Telecos around the world are using AI to revolutionize how they operate ICT infrastructure and provide services. Active integration of AI technology into network systems can improve operational efficiency and significantly reduce operation workload by leveraging real-time monitoring, anomaly detection, and performance optimization. SK Telecom draws on its long history and experience as a telecommunications company to continue to innovate the infrastructure space, building AI technology-based infrastructure, such as AI data centers and edge AI networks, and applying them across major business areas.
What is the opinion of an industry expert with decades of experience in telecommunications and ICT regarding the vision and new prospects for the adoption of AI technology in telcos? SKT Newsroom interviewed Claudio Saes, Partner and Telecom Practice Leader at Bell Labs Consulting on the innovation and strategy brought to telecoms by AI. Nokia Bell Labs is a renowned institution in telecommunications technology and home to over 10 Nobel Prize winners. Bell Labs Consulting, a group of Nokia Bell Labs focuses on next-generation technologies such as telecommunications, AI, the Internet of Things (IoT), and autonomous driving. Claudio Saes has been in the telecommunications and ICT industry for more than 25 years and is currently heading the Telco practice at Bell Labs Consulting.
※ This interview is not about a business relationship between Nokia Bell Labs and SKT. Rather, it is more about sharing his general insights with the public, industry members, and other media.
AI Contributes to Network Reliability and Customer Value.
Claudio Saes, Partner and Telecom Practice Leader at Nokia Bell Labs.
Q. How will AI-based networks evolve in the future? What do Telecoms need to do to bring fully autonomous networks to life?
AIOpsAI for IT operations – The application of AI capabilities such as natural language processing and machine learning models to automate and increase efficiency in operational workflows. is expected to evolve by further integrating AI technologies such as machine learning and natural language processing to automate operational workflows and improve efficiency. Incorporating AI into telecommunications infrastructure allows for predictive maintenance and anomaly detection, which can preemptively address network issues and enhance reliability, which are two of the main goals of TM TM ForumA global telecommunications and technology alliance of more than 800 global carriers and big tech companies from approximately 110 countries.’s autonomous network framework. As AI technologies advance, they will enable more sophisticated levels of network autonomy, eventually leading to fully autonomous networks where AI systems can independently manage and optimize network operations.
To fully achieve autonomous networks, telecom companies should focus on:
1. Investing in AI Technologies: Telecom companies must invest in advanced AI technologies that handle complex network management tasks, including predictive analytics and real-time anomaly detection.
Today, there’s a lot of dependency on vendors’ algorithms and hyperscalersCompanies that operate large-scale data centers such as Google, Facebook, Amazon Web Services (AWS), etc.’ products, requiring firm partnerships in the area.
2. Building Robust Data Infrastructure: Establishing a solid data infrastructure is crucial for AI systems to function effectively. This includes creating data lakesA repository for large amounts of structured, semi-structured, and unstructured data in raw form, making it easy to access, process, and analyze data. The name “data lake” is inspired by various streams of water flowing into a lake. to store and process large volumes of network, subscriber, and service data.
3. Implementing Governance and Policies: As networks become more autonomous, it is essential to implement governance frameworks to ensure AI systems operate within predefined policies and ethical guidelines throughout network domains -i.e., RAN, transport, core, etc.
4. Fostering Collaboration and Innovation: Encouraging collaboration between AI experts and telecom engineers can lead to innovative solutions that enhance network performance and autonomy.
Usually, AI experts work in other organizations, such as IT or the Data Office. However, the most successful examples of autonomous networks are led by multidisciplinary teams with both areas of expertise.
5. Continuous Learning and Adaptation: AI systems should be designed and calibrated to continuously learn from new data and adapt to changing network conditions, ensuring they remain effective and efficient over time.
Q. AI is often discussed in the context of operational efficiency, but its impact on customer experience is equally significant. How can telecoms leverage AI to create more personalized and seamless customer interactions?
Telecommunications companies can leverage AI to create more personalized and seamless customer interactions using various AI-driven technologies and strategies. Most of the use cases in this category are led by a mix of in-house expertise and usage of hyperscalers’ capabilities.
Telecoms can leverage AI to enhance customer experience by:
• Personalization: Using AI to analyze customer data and tailor interactions, offers, and services to individual preferences and behaviors. The goal is to meet customers’ needs thoroughly.
• 24/7 Support: Implementing AI-driven chatbots and virtual assistants to provide continuous, instant support, which most large operators already do. The main issue is if these can be improved to lower contact center call volumes.
• Proactive Solutions: Predicting issues before they arise and offering real-time solutions, augmenting human action over more complex network environments.
Key challenges include addressing the data privacy concerns above, ensuring AI’s accuracy and ethics in personalization, and maintaining a human touch in interactions.
Q. While AI is already optimizing network operations, what other areas within telecom companies do you believe could be completely transformed by AI in the near future?
Beyond network optimization and customer interaction, AI has the potential to transform several other areas within telecom companies, such as:
1. Fraud Detection and Security: AI can improve fraud detection and enhance security measures by analyzing patterns and anomalies in data traffic. Machine learning algorithms can identify suspicious activities in real-time, allowing telecom companies to respond quickly to potential threats and reduce fraud-related losses.
2. Revenue Growth and Service Innovation: AI can drive revenue growth by enabling the development of new services and business models. For instance, AI can facilitate the creation of AI-driven services that offer value-added features to customers. Additionally, AI can optimize pricing strategies and identify new market opportunities.
Innovative Thinking and Partnerships are Key in Unlocking New Business Opportunities Using AI.
Q. What are your thoughts on telecoms transitioning into broader roles as leaders in the AI space, rather than only within their own industry? What new business opportunities might this transition create?
Telecoms will need to break their ties with traditional thinking to become relevant players in the AI landscape. This transition could lead to new business opportunities, such as offering AI-as-a-ServiceAI-as-a-Service – A service that provides AI technology in cloud format. AI products that can be used immediately. to other industries, developing AI-driven applications for sectors like government, healthcare, and finance, and establishing partnerships to leverage AI for broader societal benefits.
One critical area for companies with their data centers is that they can optimize data training, inference, and storage, leading to more efficient network management and service delivery. This infrastructure can position telecoms as critical enablers of AI innovation.
Q. How can telecoms leverage AI to create new innovative business models? What strategies should they pursue to fully integrate AI into their broader business vision?
Telecom companies must reorient their mindset and organizational structure to leverage AI to create innovative new business models. Their vast datasets are essential, but nothing will be achieved if the organization’s leaders do not understand and can articulate the transformation strategic objectives and their impact, creating a sense of urgency and demand across the company.
Some strategies will depend on telcos’ distributed presence to explore AI workloads at the edge. Many of these will require partnership and collaboration beyond vendor-buyer transactional partnerships.
If these are done correctly, telecom companies could explore AIaaS and GPUaaSGPU as a Service – A service that provides GPU technology in cloud format., tackling not only telecom services but also exploiting cross-industry AI applications.
Q. Looking ahead five to ten years, what is your vision for the role of AI in telecoms? How do you foresee AI influencing everything from operations to new service offerings, and what will define success in this transformation?
AI is expected to transform the telecommunications industry in five to ten years, influencing everything from operations to new service offerings.
Networks and customer experience will naturally be the first to gather great expertise. Still, telcos must re-orient their strategy to include coopetition (i.e., SK Telecom and DT example), reskill the workforce, and enforce ethical and responsible AI use.
Shareholders and investors scrutinize everything we do in the telecom industry, so there’s enormous financial pressure on revenue growth and return on invested capital. Telecom operators should initially use these technologies to lower operational costs and bring additional topline, making the association with AI technologies crystal clear. If these changes are done right, success will follow.
