The Need of AI Applications in the Telecom Industry

Telecom Tech Outlook | Monday, January 07, 2019

Market leaders are developing comprehensive strategies robust strategies for application of cognitive systems that allow new types of automation. They are designing several pilot and production projects that will catapult further innovation. Artificial Intelligence and machine learning are now an established practice in the telecommunications domain. Customer services, chatbots, and network management use AI as their daily driver.

Cognitive systems market development

The latest trend for AI development in telecom is all about cognitive platforms with a laser focus on normal use rather than individual use cases. AI platforms are the main concern for leading mobile service providers despite the widespread use of AI and machine learning across several telecom domains. Massive development is required in spite of the encouraging platforms, and mobile service providers are unsure about where to place their bets.

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According to an open-source project by AT&T called ‘Project Acumos’, open source hardware and software are the best development environment for the telecom AI platforms creating challenges for IT vendors and hyperscale cloud service providers. The open innovation AI platforms application is ideal for telecom use cases. Mobile service providers use these AI platforms to collect and utilize vast amounts of data to improve their operations, offer valuable service to their business partners, and enhance customer experience.  Companies have saved millions of dollars by combining data gathered from the network, their own developed AI algorithm, and connecting human input.

The outlook for telecom applications for AI

Mobile service providers are still debating regarding the investment in the centralized approach of specific AI applications that they currently operate. The right decision will provide a competitive edge with financial benefits making AI one of the best technologies of the future. Expert analysts believe that the power players should adopt open source and get involved in the development of AI solutions.

The ultimate goal behind AI adoption is to reduce staff-related costs, improve efficiency, and increasing revenue. Other key drivers include marketing & engagement, improving customer support, regulatory compliance, operational insight, support business innovation, and fraud detection.

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