Automation possibilities in the Telecommunications Industry

The telecommunications industry is the driving force behind our ability to stay connected with each other. From mobile communications to internet access, this industry is a vital component of our societies. Naturally, competition is tough in such an important industry. For the companies that offer telecommunication services, to stay relevant in the market and compete with industry leaders innovation should be an integral focus. Mobile communications are always evolving (3G to 4G to 5G), the consumer base is consistently growing and the demand for faster more reliable services needs fulfilling. Companies need to capitalise on industry trends to keep up.


Industry experts list the Metaverse, wearable technology, digital retail, sustainable living and Blockchain as key trends in telecommunications right now. However, a key trend that isn’t mentioned but can drive innovation in the industry more uniquely and stay consistent in telecommunications is automation.


Automation reduces the need of human intervention for tasks and processes, allowing these processes to run faster and be less prone to error. These benefits are achieved through different types of automation: Robotic Process Automation (RPA), Process Mining, Intelligent Document Processing, Decision Management, AI chatbots, Artificial Intelligence (AI), Machine Learning (ML), Analytics and Process Intelligence.


Over the past two years we have witnessed the pandemic speed up digitalisation across all industries. For the telecommunication industry it created an opportunity to capitalise on the growing digital presence, using automation of course.

Some example applications of automation in telecoms include:

  • Using analytics & process intelligence algorithms that apply machine learning and artificial intelligence to predict network issues.
  • Using RPA to automate repetitive tasks for network management like incident and diagnostic management.
  • Deploying AI chatbots to resolve customer enquiries quickly and at unsocial hours.
  • Using process mining to improve the efficiency of purchase order flows for consumer products like broadband, mobile data plans and household WiFi packages.


NetOp is an example of a company developing AI-powered solutions for network management. Their solution can predict, recommend, and be configured to take action to resolve network issues. Although companies like NetOp are implementing automation technologies for telecommunication solutions, it is yet to be the standard in the industry.


As the industry prepares for the next generation of mobile cellular technology, 6G, researchers speculate a fusion of telecommunications and automation technologies. This speculation is more of a reality as several companies including VMware, Nokia, and BT have confirmed to be working towards an intelligent communication network standard.


Possible applications of automation in 6G,

  • Big data, using AI to apply four types of analytics for 6G, descriptive analytics, to return insights on network performance; diagnostic analytics, to detect network faults autonomously; predictive analytics, to predict traffic patterns; and prescriptive analytics, to take actions on predictions.
  • Deep reinforcement learning, an AI technique that can establish a feedback loop between the system and decision maker to resolve data offloading and wireless caching.
  • The applications for automation in 6G are extensive, from AI led analytics to AI technologies like deep reinforcement learning this cellular standard is expected to be the most dynamic. Whatever telecommunications has to offer the world in the next decade, automation will be at the forefront.

 

As the industry prepares for the next generation of mobile cellular technology, 6G, researchers speculate a fusion of telecommunications and automation technologies. This speculation is more of a reality as several companies including VMware, Nokia, and BT have confirmed to be working towards an intelligent communication network standard.


Possible applications of automation in 6G,

  • Big data, using AI to apply four types of analytics for 6G, descriptive analytics, to return insights on network performance; diagnostic analytics, to detect network faults autonomously; predictive analytics, to predict traffic patterns; and prescriptive analytics, to take actions on predictions.
  • Deep reinforcement learning, an AI technique that can establish a feedback loop between the system and decision maker to resolve data offloading and wireless caching.

The applications for automation in 6G are extensive, from AI led analytics to AI technologies like deep reinforcement learning this cellular standard is expected to be the most dynamic. Whatever telecommunications has to offer the world in the next decade, automation will be at the forefront.

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