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Artificial Intelligence Applications in Telecommunications and other network industries

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  • Balmer, Roberto E.
  • Levin, Stanford L.
  • Schmidt, Stephen

Abstract

Artificial intelligence applications in network industries have the potential to reduce network roll-out and operating costs, improve performance, enhance customer service, and support the development and introduction of new services. This paper identifies and analyzes the range of AI applications already in place and those expected to be in place in the near future. After analyzing the applications found in the literature, we have conducted interviews with senior executives responsible for AI in telecommunications, electricity, gas and water companies headquartered in North America and Europe, as well as with suppliers incorporating AI into their equipment. These interviews have provided information on which AI applications are actually being used in these network industries and which are expected to be implemented in the future. While some of these AI applications, like predictive maintenance, have already been implemented today by most operators, more complex applications, such as traffic management AIs for 5G, are being considered by only a few large operators. Finally, the paper explores the consequences of AI for regulation. At the least, regulators will need to understand AI in order to determine if AI applications raise regulatory concerns. At this point, however, as AI becomes increasingly integrated into network operations, regulatory issues are only beginning to emerge.

Suggested Citation

  • Balmer, Roberto E. & Levin, Stanford L. & Schmidt, Stephen, 2020. "Artificial Intelligence Applications in Telecommunications and other network industries," Telecommunications Policy, Elsevier, vol. 44(6).
    • Handle: RePEc:eee:telpol:v:44:y:2020:i:6:s0308596120300690
    DOI: 10.1016/j.telpol.2020.101977
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    References listed on IDEAS

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    2. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "Economic Policy for Artificial Intelligence," Innovation Policy and the Economy, University of Chicago Press, vol. 19(1), pages 139-159.
    3. Raza, Muhammad Qamar & Khosravi, Abbas, 2015. "A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1352-1372.
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    Cited by:

    1. Lim, Chulmin & Rowsell, Joe & Kim, Seongcheol, 2023. "Exploring the killer domains to create new value: A Comparative case study of Canadian and Korean telcos," 32nd European Regional ITS Conference, Madrid 2023: Realising the digital decade in the European Union – Easier said than done? 277998, International Telecommunications Society (ITS).
    2. Chen, Yan & Zhang, Ruiqian & Lyu, Jiayi & Hou, Yuqi, 2024. "AI and Nuclear: A perfect intersection of danger and potential?," Energy Economics, Elsevier, vol. 133(C).
    3. Amit Kumar Kushwaha & Ruchika Pharswan & Prashant Kumar & Arpan Kumar Kar, 2023. "How Do Users Feel When They Use Artificial Intelligence for Decision Making? A Framework for Assessing Users’ Perception," Information Systems Frontiers, Springer, vol. 25(3), pages 1241-1260, June.
    4. Lim, Chulmin & Rowsell, Joe & Kim, Seongcheol, 2024. "Exploring killer domains to create new value: A comparative case study of Canadian and Korean telcos," Telecommunications Policy, Elsevier, vol. 48(4).

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