC resources, turning off services until needed. Or consider a school building with a highly mobile-connected student body, demanding a reactive Wi-Fi channel and power management from one period to the next, tasks far too complex and unpredictable for a human IT administrator to provide. Access to 20 percent of specialized AI training can enable these bespoke efficiencies across industries. 

The human factor

This raises the related issue of low availability and high costs of securing highly credentialed and certified IT administrators. The chronic shortage of exceptional talent shows no sign of relenting. Here is where specialized AI network management can have its most significant impact by reducing the required skill sets of IT staff and freeing them from less profitable day-to-day tasks in favor of more valuable work. AI can employ digital twin modeling to suggest, simulate, test and refine network configuration changes with less human involvement—and, importantly, with less overall risk.

An example of such a digital twin exercise can be seen in Agentic AI, which can plan, use tools, remember context and work collaboratively, with safety guardrails and human supervision. Unlike classic ML or basic GenAI chatbots, agentic systems are goal-directed; they plan steps, call tools/APIs, remember prior context, and evaluate outcomes. In networking, this means an agent can translate business intent into actions—to gather telemetry, simulate on a digital twin, propose RF/SD‑WAN policy changes, and request human approval before execution.

Agentic AI trained with specialized expertise can bring proprietary domain knowledge to the table to help different parts of the network operate more efficiently together, creating a virtual planning committee applying specialized network knowledge, behavior analytics, predictive reasoning and automated troubleshooting to solve complex networking issues that are beyond the practical grasp of human IT teams. Such AI platforms may also integrate natural language interfaces that let staff ask questions and receive answers in plain language, then add specialized agents (such as configuration and assurance) to deliver orchestration that keeps human decision-makers in the loop, with full audit and rollback. 

In-house or third-party?

In these early days of mass AI adoption, the attractive pricing structures of third-party implementations are necessary as players race for market share. However, as time goes on, a long-term commitment to such a partnership may come with price increases as those providers recover their capital costs and upgrade their computing infrastructure. For many enterprises, a less ambitious but wholly owned solution may offer a preferable path forward, if the capital budget can afford it.

But one stumbling block any enterprise could face with an in-house option is a potential sophistication gap between its specialized AI network management and the hardware it manages. While advanced AI models are capable of remarkably effective and inventive optimizations, they cannot change the laws of physics; AI cannot change the RF capability of a poorly designed Wi-Fi access point any more than an autonomous-driving vehicle can compensate for bald tires. Employing applications such as AI-driven radio resource management (RRM), RF planning with digital twins, and intent-based configurations helps realize optimal ROI from hardware. It creates a balanced approach that gets the most from hardware and AI alike.

Whether adopting a vendor platform or building an in-house platform, successful implementation depends on Responsible AI; that is, a model featuring data lineage, privacy/residency, model risk, red‑teaming, change approval gates, and audit trails. 

2026 will prove to be the year of specialized AI implementations

As AI training costs continue to decline, it will become easier for organizations of all sizes to achieve a 100 percent specialized AI solution and reap operational and cost benefits. It will empower less skilled IT staff to outperform, and facilitate new network designs, optimizations and efficiencies with reduced risk. 

Some enterprises will find their solution with a vendor, and others will build their own in-house. But with virtually all enterprises now taking AI seriously enough to adopt it, there’s little doubt that it’s here to stay.