In the fall of 2023, a different type of report appeared on the use of AI in the telecommunications industry, particularly on AI research needs adapted to the telecommunications industry. The main difference with other reports is that this one is designed and written For And by Industry.
In November 2022, the GSMA, AND NO, Telephone And the Humane AI Net project (funded by the European Commission) organized a workshop in Munich, Germany, dedicated to the AI research needs of industry. Participating operators included Axiata, O2, Orange, STC, Telefonica, Telenor, Telia, Telstra, TIM, Turkcell and Vodafone. After an introduction of the latest trends in AI research by Humane AI Net partners and an overview of upcoming AI regulations by ETNO, operators began discussing on current and future uses of AI. This discussion was the basis of the “AI research program for the telecommunications industry» which you can access here.
The figure shows the table of contents of the report.
AI research program for the telecommunications industry
Data Foundations
Data foundations are important to AI because they provide the raw material that AI algorithms use to learn and make predictions. Without access to high-quality, diverse and relevant data, AI systems would not be able to perform their tasks effectively. Data foundations also play a critical role in ensuring the scalability and maintainability of AI systems over time. Relevant topics include privacy, data anonymization, and synthetic data.
Evolving AI
Most operators have started using AI to improve their businesses in several aspects, as we have seen. However, a challenge remains: how to extend the use of AI to all aspects of the business: optimization, operations, marketing, customer interaction, new products and services, new business opportunities and horizontally in transformation processes digital. Topics of interest include standardizing “core” telecommunications use cases and industrializing AI with MLOps.
AI applied to the network
The telecommunications industry is already using AI to improve its core infrastructure, the network, in several ways. But there are still many areas of improvement that need more research before they can be applied on a large scale, including 5G core and RAN, near real-time network optimization, network automation, detection anomalies, explainable AI, digital twins and Naas (network as a network). a service).
Operations and Marketing
Operations optimization is one of the most popular areas in which AI is applied, as it provides significant cost savings and increased efficiency. Typical areas where AI is already applied are: next best activity (NBA), churn prediction, smart pricing, credit scoring, device recommendation, product and service recommendation and digital assistant chatbots. Furthermore, this area of application is similar for many sectors. Topics that still need research include real-time AI and the combination of optimization and machine learning to help handle more complex and dynamic optimization problems.
Customer interaction (chatbots and virtual assistants)
Chatbots and virtual assistants help businesses interact 24/7 with their customers in a personalized and real-time manner. Current research topics that need further investigation include proactivity in interaction and better dialogue skills. Greater proactivity helps anticipate user needs and provide relevant information or complete tasks without the user having to specifically request it. However, it is important to find the right balance between personalization and generating the impression of “spamming” the user. Better dialogues involve moving from question-and-answer to conversation, which LLMs enable. Additionally, multilingual chatbots are important for multinational operators.
Responsible business and ethical AI
With the mass adoption of AI, its ethical use and societal impact are becoming increasingly important. Topics that still require research include the ability to perform consistent risk qualification; achieve this throughout the value chain with suppliers, open source and partners; ethical AI tools for bias and explainability; green AI and AI for good.
B2B/B2G data sharing and data economy
Companies hold large amounts of data, which is primarily used for the benefit of their businesses. However, there is also a huge opportunity for data sharing across businesses, within and across industries. This is also called the data economy, an emerging economy that is still in its infancy. Research topics in this area include data set standardization and interoperability, trust and sovereignty, personal data privacy, federated data sharing, and federated data. ML and assurance of ethical use.
Additional research topics
Finally, some additional research topics relevant to the telecommunications industry include AI as a Service (AIaaS), the Metaverse, and Quantum Computing.
The research agenda is accessible via this QR code.
