AI has reached an inflection point in the enterprise. After an initial wave of experimentation—which was driven more by hype than by outcomes—organizations are seeking more strategic ways to drive AI’s value at scale. Nowhere is this more evident than for IT and network teams, where critical risks and rising costs leave little room for “black box” decision-making. 

In 2026, there’s no doubt that AI will continue to transform the way we work. But it’s up to human domain experts to lead the way.

Domain Expertise Will Guide Network AI Adoption

As AI enthusiasm stabilizes, large enterprises will prioritize measurable IT Ops outcomes by combining AI with proven automation patterns instead of treating AI as an independent operator. The most successful programs will be built and governed by experienced SRE/NetOps/Platform teams that define where AI delivers operational value versus where it creates low-signal output. Domain experts will shape service models, escalation logic, and safe runbooks, while AI accelerates triage, correlates telemetry, summarizes incidents, and highlights edge cases. This expert-led operating model enables reliable, high-impact automation with clear ROI and controlled production risk. 

Network AI Will Demand Human Expertise 

Ironically, to achieve excellent AI outcomes in the enterprise, humans need to be even more central to the process. Domain experts must define which problems matter, what “good” responses look like, and which questions AI is even allowed to answer. They’ll design the workflows and set the guardrails, determining when automation should stop and a human should intervene. In short, these domain experts will serve as the dividing line between useful insights and “automation slop” that simply looks authoritative. This also means that they’ll be the ones wielding veto power over any change that could impact production infrastructure, security posture, cost, or customer experience. 

Network Digital Twins Deliver Critical Safeguards

Most infrastructure and operations teams still won’t trust AI to take fully autonomous action—and they’re right to be cautious. Even a single incorrect change can cascade into an outage or a security incident. To mitigate this risk, experts need a way to understand how different elements of their network are behaving and working together at any given time. But by using a network digital twin as a source of truth, enterprises can simulate the effects of any change in order to safely test and validate its impact. Only after passing those checks will changes be allowed anywhere near production. As a result, enterprises can successfully de-risk changes, from simple policy updates to large-scale digital transformation initiatives. 

Taken together, these predictions point to a more grounded and sustainable phase of network AI adoption. We’ve seen that AI delivers its greatest impact when it’s anchored in stateful, contextualized data. The enterprises that understand this will be the ones that move the fastest, encountering the fewest surprises.

This article was originally published on VMBlog.

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