Self-optimizing networks, AI assistants top AI use cases in the telecom industry, says GlobalData

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Over half of surveyed executives rank self-optimizing networks and AI assistants as top growth AI use cases in the telecom industry.  The experts identified  self-diagnostics and self-optimization for mobile networks as one of the telecoms Artificial Intelligence (AI) use cases with the highest growth potential over the next two years, according to GlobalData, a leading data and analytics company.

GlobalData’s report: ‘Artificial Intelligence Adoption, Usage & Investment Trends in the Telecoms Industry’ found that 61% of respondents believed AI use cases had the highest growth potential. Moreover, 58% of respondents believe that AI assistants and chatbots constitute another AI use case with high growth potential.

The GlobalData’s “Artificial Intelligence Adoption, Usage & Investment Trends in the Telecoms Industry” report examines the advancement in adoption of AI within the global telecom industry along with the key benefits influencing the deployment and projected investments in AI over the next two years. The report highlights the use cases and applications that have highest growth potential in driving the implementation of AI in the global telecom industry. Additionally, the report covers the information about the market opportunities expected to influence the investment in AI and challenges/ barriers encountered by telecoms businesses.

The majority of telecom industry executives consider that their organization is in the development phase of implementing artificial intelligence (AI). Due to significant value and potential that AI has to offer to telecom enterprises companies are moving towards AI solutions and use cases. Improved operational efficiency is expected to be the most beneficial factor of AI for telecom companies over next two years. In total, 40% of surveyed industry executives revealed that their company has plans to invest more than US$1 million in AI during 2018-2020. Moreover, the rising popularity of AI applications within the telecom industry is supported by the increasing complexity in networking caused by growing volume of IoT devices, cloud migrations, the increasing number of OTTs and the arrival of 5G. However, lack of skill-sets and making changes to traditional organizational structures are major challenges faced by the telecom companies to adopt AI.




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