Telcos are at a turning point as artificial intelligence shifts from experimentation to transformation. In our last piece, we explored how talent and workforce evolution make AI transformation possible. But capability alone is not enough: the real impact comes when AI is translated into products and services that transform networks, redefine customer experiences, streamline operations, and unlock new revenue streams.
The expansion of AI use cases is now gathering pace across the industry, ranging from the highly operational to the boldly transformative. Success will depend on how well operators can scale these applications, and on their ability to build the right partnerships and ecosystems to support them. This blog shares just a flavour of those developments, with a few examples from across the industry. The reality is far broader, but these use cases illustrate how AI is already reshaping telecoms today.
Mapping the Four Pathways to AI Value
To bring clarity to this broad landscape, we have grouped AI applications into four distinct strategic categories:
- 1. AI for Network Excellence
- 2. AI for Customer Experience
- 3. AI for Business Operations
- 4. AI Marketplace and External Monetisation
These categories reflect different objectives, users, and outcomes of AI. Some focus on cost savings and efficiency, others on revenue generation and growth. Together, they show how AI delivers value across the full spectrum of telco activity.
AI for Network Excellence
Networks remain the backbone of telecoms, where AI delivers immediate and measurable benefits. Operators are deploying AI for predictive maintenance, dynamic traffic management, and resource optimisation: enabling smarter, greener, and more reliable infrastructure.
SK Telecom exemplifies this approach, embedding AI into its radio access network (RAN) to improve service quality, reduce outages, and optimise energy consumption. These applications demonstrate how AI can simultaneously enhance customer experience whilst reducing operating costs, creating a win-win scenario.
However, energy efficiency remains a double-edged challenge: whilst AI optimises RAN and transport layer consumption, AI workloads drive new demand in data centres. This tension between AI as an enabler of efficiency and a driver of consumption is a significant industry question. To address this, the GSMA has launched a dedicated research project on the energy implications of AI, with findings to be published later this year.
AI for Customer Experience
If networks are the backbone, customers are the lifeblood of telecoms: here too, AI unlocks significant value. Predictive analytics, recommendation engines, and generative AI assistants are reshaping how operators interact with users, making services more responsive, personal, and effective.
KDDI has recently introduced a generative AI-powered chatbot to manage customer inquiries more effectively, reducing response time by 20%. By applying large language models, the operator can handle complex queries, reduce resolution times, and free up human agents to focus on higher-value interactions. This not only improves customer satisfaction but also drives efficiencies in service delivery.
MTN has taken a different approach, using AI to broaden customer access to financial services. By leveraging mobile usage data, the operator created a telco-driven credit scoring model that allows millions of previously unbanked or underbanked customers to access loans and financial products. This not only strengthens loyalty but also positions the operator as a trusted enabler of financial inclusion.
Together, these examples show that AI in customer experience is not only about reducing costs in call centres or automating processes. It is about building loyalty, protecting ARPU, and expanding the value customers receive from their operator in both digital and financial terms.
AI for Business Operations
AI’s impact extends deep into the internal machinery of business. From HR to finance, supply chain management to fraud detection, AI is helping telcos operate with greater productivity and agility.
Telstra serves as a standout example in this space. In 2023 pilots, tools like Ask Telstra and One Sentence Summary, powered by Azure OpenAI, helped frontline staff respond faster and more accurately to customer inquiries. The result: employees reported improved effectiveness, and customer follow-ups dropped by 20%.
Beyond customer interactions, Telstra has harnessed AI for internal process efficiency across areas like billing, inventory, and HR. Early trials indicate significant time savings and error reduction, freeing staff to focus on higher-value tasks.
While these use cases may not directly generate new revenue, they are crucial for ROI. They reduce costs, accelerate time-to-market, and strengthen organisational readiness, creating conditions for more ambitious AI plays.
AI Marketplace and External Monetisation
The most transformative opportunity, however, lies in external-facing services, where operators move beyond internal efficiency and customer loyalty to capture new revenue streams. By leveraging their unique assets: infrastructure, data, APIs, and trusted relationships, telcos can position themselves as AI enablers for entire ecosystems.
This includes providing GPU-as-a-service, developing sovereign or sector-specific large language models, and launching open data and AI marketplaces. KT Corporation offers a compelling example: building on its in-house LLM, KT has created lightweight sectoral models that can be tailored for enterprises across industries. Similarly, Telefónica, meanwhile, is developing its Kernel platform, a cloud-native environment powered with generative AI that provides a foundation for building and scaling new digital services.
e& is also moving in this direction with the launch of e& enterprise IoT & AI, created through the integration of Smartworld’s AI and IoT capabilities. The division provides scalable services to accelerate digital transformation, enhancing efficiency and productivity across sectors such as smart cities, logistics, and Industry 4.0.
This category brings monetisation into focus most clearly. Here, operators can secure real stakes in the emerging AI economy, moving beyond efficiency gains to shape entirely new business models. Yet this is where the industry has the most work ahead: scaling beyond pilots, defining clear ROI, and proving relevance in competitive ecosystems. To advance this agenda, we have launched focused research to build practical ROI and monetisation frameworks, helping operators convert ambition into sustainable growth.
The Path Forward
At the GSMA AI Forum, a recurring theme has been the relationship between maturity and opportunity. Early-stage operators focus on internal AI for network and operational efficiency, while more mature players explore customer-facing innovations and external monetisation. Successful productisation strategies depend not only on technology but also on ecosystem readiness: regulation, infrastructure, and talent availability all play decisive roles. What works in South Korea or Europe may not yet be possible in Africa or Latin America.
This reality underscores the importance of tailored strategies. There is no single path to monetisation. Operators must align their AI ambitions with their maturity level and the realities of their local markets, from regulation and infrastructure to talent availability and customer demand.
With GSMA-led research underway on both energy efficiency and AI monetisation, the coming year will provide fresh insights into how operators can responsibly scale AI and leverage its benefits. Those who succeed will not only transform their own organisations but also help define the telco’s role in the global AI economy.