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Artificial intelligence in telecommunication field From Trend observation in 2021

The following is the Artificial intelligence in telecommunication field From Trend observation in 2021 recommended by And this article belongs to the classification: artificial intelligence, telecommunication industry .

Accelerating AI investment in Telecom in 2021

COVID-19 has prompted CSP to manage its network and operation in different ways. CSP needs to shift from passive response to more initiative and flexibility in how to allocate and manage cyber source and how to deal with customers. Therefore, CSP will increase its investment in AI tools in 2021. The first tasks include automating network and service operation, obtaining real-time network view and improving digital customer experience.

With AI getting more and more attention, the dialogue between CSP and suppliers should focus on promoting the successful implementation of AI technology. They should pay more attention to how to easily access the data in the system and network assets, and improve the quality of data. The speed of developing, deploying, producing, and managing use case oriented AI models / applications is also important. At the same time, employees should be encouraged to view AI as a tool to improve productivity rather than a threat. Identifying the best approach and framework to promote AI enables CSPs to meet the most important business needs.

By 2021, 58% of operators will increase the expenditure on AI tools; 78% of operators will use AI to realize network operation automation as the most important it project in 2021.

Main information

Network oriented AI continues to dominate in Telecom AI use cases. COVID-19 has led to increased network traffic and increased demand for service experience. Therefore, CSP requires higher and higher network performance and deep visibility of remote operation. For network oriented AI use cases, CSP plans to speed up investment to ensure that network resources are running effectively and available when needed.

Customer oriented AI is essential for customer interaction. The development of digital interaction has promoted the demand of CSP for AI based self-service capabilities – aimed at improving online customer interaction (for consumers and enterprise customers), while improving customer-oriented operational efficiency.

Artificial intelligence, 5g and edge computing drive new business models. With enterprises aware of the need for digital transformation, CSP has the opportunity to combine AI, edge computing and the connectivity provided by 4G and 5g to achieve product diversification and create value for the enterprise market.

Suggestions for telecom operators

Data management is crucial. Data is the key to the development of AI; with the expansion of network, more and more data are created. Effective acquisition and management of these datasets enables CSPs to acquire more intelligence to solve business and operational challenges.

Invest in open interfaces and APIs. This is essential to simplify access to data and to apply insights from AI models to systems and processes.

Model management is a key topic. This problem should be solved when discussing use case implementation. For example, for network related use cases, thousands of models will be deployed to the network, and each model needs to be monitored and retrained to ensure that the strategy is consistent with the development of the network. Instead of thinking about model management afterwards, CSP should consider it as the first task.

The interaction between human and AI system should be considered in operation practice. This is very important to further ensure that decisions and strategies from AI in the CSP environment are consistent with organizational goals.

Network oriented AI implementation is a cross network activity. Don’t operate network oriented AI projects in isolation, because using AI algorithm to modify strategy will affect the operation of other network areas.

CB insights: Q2 2012 venture capital of US $8.1 billion Stanford University: 2021 global artificial intelligence index report artificial intelligence is “industrialized” NVIDIA: 4q20 revenue of US $5 billion Net profit increased by 53% year on year to promote the development of artificial intelligence: control risks and overcome difficulties Baidu: 4q20 revenue 30.3 billion yuan, annual revenue 107.1 billion yuan in 2020 Google parent company: 4q20 teleconference record with the recovery of user activities Gartner: 47% of enterprises plan to increase their investment in Internet of things IBM:3Q20 Revenue down 2.6% Red hat and hybrid cloud business shine McKinsey: development and obstacles of artificial intelligence Report on artificial intelligence in 2020 white paper on the new generation of artificial intelligence in 2020

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