23 February 2026
Monetisation of AI in the telecoms sector: Who will capture the value?
Artificial intelligence has rapidly become a strategic priority across the telecom sector. Operators are embedding AI into network planning, automated assurance, customer service and fraud detection. Vendors are repositioning portfolios around AI-enabled capabilities. Policymakers are increasingly focused on data governance, digital sovereignty and computational infrastructure.
Much of the industry narrative centres on efficiency gains and improved customer experience. AI is expected to reduce operating costs, enhance automation and support more sophisticated service delivery.
However, the more fundamental question is not whether AI can improve telecom performance – but whether it will materially improve telecom economics.
The sector has previously experienced waves of technological change that generated significant value at the ecosystem level without necessarily strengthening the structural position of connectivity providers. As AI becomes embedded across the telecom value chain, operators and regulators alike should consider where economic surplus is likely to accrue.
Will AI primarily reinforce telecom’s role as an increasingly efficient infrastructure provider? Or could it enable operators to capture a greater share of value through differentiated services, data-driven monetisation and new enterprise propositions? SK Telecom, for instance, has pivoted its entire identity toward becoming an “AI Company,” implementing an “AI Pyramid Strategy” that integrates proprietary AI chips and data centres to transform core mobile operations and scale its “A.” digital assistant into a global consumer platform. The answer to these questions will depend not only on the pace of AI deployment, but on strategic positioning within the emerging AI value chain.
How can AI create value in the telecoms industry?
AI creates economic value in telecoms across three broad domains.
1. Operational efficiency
The most immediate and tangible impact of AI is in enabling mobile operators to more actively manage their cost base and network investments, with some of the most common use cases including:
- Predictive maintenance and fault detection
- Automated network optimisation and capacity planning
- AI-driven customer service and chatbots
- Fraud detection and revenue assurance.
These applications can reduce opex and capex, improve service quality and enhance network resilience. For many operators, this represents the first wave of AI deployment. An industry benchmark for this is Orange, which implemented AI-driven energy-saving software to trigger “micro-sleep” modes and reduce power opex across Europe and Africa. Similarly, Airtel partnered with Nokia to deploy AI/ML algorithms that dynamically adjust power usage based on real-time traffic patterns. AT&T also utilized AI and digital twins to optimize 5G cell placement, ensuring maximum coverage with fewer towers to significantly reduce capex.
While economically meaningful, efficiency gains are primarily meaningful in enabling operators to protect their financial margins. They may stabilise profitability but are unlikely, in isolation, to reverse long-term revenue stagnation in the mature telecom market environment.
2. Commercial monetisation
Next to operational efficiency, AI enables more sophisticated commercial strategies. Advanced analytics allow for informed decisions, such as T-Mobile US partnering with OpenAI to use AI-driven behavioural data to proactively target churn and personalize bundles. In the enterprise market, AI can help operators deliver more consistent service by anticipating potential performance issues, managing service commitments more proactively, and adjusting network resources in line with changing demand.
In theory, AI could enable operators to move beyond static tariff structures towards more granular, usage-sensitive or performance-based pricing models. A theoretical extreme might resemble an ‘Uber‑style’ model, where data prices vary dynamically based on network load, but implementing anything close to this would require major changes in product design, commercial systems and regulatory interpretation.
3. New AI-enabled services
A third, more structural opportunity lies in AI‑enabled services for enterprises and industry verticals. These offerings apply AI to improve how organisations run their own operations, rather than simply enhancing the telecom network. Examples include:
- Edge AI for low‑latency industrial applications, such as real‑time quality control, robotics coordination or automated safety systems
- Data‑driven analytics services that help enterprises extract insights from their operational, sensor or network data
- AI‑enhanced private networks, where on‑site connectivity is combined with AI functions for tasks like automated monitoring, traffic prioritisation or security management.
In these areas, operators may look to integrate connectivity with edge computing. Singtel’s “Paragon” platform is a leading example; it provides an all-in-one orchestration platform for 5G, edge computing, and AI, allowing enterprises to deploy AI video analytics or robotic kits at the network edge.
However, these markets are competitive and shaped by hyperscalers. Operators entering these segments therefore need a clear view of where they can credibly differentiate.
Redefining MNO Priorities in the AI Era
Taken together, these domains translate into three overarching strategic positions for operators: using AI primarily to defend margins, to differentiate commercially, or to compete for higher‑value roles in enterprise solutions.
However, operators’ ability to pursue these strategies depends on where power and value reside within the emerging AI ecosystem. To better understand this, it may be useful to examine how capabilities are distributed across the broader AI ecosystem. A simplified view includes:
- Data – network performance data, customer behaviour data
- Connectivity and infrastructure – fixed and mobile networks
- Compute capacity – large-scale cloud and GPU infrastructure
- Foundation models – including large language models and other AI architectures
- Applications and software – the interface where most customer value is delivered
The figure below demonstrates how the three identified strategic options for telecom operators align with their current position in this value chain.
Telecom operators control valuable data assets and the network layer, but compute infrastructure and foundation model development are increasingly dominated by hyperscale cloud providers and global technology firms. Much of the innovation at the application layer also sits outside the traditional telecom domain.
This creates a strategic tension: does AI strengthen operators’ influence within the value chain, or does it deepen dependency on external providers who control compute and model intelligence? Historical patterns suggest that value tends to concentrate in scarce, high-leverage layers – which today often sit above the network.
Conclusion
Artificial intelligence will undoubtedly reshape telecom operations. The efficiency benefits are now clear, actionable and, for most operators, unavoidable. AI‑driven automation, predictive maintenance and improved service processes are becoming essential to protect margins and maintain competitive baseline performance. In this sense, “margin stabilisation” is not a strategic choice but an operational imperative.
The more fundamental question is whether operators can move beyond this first stage. The potential exists for AI to support new forms of commercial differentiation. But realising these opportunities requires more than technical deployment. It depends on changes in product design, greater flexibility in commercial systems, investment in edge and data capabilities, and a clearer view of where to partner versus where to build.
If these elements do not come together, AI may simply reinforce existing asymmetries in the digital value chain, with higher‑margin value accruing to cloud and technology providers.
For both operators and regulators, the focus therefore needs to extend beyond pilot use cases and deployment metrics. The central issue is strategic: can telecoms convert AI from a cost‑containment tool into a catalyst for repositioning within the digital ecosystem?
The outcome will depend not only on technological capability, but on strategic discipline, ecosystem choices and the evolution of regulatory frameworks over the decade ahead.
