Investment in Artificial intelligence (AI) is transforming telecom operations, offering new opportunities and challenges, GSMA Intelligence report said.
Operator progress and strategies vary globally, but responsible AI implementation remains a key focus. Among surveyed operators, 65 percent have adopted an AI strategy, whether standalone or integrated into broader objectives, highlighting AI’s growing importance in the industry.
Productivity gain is a primary success metric for 68 percent of operators, measured by time savings compared to previous efforts. Cost savings also rank highly, emphasizing efficiency as a priority.
Security remains critical, with 40 percent of operators highlighting a secure-by-design approach as a key impact area in recent years. 85 percent of operators believe cyber threats like DDoS attacks will grow in frequency.
AI activities
Telecom operator KT announced its partnership with Microsoft for AI and cloud enterprise solutions in South Korea.
Japan’s Softbank and T-Mobile US announced their RAN-AI convergence research with Ericsson, Nokia, and Nvidia.
SK Telecom has introduced an AI code of conduct focusing on governance, non-bias, and ethical AI principles.
Telecom operator e& is exploring autonomous networks and responsible AI deployment in collaboration with Ericsson.
Vodafone’s $1 billion deal with Google extends its cloud and AI initiatives.
Qualcomm’s device and network chips emphasize on-device and edge AI, underscoring AI’s expanding role in innovation.
Gains from AI
There is increasing attention on exposing network capabilities through APIs, where AI can play a role in supporting developers. However, operator networks must be prepared to handle AI workloads, with uncertainty surrounding the investment required for AI-native networks. The return on investment from AI deployments is debated, with operators aiming for top-line growth through new services while weighing the risks of externally focused AI use cases.
Customer-facing AI use cases beyond chatbots remain limited, reflecting concerns over AI maturity and risk tolerance. Internally deployed AI use cases are seen as safer, with less public exposure to potential issues. As operators navigate the rapid pace of AI innovation, starting AI journeys is essential to understand use cases and requirements, paving the way for potential returns on investment.
External-facing AI use cases are gaining traction, though operators are cautious about exposing customers to risks associated with new technologies. Collaborations such as Vodafone’s partnership with Google and KT’s with Microsoft suggest a strategy of bundling AI solutions from specialists with connectivity services, requiring robust connectivity assets to succeed.
As AI innovation accelerates, with advancements in LLMs, SLMs, distributed AI, and AI agents, many operators are still in the discovery phase. Rapid upskilling is essential to avoid missing out on these benefits.
AI focus of KT
KT is heavily investing in AI, targeting KRW 1 trillion ($773 million) in revenue from AI-based services by 2025 and committing $5.4 billion to its AI business by 2027. Workforce development is a key focus, with initiatives like the AI Tech Center recruiting developers and training internal teams.
KT evaluates the impact of its AI initiatives through metrics like productivity improvement, cost reduction, and sales growth. Productivity improvements are measured by the time and resources saved post-AI adoption, while efficiency is assessed by the breadth of internal adoption across departments. AI implementation is already yielding tangible benefits: the AI Customer Center (AICC) handles 15 percent of calls and has reduced response times for the rest by 10 percent. KT is scaling its AI workforce, aiming to recruit and train 1,000 specialists annually across all levels.
Since launching its AI journey in 2017 with GiGA Genie, a conversational interface now boasting over 4 million subscribers, KT has consistently advanced its AI capabilities. In 2022, it developed its proprietary LLM, mi:dm, applied to innovations like the Social Robot and AI Customer Center (AICC). By 2024, KT had embraced AI-driven internal innovation to achieve cost reductions, operational efficiency, and new service development.
AI strategy of Telstra
Telstra’s AI strategy aligns with its T25 and beyond-T25 corporate goals, focusing on modernizing its data ecosystem, enhancing AI capabilities, and embedding AI throughout its operations. The primary objectives include improving customer experience, boosting efficiency, ensuring responsible AI use, accelerating strategic progress, and generating revenue through AI-enabled services. Progress highlights include improving over 70 percent of key business processes, developing in-house generative AI tools like One Sentence Summary and Ask Telstra with Microsoft Azure OpenAI, and utilizing AI for network optimization and energy efficiency.
Telstra is advancing its AI capabilities through investments in six dynamic phases and a dedicated group for AI development. Collaborating with partners like Microsoft, AWS, and Accenture, Telstra has built AI infrastructure and established roles to incubate emerging technologies.
During 2023 trials, 90 percent of employees using in-house AI tools like One Sentence and Ask Telstra reported time savings and increased effectiveness, reducing follow-up contact by 20 percent.
Looking ahead, Telstra is exploring and experimenting with emerging AI technologies. Its joint venture with Quantium enhances AI and data science capabilities across sectors such as supply chain, mining, energy, and agriculture, further solidifying its position as an AI-driven leader.
Operator strategies
Most operators recognize the importance of a strategy-driven approach to AI adoption. Around 65 percent have formal AI strategies, either as standalone initiatives or integrated into broader corporate objectives. However, 25 percent still rely on ad hoc approaches, while 10 percent lack any strategy. Many operators are progressing from initial exploration to integrating AI into core operations.
Leadership support is pivotal in driving this transition. Over half of operators report active sponsorship of AI initiatives by their leaders, with 78 percent of those with a formal AI strategy confirming leadership support in change management and addressing related concerns.
Operators are utilizing both core AI and generative AI (genAI) to varying degrees. Core AI has enhanced operational efficiency, with 30 percent of operators deploying it across business areas. Meanwhile, 40 percent are widely deploying genAI. Despite these advancements, a substantial portion (35 percent for core AI and 41 percent for genAI) remains in early stages, focusing on identifying use cases or running pilots.
Around 60 percent of operators have effective frameworks for identifying and prioritizing AI use cases that maximize business impact while mitigating risks. Leading operators deploy AI across an average of nine out of thirteen possible domains. Key focus areas include network optimization, enhancing customer experiences, discovering new revenue streams, improving employee productivity, and boosting operational efficiency.
Early experimenters often use off-the-shelf AI solutions for operational improvements. In contrast, advanced practitioners scale AI across their organizations, emphasizing high-impact, customer-facing use cases. Among surveyed operators, 24 percent frequently adopt off-the-shelf AI products, 30 percent co-develop solutions with partners, 22 percent create custom in-house applications, and the remainder outsource development.
Concerns
Operators are increasingly focused on building AI capabilities, with significant disparities in skills, resources, and investments across regions and company sizes. Skills shortages remain a critical challenge, with 40 percent of operators reporting under-resourcing in AI talent. However, nearly 60 percent indicate adequate or strong resourcing, with Asia-Pacific and Europe leading in talent strength. Retaining AI talent poses difficulties for more than half of operators, and fewer than a quarter find it manageable to recruit and retain skilled professionals. These challenges highlight the importance of targeted investment in AI skills development and retention.
To address these gaps, operators are forming specialized AI teams, allocating dedicated budgets for AI tools and infrastructure, and developing robust platforms and data management systems to facilitate AI integration. Investment priorities vary by region, with North America and Europe leading in deployment and Asia-Pacific operators allocating the highest percentage of their digital budgets to AI globally — an average of 13 percent.
Budget allocation for AI reflects diverse levels of commitment to digital transformation and innovation. Nearly half of operators allocate 5–15 percent of their digital budgets to AI, while 22 percent allocate less than 5 percent.
Operators with annual revenue between $5 billion and $10 billion dedicate the highest proportion, averaging 15 percent, while smaller operators with revenues under $500 million allocate around 9 percent. This mixed landscape underscores the need for strategic planning to maximize AI’s impact in addressing skill gaps, operational efficiencies, and evolving customer demands.
Baburajan Kizhakedath
