For years, AI inside telecom networks sat mostly on the sidelines. Models were tested in labs, discussed at conferences, and trialled in narrow use cases that rarely touched live traffic. At Verizon, that boundary is starting to blur inside the network itself.
The company is now using AI directly inside its commercial network to manage power use, improve performance, and support new edge services aimed at enterprise customers. This is positioned as a response to rising costs, heavier network loads, and the growing demands of AI-driven applications.
Telecom networks are expensive to run. Power costs are rising, equipment cycles are long, and traffic patterns are less predictable than they were a decade ago. Verizon’s move reflects a shift in the industry: AI is being pulled closer to daily operations, not kept as an experimental layer.
Verizon applies AI directly inside the network
Verizon’s AI work focuses on areas that have clear cost and performance pressure. One of those is energy use in radio access networks. Radio equipment runs continuously, even when demand is low, and small efficiency gains can add up in thousands of sites.
By using AI tools that adjust how network equipment behaves based on real conditions, Verizon aims to reduce power use without disrupting service. That kind of optimisation does not change how customers interact with the network, but it affects operating costs measurably.
The same logic applies to performance management, where Networks handle a mix of traffic types, from video streaming to enterprise data flows. AI systems can help detect patterns that static rules miss, allowing the network to respond more quickly when conditions change.
What matters is that these systems are being applied to live environments. That raises the stakes. Mistakes can affect service quality, which is why telecom operators have traditionally been cautious about automation. Verizon’s decision suggests that the balance between risk and reward is shifting.
Edge services shaped by AI workloads
Another driver behind Verizon’s approach is demand from enterprise customers. More companies are running AI workloads that depend on low latency and reliable connections, especially for tasks like real-time analysis or on-site decision support.
To meet those needs, Verizon has been shaping edge services that sit closer to customers than central cloud regions. AI plays a role here by helping manage traffic, prioritise workloads, and maintain service levels.
This is about making the network suitable for AI-heavy use. Edge services fail if the underlying network cannot respond quickly or predict demand, and AI tools are being used to close that gap.
Verizon’s focus suggests that telecom operators see AI workloads as a long-term, not temporary feature of enterprise demand. Supporting those workloads requires changes at the network level.
Automation, cost pressure, and control
The push toward AI-driven operations also reflects cost discipline. Telecom margins are under pressure, and large capital investments are hard to justify without clear savings. AI offers a way to reduce manual intervention and improve consistency without large staffing changes.
Verizon has been careful in how it presents this shift. AI is described as a tool to assist network management; engineers still set boundaries and review outcomes, and automation operates in defined limits.
That framing mirrors patterns seen in other regulated industries. Fully autonomous systems are rare where reliability and accountability matter. Instead, companies use AI to narrow options and surface issues faster.
Control is central to this approach. Verizon’s AI systems operate in the company’s own infrastructure, where behaviour can be monitored and adjusted. That reduces the risk of unpredictable outcomes that could arise from external tools or black-box systems.
It also aligns with regulatory expectations. Telecom networks are important infrastructure. Any automation that affects availability or emergency services must be auditable. AI that cannot be explained or traced would face resistance.
Verizon’s network AI approach and what it means for telecom
Verizon is not alone in exploring AI for network operations, suggesting that the industry is moving past the question of whether AI belongs in telecom operations and toward how it should be governed.
That does not mean AI will transform networks overnight. Integration is slow, and legacy systems limit how quickly changes can be made. Many uses will remain narrow for some time.
AI is being treated less like part of the machinery that keeps networks running. That shift mirrors what has already happened in other large enterprises, where AI spending is folded into core budgets.
For other operators, Verizon’s experience offers a practical reference. AI deployment starts with problems that are costly, repeatable, and hard to solve with fixed rules.
Networks are being shaped with AI workloads in mind. Performance, latency, and stability are becoming design priorities, not afterthoughts.
Verizon’s approach does not guarantee smooth results. AI systems can misread conditions, and tuning them takes time. But the company’s choice to apply AI in live networks suggests that waiting on the sidelines now carries its own risk.
In telecom, as in other infrastructure-heavy sectors, the question is how much control operators can maintain as they rely on it more deeply to manage cost and complexity.
(Photo by José Matute)
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