Talley

Autonomous network development The more ambi ous piece of this partnership is the network itself. Verizon's long-term goal is an autonomous network intelligence framework — a system that predicts, diagnoses, and resolves anomalies with minimal human interven on. Google Cloud's data pla orm will ingest network telemetry and performance data, and AI models will look for pa erns and early signs of trouble before customers no ce anything is wrong. The part worth paying a en on to is what happens a er detec on. AI agents will get programma c access to network systems via APIs, enabling automated patching, re-rou ng, and configura on changes without a human in the loop. The stated goal is fewer outages, be er reliability, and more consistent service quality, par cularly for latency-sensi ve applica ons. It's a logical extension of Verizon's 2025 AI Connect strategy, which posi oned the company's network as infrastructure for heavy AI workloads — Google Cloud was among the early adopters cited then, so its deeper role here isn't a surprise. But giving AI agents write access to live network infrastructure is a genuinely different risk category than le ng a chatbot answer billing ques ons. The companies haven't yet provided detailed governance frameworks for how those agents will be constrained, audited, or overridden, and that gap is likely to a ract regulatory scru ny. An autonomous network that fixes problems faster than humans can is a compelling pitch. An autonomous network that causes a problem faster than humans can catch it is the scenario regulators will want addressed on paper first. Data unifica on and marke ng Underneath both of those efforts sits a data problem, and that's where Google's Agen c Data Cloud comes in. Verizon's data is currently sca ered across business units, and the plan is to integrate it into a unified view of customers, networks, and opera ons — elimina ng the silos that make consistent AI deployment difficult. Standardized data processes across business units mean AI agents can be deployed and managed the same way everywhere, rather than rebuilt for each division's quirks. Marke ng is the most visible beneficiary. Verizon plans to use Google's AI tools to modernize its marke ng pla orms, automa ng content crea on and campaign design, and tailoring messages and offers based on unified customer data and behavioral signals. The goal is be er engagement and reten on. Centralizing that much data raises the stakes on governance. Strict access controls and auditability stop being nice-to-haves when a single pla orm holds an enterprise-wide view of tens of millions of customers. To the companies' credit, security is part of the deal — Verizon expects Google Cloud's security and threat detec on tools to strengthen its defenses against cyber risks, though that's a vendor expecta on rather than a demonstrated outcome at this point. Step back and this deal fits a pa ern that's become familiar across telecom. Hyperscalers supply the compute, data pla orms, and AI models; carriers contribute the network edge, enterprise rela onships, and managed connec vity. Neither side can build the other's half cheaply, so partnerships like this one keep forming. For Verizon, the bet is differen a on — pairing its network with Gemini and Google's data tools gives its enterprise offerings something rivals building on other clouds can't easily replicate. For Google Cloud, it anchors Gemini Enterprise in a sector where reliability and low latency actually ma er, which is a useful proof point beyond generic enterprise AI pitches. The broader concern is concentra on. Every deal like this places more cri cal communica ons infrastructure in the hands of a small number of large cloud providers, and that has implica ons for both resilience and an trust debates that go well beyond these two companies. Ar cle Credit: h ps://rcrwireless.com/20260826/ai/verizon-google-cloud-ai-partnership Talleycom.com You Connect the World. We Make it Easy. ® 13 QUARTER 3 2026 SHEET®

• Cover