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Telecom companies use AI and automation to lower recurring operating costs mainly by matching network energy use to demand, automating fault handling, predicting equipment problems, and speeding up service workflows. The largest practical opportunity is often the mobile radio access network (RAN), where operators can reduce power to underused equipment while maintaining coverage and service quality. Published savings figures come from specific operator deployments and vendor or industry case studies—not from a comparable, industry-wide estimate.
Where AI can reduce telecom operating costs
AI in telecom is not one cost-cutting feature. It is a set of methods applied to recurring work: electricity and cooling, network monitoring, fault resolution, maintenance, and service operations. AI or machine-learning models identify patterns in network and equipment data; automation can then recommend or carry out an operational response.
- Energy: Adjust radio resources to match traffic and reduce unnecessary power use.
- Network operations: Correlate alarms, identify likely causes, and automate some corrective actions or ticket creation.
- Maintenance: Flag abnormal equipment conditions early so teams can investigate before a failure.
- Service workflows: Connect network events with customer complaints and operations systems to shorten resolution loops.
These approaches can reduce resource use or manual workload, but the available examples do not establish an average amount of cost saved across telecom companies.
How AI improves network energy efficiency
Radio networks have to handle changing traffic throughout the day. AI and machine learning can analyze current or expected demand to identify radio resources that are underused. Operators may then lower their power use or place selected equipment into low-power or sleep states, instead of running every resource at the same level during quiet and busy periods.
Those decisions must account for coverage and traffic moving between neighboring cells. Nokia says its KDDI system coordinates power-saving decisions across cells, balancing energy use with network performance and customer experience. Nokia reported no performance degradation in the pilot, but that is a vendor-reported result for that deployment, not a guarantee for other networks. Nokia’s KDDI case study
Energy management can also include cooling. In a 2025 announcement, Nokia described an Indosat deployment that uses traffic analytics to adjust or shut down idle radio equipment and thermal management intended to reduce cooling energy. The rollout began across Nokia RAN sites in Sumatra, Kalimantan, Central Java, and East Java after a successful pilot, with a stated plan to deploy nationwide. The announcement did not report a realized percentage reduction in cost or energy. Nokia’s Indosat announcement
How network automation reduces operations workload
Network operations centers handle large volumes of alarms and events. Many alarms may reflect symptoms of the same underlying issue. AI-based correlation can group related signals and help teams focus on likely causes; closed-loop automation can take approved corrective actions or create a trouble ticket. This can reduce repetitive triage and help move operations from reactive repair toward prediction.
A TM Forum case study of Airtel’s work with Ericsson Operations Engine reports that 69% of alarms were automatically correlated and resolved. It also reports a 29% reduction in mean time to repair (MTTR), a 47% reduction in network unavailability, and a 26% improvement in customer experience. These are results attributed to that case study and deployment, not general benchmarks for telecom operators. TM Forum’s Airtel case study
Predictive maintenance and equipment monitoring
Predictive maintenance uses operating data, sensor readings, or other signals to identify abnormal conditions before they become outages or require routine site visits. The potential cost benefit is avoiding or better targeting maintenance work, but the cited example does not quantify labor savings or avoided incidents.
In a 2024 announcement, Ericsson described Chunghwa Telecom’s use of temperature sensors and AI/ML analysis to predict fire likelihood within five minutes. The announcement does not state the model’s accuracy or how much the approach reduced inspection frequency. Ericsson separately reported a 34% reduction in Chunghwa Telecom network energy consumption; that figure is an energy result, not a quantified predictive-maintenance saving. Ericsson’s Chunghwa Telecom announcement
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Automating complaint resolution and ticket handling
Network automation can connect network data with customer-experience information and service-desk workflows. When an issue is detected, systems may create a ticket or initiate a corrective action, reducing handoffs between network teams and customer support. This does not necessarily mean a chatbot or generative AI is involved; the cited example describes operational automation.
Ericsson’s case study for Digital Nasional Berhad (DNB) reports a 90% reduction in customer complaint resolution time and automatic creation of 95% of trouble tickets. It also reports that alarm count fell by 500% after six months and network uptime exceeded 99.8%. The case describes system-driven operations with qualified personnel retaining oversight. These are DNB-specific, Ericsson-reported results. Ericsson’s DNB case study
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What reported energy savings show—and do not show
Named deployments illustrate that AI-based RAN energy management can produce substantial results in particular settings. Their figures measure different environments and scopes, so they should not be treated as a head-to-head comparison.
| Operator and source | Reported result | Scope and qualification |
|---|---|---|
| Chunghwa Telecom, Ericsson | 34% reduction in network energy consumption | Reported by Ericsson in 2024; the announcement does not provide a directly comparable measurement period or cost figure. |
| KDDI, Nokia | Up to 50% lower average power consumption in low-traffic environments and up to 20% per cell | Nokia-reported pilot results; publication date is not visible in the cited excerpt. Nokia says the pilot had no network performance degradation. |
| Indosat, Nokia | No numeric realized reduction stated | 2025 deployment announcement describes RAN energy controls and thermal management, but gives no percentage saving. |
The figures use different measures, conditions, and deployment stages. Without consistent baselines, geographies, measurement periods, service-quality checks, and independent verification, they cannot establish which system saves more or what another operator should expect. The cited materials provide no harmonized estimate of average telecom cost savings attributable to AI.
What operators should assess before deploying AI
A credible business case should connect an operational change to a measured cost or service outcome. Operators evaluating AI for network energy efficiency or network automation should examine:
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- Service quality: Whether energy controls preserve coverage, capacity, and customer experience.
- Scope: Which network domains, sites, equipment vendors, and operations systems are supported.
- Deployment stage: Whether reported results come from a pilot, a limited rollout, or production operation—and the footprint covered.
- Operational integration: How the system works with existing alarms, OSS/BSS, ticketing tools, and staff processes.
- Human oversight: Which actions happen automatically, which require approval, and how teams can intervene or reverse an action.
- Data and monitoring: Whether network and sensor data are sufficient to support the models and verify their ongoing performance.
- Evidence quality: Whether savings are independently measured, operator-reported, or presented in a vendor case study, and what baseline and measurement period were used.
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