Managing the data traffic surge: Challenges and AI solutions

With the rapid adoption of 5G, fibre networks, and the internet of things (IoT), global data traffic is expected to grow exponentially.

This surge presents significant challenges for telecom operators, particularly in managing network complexity while ensuring seamless user experiences.

The transition to 5G and fibre technologies has unlocked new opportunities for faster, more connected networks. Globally, the data centre market is projected to grow from $216bn in 2021 to $288bn by 2027, with a compound annual growth rate (CAGR) of 6 per cent.

The pace of growth in the Middle East is even more remarkable, with the market expected to expand from $3.9bn in 2021 to $6.7bn by 2027, reflecting a 10-perfect CAGR.

This expansion is driven by increasing data volumes, the proliferation of smart applications, and evolving user behaviour, particularly in regions investing heavily in digital infrastructure, such as the Middle East and North Africa (MENA).

In MENA, countries like the UAE and Saudi Arabia are leading the way, investing nearly $50bn in smart city projects by 2025, transforming urban landscapes with connected technologies.

As data-intensive applications become more prevalent, managing this surge in traffic becomes a complex task, particularly as networks expand to meet the growing demands of IoT devices and real-time digital services.

AI: A critical tool for managing networks

In response to these challenges, artificial intelligence (AI) has become a necessity in network management. As data traffic grows, manual management is no longer viable. AI is now essential for operators, enabling them to monitor and manage network traffic in real time.

Embedded at multiple layers — from the edge to the core — AI enhances network observability, allowing operators to understand traffic patterns and optimise performance.

AI plays a crucial role in aggregating and interpreting complex data models, which are becoming more intricate as networks grow. With AI’s ability to analyze vast amounts of data, operators can improve network performance, enhance business operations, and even predict traffic surges.

As a result, AI’s contribution to the Middle East’s economy could reach $320bn by 2030, representing 2 per cent of the total global benefits from AI advancements.

AI and 5G: Reducing human intervention

AI’s integration into 5G networks is particularly transformative. Telecom operators are leveraging AI to automate network lifecycle management, reducing the need for human intervention.

Tools like continuous integration and continuous delivery (CI/CD) streamline processes such as test automation and high-level design creation.

By employing AI in these areas, operators can deploy networks faster and more efficiently while improving network performance.

User-friendly AI for telecom

A key priority is ensuring that AI tools remain accessible to less experienced security analysts.

Through user-friendly interfaces and simplified data patterns, even teams with limited experience can leverage AI to improve network security and management, ensuring widespread adoption across the industry.

Energy efficiency and AI

In addition to traffic management, AI plays a pivotal role in optimising energy usage across networks.

With global attention shifting toward sustainability, AI is enabling operators to make dynamic energy-saving decisions based on real-time traffic patterns. This is particularly important in the Middle East, where smart city projects are central to national visions.

By optimising energy consumption, AI ensures that networks are both efficient and aligned with sustainability goals.

Predicting and managing data network congestion with AI

AI solutions span across multiple business domains, including mobile networks, fixed-line services, and cloud solutions.

Each domain uses AI to optimise specific areas of network management. For instance, AI-driven insights are integrated into network assurance systems to help operators predict traffic patterns, detect anomalies, and optimise network performance for end users.

Advanced AI-driven use cases, such as “prescriptive experience”, are being developed to improve customer satisfaction by anticipating and addressing network issues before they affect the user.

Across its global operations, Nokia’s advanced consulting services are helping operators deploy AI solutions that address region-specific challenges. In Europe, for example, AI algorithms are being used to resolve network disruptions caused by environmental factors such as tree growth.

These bespoke solutions illustrate the power of AI in tackling unique problems while improving overall network reliability.

The writer is the head of Advanced Consulting Services Europe, MEA and APJ at Nokia CNS.

Read: Taking AI-driven data centres into the future