Original article

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2023 was the year of artificial intelligence and as we move into 2024 the telecom market is undergoing a massive transformation through the integration of AI.

Telcos are leveraging AI to reinvent multiple network functionalities such as predictive maintenance, customer service and employee workloads.

Through the use of AI, telecom companies can fine-tune network performance, modernize legacy systems, increase scalability and lower operational costs.

This article delves into AI trends we expect to see in the telecom industry in 2024, uncovering additional avenues for growth.

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#1: SECURING THE LAYER 2 DATA CENTER TO CLOUD NETWORKS

The data link layer, or Layer 2, is the layer of computer networking that transfers data and connects it to physical links in the network. Traditionally, layer 2 data exists to package data and create secure connections between data points.

With an increasing number of applications adopting subscription models or in-browser access, sensitive data such as financial and client information may travel through the public internet to and from the cloud, posing data privacy risks. When the data link layer is tied to cloud networks through the use of AI, it streamlines processes and surpasses public internet broadband holdups.

While selecting a reliable cloud computing services provider is essential, the method of connection is equally as important.

After securing a provider that complies with local data storage laws, ensure that your connection is both secure and compliant. Layer 2 access allows organizations to integrate providers seamlessly, treating them as nodes on the private network. In 2024, we will see more companies secure their layer 2 data to the cloud to reap its benefits and ensure a compliant, fast and secure connection.

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#2: PSTN NETWORKS GET A FACELIFT

PSTN, or the public switched telephone network, is a combination of telephone networks used worldwide to support landline phone calls.

A legacy PSTN network is comprised of telephone lines, fiber optic cables, switching centers, cellular networks, satellites and cable systems, allowing users to make landline telephone calls globally.

The system functions through centralized switches where voice signals travel over connected phone lines. PSTN phone lines were historically used for dial-up internet connections but became obsolete with the advent of broadband internet services. Now, there are still 862 million fixed-line telephone subscriptions worldwide.

Legacy networks consist of a blend of PSTN networks that have historically served as the foundation of our digital economy, however, these networks are currently encountering various challenges faced by outdated infrastructure including limited scalability, equipment obsolescence, elevated operational costs and a high demand for increased bandwidth.

To address these issues, telecom network services will undergo a transformative shift in 2024, transitioning from hardware-based to software-based solutions to harness the capabilities of emerging technologies such as AI.

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#3: INTEGRATING AI FOR NETWORK SCALING

The telecom industry has already faced decade-long pressure of increasing costs and economic challenges, making many telcos overly hesitant to invest in new solutions.

However, telcos looking to remain competitive and scale in an evolving market need to keep up with the technology and the industry leaders who have already implemented artificial intelligence.

Staying ahead requires telecom operators to make critical investment decisions that allow for the modernization of customer and employee experiences.

To optimize the advantages of using AI, organizations can identify processes where AI can automate tasks and provide valuable data insights.

The effective integration of AI in the telecom industry also has the ability to boost retention and efficiency and lower operating costs, making it easier to scale than ever before.

AI models can extract valuable insights and patterns for business growth by analyzing extensive amounts of data, however, the accumulation of data alone is not enough; it must align with the organization's strategic goals. The combination of robust data analytics and business strategy is crucial for developing purpose-driven AI initiatives that directly contribute to scalability.