Telecom network economics are under structural long-term pressure. Network complexity continues to rise, customer expectations for performance remain high, and cost pressures arise in the context of an expected data traffic growth deceleration in developed markets.
The rise of AI provides multiple opportunities for telcos to turn the tide. For starters, AI can be embedded in telco value propositions offering differentiated services. Operators can also participate in the emerging AI value chain by becoming the AI infrastructure backbone. And telcos can leverage AI to transform their operations from the ground up.
Most directly, in an industry where improved operational efficiency has become central to the networks’ value equation, AI represents a rare opportunity to reimagine the domain and reset its economics, while delivering exceptional performance. According to our latest survey of global telco top executives, the network domain will be one of the primary focus areas for AI deployments during the next two years, alongside customer care.
However, the difference between marginal gains and structural impact lies not in the technology itself but in how operators redesign processes, roles, governance, and budgeting around it. To capture these opportunities, operators need to raise their ambition and apply the same type of discipline to the network domain.
Recent progress in this field is encouraging. One of the loftiest and potentially most consequential ambitions many operators have articulated—a fully autonomous, self-optimizing, self-healing network—is no longer a distant vision, but an achievable goal in the coming years.
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What’s at stake?
AI’s impact on networks spans both capital expenditure (capex) and operating expenditure (opex), across the following three major domains.
1. Value-led planning and simulation
Network planning is shifting from static engineering thresholds to AI-driven, value-based optimization. To complement traditional network planning based on capacity and coverage estimates, advanced machine learning models and digital twins now simulate thousands of rollout and upgrade scenarios before capital is deployed. These simulations consider and estimate impacts on customer experience (CX), traffic evolution, customer churn and average revenue per user (ARPU), and potential competitive moves from other operators.
Operators deploying AI-based planning engines are already reporting success metrics:
- 10 to 20 percent lower greenfield rollout capex
- 15 to 35 percent lower upgrade capex
- Reallocation of 20 to 30 percent of annual capex away from low-ROI interventions
However, AI alone will not deliver results. Operators need to embed those simulation engines into their capital process and governance. Budget decisions need to be explicitly tied to the outputs, planning teams need to be trained to act on and improve model outputs, and engineering teams must provide feedback, so models consider new or unexpected restrictions.
2. AI-driven operations
AI is also reengineering and optimizing network operations across multiple areas: In energy management, AI dynamically optimizes energy consumption by managing sleep features and detects anomalies without affecting service quality. In field operations, route optimization and automated scheduling reduce idle time and unnecessary dispatches. In maintenance, predictive models shift operators from reactive repairs to proactive interventions on critical assets.
Combined, AI-driven operational use cases can reduce total network opex by 15 to 30 percent.
To maximize value, operators need to redesign current workflows around human–AI collaboration, automating workflow steps, eliminating redundant handoffs, coordination efforts, and inefficiencies.
3. Automated repair and self-healing networks
Issue resolution and self-healing capabilities are emerging as one of the most widespread AI applications in network operations. Operators are deploying AI across the entire “issue management journey.” For example:
Advanced anomaly detection and CX monitoring models identify potential faults early, even before customers are aware of a problem. Smart co-pilots and root-cause models analyze historical incidents and equipment documentation to recommend remediation steps, while dynamic matchmaking systems assign tickets to engineers with the most relevant expertise. Critical change agents automatically identify planned updates with disruption risks and design fallback or remediation plans to prevent major disruptions.
At scale, these capabilities have enabled operators to achieve 30 to 70 percent fewer troubleshooting tickets, leading to 55 to 80 percent reductions in network operations center costs, and 30 to 40 percent faster mean time to repair, alongside measurable improvements in customer experience.
The telco reinvention: How AI can fuel value creation
What’s ahead?
As operators move beyond pilots, four AI-enabled shifts are emerging:
- Increased focus on “fixing the foundations:” Network data has historically been fragmented and less mature than commercial data due to its complexity and volume. Scaling AI requires centralizing, governing, and standardizing network data, and defining a hybrid architecture (cloud and on-prem) to support AI use cases and workflow automations at different levels of the network.
- Capturing institutional knowledge: Much network know-how resides in engineers’ experience. To enable network autonomy, operators are systematically codifying this expertise—through document ingestion, interviews, and structured modeling—so AI systems can infer network dependencies and remediation logic. This semantic layer becomes the foundation for self-directed decisioning.
- Growth of network APIs and programmable connectivity: AI-native and enterprise applications increasingly require identity, location, security, and quality-on-demand as programmable network functions rather than static connectivity. Network APIs allow operators to expose these capabilities directly to developers and enterprises, enabling differentiated, usage-based monetization. API coverage already spans around 80 percent of global mobile connections, positioning operators to participate in application-layer value creation rather than remaining transport providers.
- Intelligent network services and cloud interconnect: As AI workloads become more distributed, enterprises need software-defined network services to dynamically manage latency, routing, bandwidth, and regulatory constraints across cloud and hybrid environments.
What does it take to succeed?
Access to AI doesn’t separate leaders from laggards; instead, it’s the ability to embed AI as a continuous management capability across the network life cycle. Top performing operators take a business-led approach to AI, prioritizing use cases by value rather than technology, investing early in data foundations and digital twins, building internal capabilities beyond pilots, and partnering selectively with hyperscalers and AI specialists. Crucially, they recognize that AI is evolving at exceptional speed—models, tooling, and use cases are advancing every month.
AI is now the primary lever to reset network economics, protect margins in mature markets, enable disciplined growth in emerging markets, and unlock new, differentiated revenue streams. Operators that industrialize AI end-to-end can materially outperform peers on ROIC, EBITDA resilience, and long-term strategic optionality.
