Modern dependence on “always on” connectivity has changed the game for service providers and networks
Service provider networks have grown well beyond the traditional offerings of video and residential broadband, and meeting the needs of today’s subscriber will depend on how these networks integrate Artificial Intelligence (AI) and Machine Learning (ML) tools in 2026 and beyond.
Today, subscribers increasingly rely on this broadband connectivity as critical — not only for home-based business operations, but for a range of diverse applications such as in-home security, medical device telemetry, and other services. Modern dependence on “always on” connectivity has changed the game for service providers, as network downtime is no longer a matter of just missing a favorite TV show. It can be seriously disruptive to a household — and for a service provider, downtime can translate into increased subscriber churn that puts pressure on a service provider’s bottom line.
In light of these higher stakes, service providers face difficulty staffing their teams with qualified network experts who can effectively address analytics and implement their recommendations. As the most senior and experienced staff age out of the industry, availability is expected to tighten further — and costs are likewise expected to continue climbing. Additionally, managing the mountain of telemetry available from today’s smart network devices demands automation to find the insights within. AI-driven analytics powered by ML algorithms are beginning to be integrated into service provider networks, but the year ahead will likely set the stage for greater adoption and broader implementations of these advancements to support network staff.
The state of the market: Competitive
Even for a fast-evolving industry like broadband access, service providers have been fiercely competitive in delivering new services and better availability across their served markets, and those markets are now more frequently shared by multiple providers. According to an October 1, 2025, writeup by Broadband Search, only 33.4% of households in the United States had a choice of three or more providers for basic connectivity in June of 2020. Five years later, that choice is now available to 83.7% of households and is projected to rise further yet.1
While all providers are not created equal in terms of offerings, it all adds up to increased competitive and cost pressures for providers serving the vast majority of homes, including through alternate network technologies such as satellite and 5G fixed wireless. Of course, this is excellent news for the subscriber, but it also puts an increased onus on the service provider to press forward or risk being left behind. To manage these pressures and maintain competitive levels of network availability, AI and ML are helping address the increased complexity of ultra-high-speed networks, staff availability, and budget constraints. In the past, network operators accepted using lower modulation orders, which required much more forgiving network performance, but today and into the future, only the cleanest networks can achieve the highest modulation profiles — and highest speeds that consumers are now demanding.
Getting the best from network resources
From what we see, the use of AI by service providers has largely been focused on customer service, rather than network surveillance and maintenance.
Maintaining network availability and performance is beginning to exceed manual human capability. Today’s networks require systems that can look across multiple variables to determine how they correlate and affect outcomes. AI will be able to see patterns and identify issues that humans would miss completely, even if budget and staffing were not a problem. Furthermore, AI can push network efficiency and performance to levels not easily achieved by humans alone.
Network tools powered by AI can provide continuous monitoring, connect the dots and flag issues for human-driven resolution measures, while reducing the amount of bandwidth overhead required to process the data and freeing up network resources for higher level operations and revenue-generating use instead. Adjusting the entry point for human intervention helps elevate the utility of the network engineering staff, who can spend more time dealing with potentially business-impacting events, leveraging the greatest strengths of AI and human alike.
These advantages have set the stage for network tools powered by AI and ML to reach deeper into service provider networks and back-office systems as a predictive resource capable of correlating the vast amount of billing, customer service, technical and other data to map next steps for human evaluation.
Learning to trust the machine
In spite of AI’s increasing number of proven applications, there remains a degree of reluctance to hand over control to the “black box algorithm” entirely, and there are good reasons to be cautious. As mentioned above, network downtime is no longer an irritating inconvenience for subscribers; always-on expectations are often driven by critical applications running on those networks. For this reason, service providers still generally prefer to have human involvement in any decision or mitigation effort.
But even here, AI can help improve the value of that human element by focusing human interaction on higher-level analysis and functions. The increasing adoption of natural language interfaces for AI agents provides the opportunity to access data in new ways and challenging environments, as needed, even in remote locations where interactions must take place over a mobile device or under less-than-ideal circumstances, such as out in the field on a stormy night. Such a utility helps develop relevant skills more quickly — learning by doing, with real-time AI voice assistance — and again helps to elevate a network engineering staff’s productivity.
It’s also worth noting that the greater depth and insight afforded by AI helps pinpoint locations for network impairments, reducing mean time to resolution (MTTR) metrics and limiting the amount of unnecessary handling of unrelated network infrastructure. Because AI/ML-driven analytics are growing more proactive — and service providers are gradually increasing their trust in them — the goal of a truly self-healing network becomes more of a reality. Because AI management is infrastructure-agnostic, service providers can realize these efficiency and availability benefits across DOCSIS®, PON, hybrid, I-CCAP, vCCAP/vCMTS, DAA and even wireless networks. However, as with all AI/ML applications, the quality of training the models and the expertise of the AI solution vendor, as well as the quality of the data used in training, remain critical prerequisites for AI implementations that can justify increased levels of trust.
AI is moving to the edge
While the effects of AI have been felt most significantly in the core network, it is now starting to assert its utility at the network edge as a distributed analytics and management tools. Multiple DOCSIS 4.0, DAA, and PON access network solutions are now available on the market that incorporate neural processing units (NPUs), which are AI-optimized processors that extend the reach of AI further out into the distributed network. By offloading some of the monitoring and analysis burden from the central office — and reducing backhaul requirements on the upstream network — these NPU-enabled solutions reduce latency for AI operations and unlock a number of benefits for service providers.
In some cases, these NPU-enabled devices can be leveraged to auto summarize data locally, resulting in more concise telemetry sent upstream. Additionally, quick-burst events that would normally be missed by periodic telemetry collection, now have the opportunity to be detected by the edge device for a highly granular view of network events, their causes, and their effects than could be detected by just manual human monitoring.
The capabilities of AI at the network edge will only increase in the years ahead, providing greater utility for service providers, and greater network availability for subscribers.
In 2026, AI’s impact will be felt strongly
AI exploration is active within service provider organizations, and while adoption has begun in areas like customer service, there is a long way to go to more fully realize the value of AI in managing network performance.
The coming year will see many of its promises come to life, helping service providers make more sense of the vast amount of data and telemetry their networks produce, helping better detect and prioritize work for network technicians to assist them in becoming more productive, and gaining deeper trust from human decision-makers. Both in the core network and increasingly at the network edge, network tools powered by AI/ML will continue to improve network availability and efficiency as self-configuring and self-healing/optimizing networks become the norm.
To fully realize AI’s potential, however, those tools must be trained on quality data and expertise — and organizations must have strong management support. With service providers at differing stages of their AI journey including discovery, evaluation, adoption, and execution, ROI will require commitment and planning. To maintain competitiveness and accelerate the path to success, service providers must consider working alongside a qualified solutions partner to establish a plan, explore low-hanging solution opportunities, and chart a path to the future.
