Building AI-ready telecommunication infrastructure: A comprehensive guide

As artificial intelligence (AI) merges with the telecommunications sector, it’s creating a radical shift in the way we share information and connect, writes Guido Voigt, Director of Engineering for Lantronix’s EMEA region. This fusion is giving companies around the globe new abilities to improve how they help customers, understand important usage data, and fight against scammers with more success.

However, like many other industries, the implementation of AI in telecommunications requires an extensive and reliable infrastructure. It’s also important to recognize the challenges and considerations that come with building AI-ready telecommunication infrastructure and address them correctly.

How is Artificial Intelligence currently being used in telecommunications?

The telecom sector has seen incredible changes recently, driven by the rapid advancement of AI. This leap forward has brought forward even more disruptive technologies that are changing the core operations of many organizations. With smart networks that manage themselves and advanced tools for interacting with customers, AI is quickly becoming a game-changer

Smart network management

The telecom industry is stepping into a new phase where smart automation has become critical. The significant volumes of data collected from telecommunications demand that network providers rely on AI-driven systems for more accurate and faster monitoring of traffic.

The real benefit of this cutting-edge technology is its rapid data processing capabilities, enabling immediate identification and resolution of network issues. This not only refines the network management process but also increases customer satisfaction by delivering a more consistent and dependable service.

Enhanced support with AI

Today’s customers expect support services to be available at all times. Telecommunications companies are stepping up to the plate by integrating AI-driven chatbots and virtual assistants. This shift provides consumers with a simple and immediate solution for resolving their inquiries and getting the assistance they require.

These chatbots also allow customer service teams to focus on more complex customer issues, improving the overall quality of service of the provider.

Improved network operations

A major benefit of integrating AI in network management is its ability to fine-tune network performance. AI uses advanced algorithms to manage network data and is able to predict traffic spikes or slowdowns while adjusting resources accordingly.

This information lets companies adopt a more proactive approach when improving how their networks function, cutting down costs and improving connectivity for users. This results in faster internet speeds, fewer call interruptions, and less downtime for the network.

Common challenges with incorporating an AI infrastructure

As artificial intelligence becomes more adopted into diverse sectors, businesses are facing challenges in constructing and maintaining AI systems that meet their specific operational needs. We’ll look into some common issues that arise for businesses during the deployment of AI infrastructure.

Deciding on local hardware versus cloud computing

Businesses planning to integrate AI must make a tough choice – invest in local hardware solutions or embrace cloud computing. Local hardware is a popular choice for its performance and dependability but may come with a steep price, significant space requirements, and higher energy consumption.

Cloud computing, conversely, offers flexibility and can scale with the company’s growth, though it may lag in performance compared to dedicated, on-site options. Balancing cost, performance, and expansion plans is critical in this decision-making process.

Refining AI learning processes

Teaching AI systems how they should operate is a sophisticated and demanding process involving the input of extensive data sets to facilitate learning and enable accurate predictions. The volume of data needed, its quality, and its relevance can all cause substantial challenges for most organisations. The process of refining AI systems can be both time-intensive and quite costly.

Implementing distributed computing architectures

Distributed computing involves arranging complex computational tasks across a network of computers and services for more effective problem-solving, which can improve AI functions significantly.

 

Artilce by Guido Voigt, Director of Engineering for Lantronix’s EMEA region.

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