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Telecom operators can approach operating expenditure (opex) as a portfolio problem: measure costs by network domain and site where possible, then compare energy, technology, operating-model and network-rationalization initiatives against their full costs and service risks. Energy is a substantial opportunity, but neither a technology trend nor a headline savings estimate is a business case. The right choice depends on the operator’s network, electricity market, data maturity and migration constraints.
What makes telecom opex difficult to control?
Operating costs span network energy, equipment and technology, field operations, IT, procurement and the work required to maintain service. They also move as traffic grows, networks expand and operators transition away from legacy technologies. A cost reduction that ignores coverage, capacity, resilience or service quality can simply shift expense or create operational risk.
Energy is a major controllable cost, though its share varies by operator. The GSMA’s The Mobile Economy 2025, published in January 2026, puts energy at about 20% of total operator opex, drawing on survey and benchmarking projects. Treat this as a broad industry estimate, not a forecast for any specific company.
The first management challenge is visibility. In a survey of 30 telecom technology, procurement and sustainability officers worldwide, fielded in the first half of 2023 and reported by McKinsey in 2024, 53% said they had limited or no use of real-time energy monitoring tools, while 33% tracked energy KPIs at individual-site level. Those figures describe survey respondents, not every operator, but they illustrate why a savings target without a credible baseline is hard to verify.
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How should executives establish a usable cost baseline?
Map expenditure to operational drivers
Build the most granular defensible view available across network domains, sites, equipment and activities. For energy, connect consumption and cost data to location, equipment and operating conditions where the underlying data permits. For IT and field operations, identify the systems, processes and work that drive recurring costs. Record data gaps rather than filling them with assumptions.
Assign accountability across functions
Energy and network efficiency cross organizational boundaries. Name a senior owner who can coordinate network operations, procurement, facilities and IT, and ensure finance can validate the financial result. Set a baseline and define measurable targets before launching a pilot. This is a management approach recommended in McKinsey’s analysis, not a universal regulatory requirement.
Keep the outcome and denominator clear
Separate a reduction in an energy bill from a reduction in network opex or total company opex. Track service quality and relevant sustainability measures alongside cost, and account for implementation expenses. A change that lowers one line item but raises another is not necessarily a net saving.
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How can operators reduce network energy costs?
McKinsey’s February 2024 analysis groups energy opportunities across site and equipment optimization, analytics-based operations, energy pricing and sourcing, and technology changes. It estimates that a holistic package could save 15–30% of energy costs. This is a consulting estimate, not a guaranteed result; it concerns energy costs, not total company opex. The same analysis notes that traffic growth, network rollout and the transition away from legacy technologies can push energy use and costs upward.
- Site and equipment: Examine site design, equipment configuration and operating practices for opportunities to reduce consumption without compromising coverage, capacity or resilience.
- Analytics and operations: Use sufficiently granular data to identify where energy is consumed and whether operational changes produce a measurable improvement.
- Pricing and sourcing: Review tariffs, contracts and sourcing options in the operator’s actual electricity markets; availability and economics vary by geography.
- Technology shifts: Evaluate infrastructure changes against energy performance and the cost and risk of deployment, integration and ongoing operation.
For each proposal, compare the expected effect on the energy bill, network opex and company opex separately. Include capital needs, time to implement, data and skills requirements, service constraints, local power-market conditions and carbon implications. The cited estimates do not establish a single best measure or a universal return on investment.
Can technology simplification lower costs without weakening capability?
Technology cost reduction need not mean indiscriminate cuts. McKinsey’s 2025 benchmark of more than 20 operators found that those in the top quartile for technology capability had an average IT cost-efficiency ratio nearly 30% lower than peers. The benchmark supports an association between stronger technology capability and lower relative IT cost; it does not prove that a particular investment caused the difference.
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Executives can use that finding to frame a practical sequence:
- Inventory duplication: Identify overlapping systems, platforms and processes, and establish their recurring operating and support costs.
- Prioritize simplification: Decide what to consolidate or redesign in light of business needs, network requirements and risk, rather than applying uniform cuts.
- Connect spend to outcomes: Require a measurable service, engineering or efficiency objective for material technology spending, then review the result against the baseline.
Cloud and AI should be assessed as choices within that work, not assumed savings mechanisms. The cited sources do not establish that moving a particular workload to public cloud automatically reduces cost.
How should operators compare automation, Open RAN, cloud and other network options?
The GSMA’s The Mobile Economy North America 2025 reports that operators in its regional survey ranked network and service automation, Open RAN, energy-efficient infrastructure, GenAI and public cloud among their leading opex-reduction approaches. This reflects reported priorities in North America, not a global ranking or proof that these options achieved savings. The result does not provide a comparable cost case for each approach.
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Use a common decision screen before comparing proposals:
- Lifecycle economics: Include capital, integration, operation, maintenance and migration costs over the period that matters to the business.
- Operational fit: Test interoperability, vendor dependence, skills needs and the extent of operating-model change required.
- Network obligations: Check coverage, capacity, resilience and service-quality needs during deployment and steady-state operation.
- Energy and data: Examine the option’s energy profile and whether the operator has the data and capability to verify the claimed outcome.
- Execution risk: Account for integration complexity, migration sequencing and the consequences of a delayed or unsuccessful change.
Ranked operator interest can help identify options worth evaluating; it cannot substitute for an operator-specific business case.
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When does legacy network rationalization make sense?
Retiring duplicative legacy network layers can simplify operations, but savings depend on the migration case. An older GSMA analysis, The Economic Benefits of Legacy Network Rationalisation, estimated a 4–6% opex reduction for a typical mobile operator in a developed market. The estimate dates to approximately 2019 and should not be treated as a current forecast for every market or operator.
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Before approving a retirement, assess remaining customers and devices, continuity of service, migration costs, regulatory and wholesale obligations, and the intended target architecture. Country-specific shutdown schedules and obligations are not established by this estimate; they need to be assessed for the market in question.
Where can AI help, and how should a pilot be judged?
McKinsey’s February 2026 Issue Brief: AI-driven telecom networks describes operational applications including energy management, field-route and scheduling optimization, and predictive maintenance. It estimates that combined AI-driven operational use cases could reduce total network opex by 15–30%. This is consulting analysis, not an audited industry-wide result or a guaranteed outcome, and it does not quantify implementation or compute costs.
Evaluate AI in a defined workflow rather than buying against a general promise:
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Quick Recap
- Set a baseline for the specific activity, such as energy use, route efficiency or maintenance outcomes.
- Define the operational change the system is expected to support and the service-quality guardrails it must respect.
- Run a bounded pilot, retaining human oversight where the workflow requires it.
- Measure actual outcomes against the baseline, including implementation and operating costs, before deciding whether to scale.
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