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Telecom executives should not choose between innovation and operating-expense (opex) control. They should redirect money and talent from repetitive, low-value work into capabilities that lower the cost of running the network, improve customer outcomes, or create revenue with a credible route to market. That means judging initiatives by total cost of ownership and measured results—not by technology labels, pilot counts, or promised savings.
Replace the false choice with an investment portfolio
Telecom operators must keep investing in networks, software, security, and new services while managing recurring costs for energy, sites, maintenance, licenses, cloud, vendors, field work, and customer support. The tension is real: a new platform may require integration, skills, and parallel operation before it produces any savings. But freezing innovation can leave an operator with expensive legacy processes and no credible way to monetize new capabilities.
The useful question is not “innovation or cost cutting?” It is: which spending should stop, shrink, or change so the business can fund the capabilities that matter? Treat the portfolio as three connected horizons:
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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →| Horizon | Typical timing | Focus | Evidence required |
|---|---|---|---|
| Operating leverage | 0–12 months | Automate high-volume work, reduce energy waste, rationalize licenses, improve inventory, govern cloud use, and optimize field service. | Baseline and near-term change in cost per transaction, incident, site, or customer—without deterioration in service. |
| Platform modernization | 12–36 months | Simplify architecture, modernize selected OSS/BSS components, build reusable data and API foundations, and automate network-function lifecycles. | Lifecycle economics that account for migration, coexistence, resilience, skills, and eventual legacy retirement. |
| Growth and differentiation | 24–60 months | Validate network APIs, private networks, edge, security, IoT, managed services, and differentiated connectivity. | A named buyer, a price metric, delivery and support costs, a route to market, and credible gross-margin assumptions. |
The horizons overlap; they are not a promise that every program will pay back on a fixed schedule. Some modernization is necessary to unlock growth, while some near-term savings can fund modernization. Require each initiative to state whether its primary value is structural cost reduction, cost avoidance, productivity, revenue, strategic control, or material risk reduction.
#1 Best Overall
There is evidence that technology efficiency and innovation can reinforce each other, but benchmarks should not be mistaken for a promise to any one operator. McKinsey’s February 2025 benchmarking of more than 20 operators found that top-quartile technology organizations had an IT cost-efficiency ratio nearly 30% lower than peers; the analysis described a potential opportunity equivalent to 1–2 percentage points of revenue. This is an IT-efficiency comparison, not a forecast for total telecom opex. McKinsey’s analysis links stronger results to architecture simplification, portfolio and talent management, cloud, data, and AI capabilities. Meanwhile, GSMA’s 2025 industry analysis found that operators prioritized revenue generation and customer experience over capex and opex savings by a four-to-one margin. Cost control matters, but it is not a complete innovation thesis.
Start with the cost base, not a technology pitch
Map the largest recurring cost pools and the operational drivers beneath them. Depending on the operator, these can include:
- Network operations: monitoring, incident handling, configuration, assurance, maintenance, and network planning.
- Energy and facilities: radio sites, data centers, cooling, backup power, and leased locations.
- Field service: site visits, truck rolls, repeat visits, dispatch, and spare-parts logistics.
- Customer operations: service orders, billing questions, trouble tickets, contact centers, and avoidable repeat contacts.
- Technology estate: software licenses, infrastructure, cloud consumption, support, integration, and duplicated platforms.
- External services: managed operations, systems integration, leased capacity, and vendor support.
- Control functions: security, compliance, resilience, and regulatory obligations.
For each pool, identify the unit of work, its volume, cost, failure or rework rate, service impact, and accountable owner. A large budget line is not automatically a good automation target: the process may be volatile, poorly understood, or risky to change. Conversely, a seemingly small workflow can be a useful pilot if it is repeated at scale and has clean data.
Be precise about what “saving” means:
- Structural opex reduction: recurring resource or cost-to-serve falls and the reduction persists.
- Cost avoidance: the operator avoids a likely future expense, such as hiring, capacity expansion, a truck roll, or equipment replacement. Keep this distinct from cash removed from the current budget.
- Productivity: the organization handles more traffic, orders, or incidents without a proportional increase in resources. This is valuable, but not automatically a cash saving.
- Variable-cost conversion: fixed infrastructure expense becomes usage-based cloud or service expense. The cost profile changes; total cost may rise or fall.
- Cost displacement: expense leaves one team or ledger and appears in another—for example, labor falls while cloud, license, integration, or vendor charges rise.
- Quality-adjusted savings: the net benefit remains after accounting for reliability, customer experience, churn, compliance, and resilience.
Approve benefits using a total-cost view: build and migration costs; parallel legacy operation; cloud compute, storage, networking, and data transfer; licenses; integration; training; security; resilience and disaster recovery; ongoing support; vendor management; and exit costs. Separate hard cash savings from avoided costs, freed capacity, and speculative revenue.
Screen investments for value and reversibility
Use a common scorecard across network automation, AI, cloud, energy projects, APIs, private networks, network sharing, and managed services. Score each against recurring opex impact; time to benefit; revenue and customer impact; reliability and resilience; security and regulatory exposure; ability to reuse it across fixed, mobile, enterprise, and wholesale operations; data readiness; integration effort; vendor dependence; energy effect; and workforce implications.
Every funded program should have a named executive owner and an operational owner, a measured baseline, a hypothesis that can be tested, a 90-day pilot measure, a production-scale target, and a stop-loss or sunset rule. Where feasible, compare against a control group or a pre-implementation benchmark adjusted for traffic, seasonality, and network change. Define in advance who can halt deployment if quality or safety limits are breached.
Reversibility belongs in the business case. Ask how hard it would be to move workloads, data, workflows, models, templates, and operational knowledge away from a cloud platform or supplier. Portability may cost more initially, but its value rises when a service is strategically critical or supplier concentration is already high.
Automate repetitive work before high-consequence decisions
The strongest first candidates usually involve high transaction volume, repeated decisions, stable rules, reliable historical data, low consequences from a temporary error, a clear human override, and accessible APIs or orchestration interfaces. Examples include:
- Service qualification, order decomposition, and routine provisioning for devices or SIMs.
- Alarm correlation, ticket enrichment, incident classification, and trouble-ticket routing.
- Inventory reconciliation and routine checks for configuration or software compliance.
- Capacity forecasting and field-visit prioritization.
- Repeatable customer-service steps with a clear escalation path.
- Energy actions during predictable low-demand periods, subject to coverage and service safeguards.
First fix the process and its data. Inaccurate inventory, inconsistent identifiers, incomplete topology, or fragmented customer and service records undermine both deterministic automation and AI. An automated workflow built on bad records can execute mistakes faster and at greater scale.
Measure the outcome, not just the automation rate. Pair the proportion of work automated with cost per order or ticket, exception and rework rates, time to restore service, first-time-right provisioning, truck rolls per 1,000 customers, and complaint or churn measures. A high automation percentage can conceal the fact that only easy, low-value cases were automated—or that exceptions have become more expensive to handle.
Do not begin with unconstrained autonomous control of high-impact network functions. Stage changes in simulation or shadow mode, then use bounded actions, human approval where needed, explicit thresholds, audit logs, automatic rollback, and escalation. TM Forum’s coverage of autonomous networks describes a shift toward production deployment and a broader focus on quality, resilience, and customer experience—not cost efficiency alone. That is a direction of travel, not evidence that every operator or network domain is ready for full autonomy.
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AI is not inherently cheaper than rules-based software. If a process has a simple, stable rule, a deterministic workflow may be cheaper, easier to explain, and easier to test. Models, data pipelines, inference, monitoring, governance, integration, and specialist skills create continuing costs. An AI recommendation that every expert must review may improve visibility without reducing labor.
Rank #3
Distinguish four operating levels, because their risks and economics differ:
- Descriptive: explain what happened, such as grouping alarms around an incident.
- Predictive: estimate what may happen, such as a fault, demand spike, churn event, or energy requirement.
- Prescriptive: recommend an action for an operator to approve or reject.
- Closed-loop: execute a bounded action automatically, with defined constraints and recovery.
For every use case, assign a data owner and model owner; set performance thresholds and monitor drift; keep audit logs; secure access to operational data and control systems; specify human approval for high-risk decisions; and test rollback and recovery. Track cost per inference or automated transaction, net expert hours removed, exception rates, and the result against a deterministic alternative. Restrict model permissions, segment control environments, and include software-supply-chain and identity controls appropriate to the system. Applicable legal and regulatory requirements vary by jurisdiction and must be assessed locally.
AI has several plausible telecom applications in planning, operations, energy management, and customer experience, but results are use-case dependent. McKinsey’s discussion of AI-driven networks outlines those opportunity areas; it should not be read as a guarantee of operator savings. GSMA Intelligence’s 2026 survey page reports that 85% of operators identified opex efficiency as a priority objective for network AI deployment. That is a statement of priority, not a measured result. Similarly, TM Forum’s 2026 IT-reinvention research surveyed 216 IT executives from 111 operators in 72 countries and identifies agentic AI and network automation among drivers of IT reinvention; it is an industry survey, not proof that a particular tool pays back. TM Forum research.
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Cloud-native and software-defined approaches can speed deployment and upgrades, standardize infrastructure, improve utilization, reduce reliance on bespoke hardware, and support geographic expansion. They can also increase recurring bills, introduce egress and storage charges, require specialist skills, and create new dependencies on tooling or providers. Telco-grade latency, availability, and recovery needs can change the cost calculation substantially.
Do not adopt a “move everything to cloud” target as a proxy for business value. Evaluate each workload and its operating requirements. Model the cost per subscriber, gigabyte, network function, transaction, service instance, site, or region, including standby capacity, observability, data transfer, resilience, and disaster recovery. Identify duplicate legacy and cloud environments and the date or condition for retiring each old component. If workloads cannot be moved easily, model an exit path and its cost.
McKinsey reported that close to one-third of operator workloads, including software-as-a-service workloads, were running in cloud and that operators expected the share to grow substantially. That is a snapshot and expectation, not evidence that cloud is cheaper for every workload. Its operator benchmarking also underscores why architecture, portfolio, and talent changes matter alongside technology.
Consumption-based platforms make unit economics particularly important. For example, AWS Telco Network Builder documentation describes charges based on managed network-function item-hours and API requests, with additional AWS infrastructure and related-service charges. AWS documentation. Google Cloud Telecom Network Automation describes pay-as-you-go pricing based on automated vCPU-hours, with prices requiring a sales conversation. Google Cloud product information. These are examples of pricing dimensions, not like-for-like comparisons or endorsements; assess infrastructure, implementation, integration, portability, and operational effort as well as the platform fee.
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Hybrid and purpose-built approaches may be appropriate for workloads that need on-premises deployment or specialized performance. Open RAN can offer supplier choice and programmability, but integration, testing, lifecycle management, and performance assurance can become ongoing costs. Treat those capabilities as part of the steady-state operating model, not a one-time migration line.
Put energy efficiency on the operating agenda
Energy is a direct cost and a capacity and sustainability issue. Potential levers span radio access networks, data centers, cooling, backup systems, and distributed edge sites: cell sleep or carrier shutdown in low-traffic periods; more efficient radios; dynamic cooling; workload scheduling; traffic engineering; site modernization or consolidation; renewable procurement; and monitoring energy per bit.
Energy controls must be bounded by demand and service conditions. Set traffic thresholds, geographic and rural-site exceptions, automatic wake-up behavior, and protections for emergency traffic, coverage obligations, public-safety needs, service-level agreements, and event-driven surges. Measure energy saved alongside availability, dropped or blocked traffic, coverage, and customer impact. A reduction in electricity that degrades coverage or creates an incident is not a quality-adjusted saving. GSMA includes energy efficiency, circularity, and sustainability among industry priorities in the 5G and AI era. GSMA’s 2025 trends analysis.
Fund growth from buyers and delivery economics
New network capabilities are not revenue streams until a customer buys a service at a price that covers delivery and support. Before scaling an innovation, answer: who is the buyer, what recurring unit will be priced, what must be built and integrated, who supports it, which partners are needed, and what gross margin is expected?
- Network APIs: potential buyers include developers, banks, fraud teams, communications-platform providers, and digital platforms. Adoption, standardization, ecosystem reach, and revenue sharing determine whether an API earns more than it costs to expose and support.
- Private networks: manufacturers, ports, mines, utilities, logistics firms, hospitals, and public-sector organizations may need tailored connectivity. Custom design, integration, coverage commitments, and ongoing support can erode margins.
- Edge: industrial automation, content delivery, gaming, computer vision, and real-time analytics may need low-latency processing. The operator still needs a viable footprint, workload demand, and distributed operations model.
- Security, IoT, and managed services: enterprises may buy protection, asset connectivity, fleet or industrial services, and outsourced operations. Solution delivery and customer support are part of the cost of sale.
- Differentiated connectivity: slicing or other performance and resilience guarantees can matter to enterprises, but require the technical, commercial, billing, and assurance capabilities to deliver on the promise.
Commercial validation should precede broad platform commitments. Pilot with a real buyer and a defined service-level promise; test sales-cycle length, attach rate, delivery effort, support demand, and renewal intent. TM Forum and industry leaders have argued that future network generations should be designed around commercialization, operational simplicity, and ecosystem collaboration. That is a useful design principle, not evidence of automatic demand. TM Forum’s discussion.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Share infrastructure where differentiation matters least
Sharing can reduce duplication and improve asset utilization, but the right layer depends on strategy, regulation, and local circumstances. Options include passive towers, RAN, spectrum where permitted, fiber, edge facilities, wholesale cores, cloud or data-center capacity, API platforms, and managed operations.
Compare expected savings with governance burden, partner dependency, service-level accountability, change-control delays, and loss of differentiation. Sharing a tower may have less effect on customer-facing differentiation than sharing a service platform or an enterprise capability the operator intends to distinguish. Regulatory permissions are jurisdiction-specific; do not assume a sharing arrangement is available or allowed without local review.
Change the operating model along with the technology
A modern platform surrounded by old processes often produces both old costs and new ones. Simplify the product and technology portfolio; retire duplicate platforms and avoid unnecessary variants. Give cross-functional product teams clear outcomes and decision rights. Build platform engineering and reusable components so each product or network team does not solve the same problem independently.
Network and IT convergence needs shared data definitions, API standards, and ownership of inventory and service models. DevSecOps and NetDevOps practices, site-reliability engineering, unified observability, and disciplined release management can reduce handoffs and improve recovery. FinOps should make cloud costs visible to the teams that create them. AI and model operations need owners for performance, access, drift, and change. Vendor management should track not only contract price but service quality, consumption, integration burden, data access, and exit conditions.
Workforce planning is part of the economics. Cutting operational expertise too quickly can leave the operator unable to run the new environment, challenge supplier claims, or recover from failure. Retrain and redeploy people from repetitive work into automation engineering, reliability, architecture, data, security, and customer solution roles. McKinsey’s operator analysis identifies architecture simplification, agile transformation, portfolio and talent management, and cloud, data, and AI capabilities as important dimensions of stronger technology organizations. McKinsey’s benchmarking.
Use a balanced executive scorecard
Review outcome measures together so a saving in one function cannot conceal damage elsewhere. Select a manageable subset suited to the business, and define each metric consistently:
| Dimension | Useful measures |
|---|---|
| Financial and unit cost | Opex per subscriber, site, gigabyte, service order, or trouble ticket; cloud cost per workload or network function; recurring cash saved; cost avoidance reported separately; payback and net present value. |
| Operations and quality | Mean time to repair, incident rate, availability, repeat faults, truck rolls per 1,000 customers, first-time-right provisioning, exception and rework rates. |
| Customer and growth | Complaints, churn, service-order completion, enterprise attach and renewal rates, revenue and gross margin by innovation product. |
| Innovation productivity | Release frequency, time to provision, reuse across domains, cost per automated transaction, and proportion of eligible work automated—paired with actual outcomes. |
| Risk and resilience | Security incidents, control exceptions, recovery performance, rollback success, audit findings, vendor concentration, and tested exit readiness. |
| Energy and sustainability | Energy cost and consumption per site or bit, with coverage, capacity, and service quality tracked alongside it. |
Set baselines and targets before rollout; normalize where possible for traffic growth, subscriber mix, seasonality, and network expansion. Report realized cash savings separately from freed capacity, avoided future cost, and forecast revenue. A pilot is evidence only when it measures the outcome that justified the investment.
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Common traps that erase the business case
- Cloud migration without workload economics: hardware may disappear from one budget while compute, storage, networking, data transfer, resilience, and parallel operations appear elsewhere.
- AI recommendations without net labor change: count expert review and exception handling, not model outputs.
- Energy controls that impair service: include automatic recovery and coverage safeguards, then measure quality with power savings.
- Open architecture without integration capability: supplier diversity does not remove testing, orchestration, and lifecycle costs.
- Legacy coexistence without an exit date: parallel platforms, duplicated data, and multiple skill pools can consume the anticipated benefit.
- Managed services without retained control: define access to data, automation logic, runbooks, operational knowledge, performance measures, and exit support.
- Pilots without commercial owners: demonstrations do not establish buyers, pricing, sales channels, support economics, or margin.
- Activity metrics instead of outcomes: numbers of pilots, APIs, migrated workloads, or automated tasks do not prove value by themselves.
Vendor platforms can accelerate deployment or supply specialist expertise, but compare them on the actual operating problem. For any proposal, clarify the charging unit, what infrastructure and data-transfer costs are excluded, which integrations and professional services cost extra, who owns data and automation artifacts, how multi-vendor operation works, how rollback is handled, and what exit entails. AWS, Google Cloud, Microsoft, ServiceNow, Ericsson, and Nokia publish or describe different platform and managed-service approaches, but pricing and scope are not directly comparable from product pages alone. A vendor’s potential-outcome claim is not an operator result; seek a baseline, contractual accountability where appropriate, and a post-deployment measurement plan.
A practical first 90 days
- Establish the baseline. Map major opex pools and service measures; assign owners and agree on definitions for cash savings, cost avoidance, and productivity.
- Find the avoidable work. Identify the five largest plausible cost opportunities, including energy, repeat incidents, field visits, customer contacts, licenses, and cloud waste. Validate volumes and data quality.
- Choose two bounded pilots. Select low-risk, high-volume workflows with a clear human override. Define a baseline, control or comparison, 90-day measure, service guardrails, and rollback.
- Model cloud and AI unit economics. Include consumption, integration, resilience, monitoring, skills, coexistence, and exit; compare AI with simpler automation where suitable.
- Challenge the portfolio. Stop, defer, or redesign initiatives with no accountable buyer or owner, no credible cost baseline, no path to production, or no measurable outcome.
- Test one growth proposition with customers. Choose an API, private-network, edge, security, IoT, or managed-service use case and validate buyer, price, delivery cost, support, and margin before scaling.
- Set production gates and executive reporting. Require quality and security controls, workforce plans, scale economics, and an explicit sunset decision. Review financial, customer, operational, risk, and energy results together.
The discipline is straightforward even when execution is not: invest in changes that improve the economics or value of the service, make every displaced cost visible, and preserve the capabilities needed to operate a reliable network. Innovation is sustainable when the operator can prove both what it gains and what it will continue to cost.
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