Telcos Double Down on AI as 90% Report Positive Returns

Telecom operators’ relationship with artificial intelligence (AI) is now defined by results, not rhetoric. Nvidia’s 2026 State of AI in Telecommunications survey, which polled more than 1,000 industry professionals globally, found that 90% of operators say AI is driving positive return on investment (ROI) and boosting both revenue and cost efficiency, while 89% plan to increase AI spending in the near term.

The most notable shift is where that ROI is coming from. Instead of leading with customer service chatbots, telecom operators are seeing the strongest returns from network automation and internal process optimization.

Network Automation Becomes the Leading Source of Value

Nvidia’s report shows that roughly half of respondents identified network automation as the top AI use case driving ROI, ahead of customer service and marketing applications. That reflects a broader industry recognition that the network itself is the highest-leverage domain for AI deployment.

AI models are being applied to predictive maintenance, traffic optimization, fault detection and spectrum allocation. These capabilities allow operators to prevent outages before they occur, dynamically reroute traffic during congestion and reduce energy consumption in radio access networks.

Bain cautioned that fully self-driving networks remain an aspirational goal for many operators. However, Bain noted that targeted AI deployments in service assurance, network planning and operations support systems are already delivering measurable cost savings and faster incident resolution times. Rather than attempting end-to-end automation immediately, operators are layering AI into high-impact workflows where returns are clear.

Vendor strategies are evolving accordingly. Ericsson recently partnered with Mistral AI to embed advanced AI capabilities into telecom operations. The collaboration focuses on automating troubleshooting, modernizing legacy code bases and accelerating next-generation network development. The emphasis is not on consumer-facing generative AI interfaces, but on embedding intelligence inside network management systems.

At the operator level, AT&T has introduced its Geo Modeler tool to simulate environmental and geographic variables before deploying infrastructure. By modeling build scenarios digitally, AT&T can reduce capital expenditures and improve coverage planning accuracy. These are capital allocation decisions measured in millions of dollars, underscoring why network-focused AI initiatives are becoming strategic priorities.

Internal Process Automation Overtakes Customer Service

While customer service automation remains important, Nvidia’s survey suggests internal operational improvements are delivering higher and more immediate returns.

Telecom operators manage complex back-office environments that include billing reconciliation, fraud management, workforce dispatch, compliance tracking and vendor coordination. AI systems are increasingly automating anomaly detection in billing, streamlining ticket routing and optimizing technician scheduling. These improvements shorten cycle times and reduce manual intervention.

PYMNTS reported that AT&T is deploying autonomous AI agents to reduce fraud and customer wait times. Rather than relying solely on scripted bots, the company is experimenting with agents capable of analyzing patterns in real time, initiating actions across systems and adapting to new fraud vectors dynamically. Fraud prevention represents a significant financial lever for telecom operators, where both false positives and missed detections carry high costs.

By focusing AI investment on internal efficiency and risk mitigation, operators are directly improving operating margins. Telecom remains a capital-intensive industry facing pricing pressures and high infrastructure investment requirements.

Autonomous Networks on the Roadmap

The longer term ambition is the transition toward autonomous networks capable of self-configuring, self-healing and self-optimizing. Nvidia’s findings show strong confidence in that trajectory, with a majority of respondents expecting AI-native networks to materialize ahead of 6G rollouts.

Bain noted that progress toward autonomy will be incremental. Legacy infrastructure, fragmented IT stacks and regulatory constraints remain barriers. However, each targeted automation initiative builds foundational capabilities that bring operators closer to intent-driven network management.

Ericsson’s AI partnerships and AT&T’s operational deployments illustrate that the ecosystem is aligning around embedded intelligence rather than surface-level automation. AI is becoming the control layer across network functions, operations support systems and enterprise workflows.

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