The Top Three Ways Telecom Operators Can Use AI to Enhance Their Operations in 2024
Artificial Intelligence (AI) is becoming a key tool for telecom operators looking to improve their operations in 2024. With 60% of C-suite executives planning to integrate AI, its role is set to expand, especially as communication service providers (CSPs) and network equipment providers (NEPs) face ongoing challenges in cost control and network efficiency.
Generative AI (gen-AI) is gaining traction for its ability to address complex network demands. Operators are focusing on three main areas where AI can make a real difference: network planning, network optimisation, and fault identification and resolution. Here’s how AI can help telecom operations run smoother and deliver better service.
Network Planning
Traditional network planning relies heavily on historical data to forecast demand, but this approach can miss emerging trends and shifts. AI changes the game by processing vast amounts of real-time data to identify patterns and predict future needs with greater accuracy.
This enables operators to plan capacity upgrades precisely where they are needed and optimise infrastructure usage. Additionally, AI helps identify underserved areas, allowing providers to focus deployment efforts and reduce network gaps. According to a recent survey, 70% of solution providers see the biggest ROI coming from AI-driven network planning.
However, challenges remain. AI systems must handle data privacy concerns and avoid biases in their algorithms. Plus, integrating AI with legacy infrastructure can be tricky, which is why many operators are moving toward disaggregated network systems that separate hardware from software for better flexibility.
Network Optimisation
Managing network traffic efficiently is critical for delivering reliable service without overspending. Manual optimisation is time-consuming and limited in scale, but AI changes that by analysing user behaviour and network conditions in real time.
With AI, network teams can manage infrastructure that’s four times larger than before, making proactive adjustments to bandwidth allocation and reducing congestion before it impacts users. This not only improves customer experience but also maximises operational efficiency, helping telcos keep costs down while maintaining quality.
Fault Resolution
Network faults and equipment failures are inevitable, but AI can significantly reduce their impact. By detecting subtle signs of problems early and pinpointing root causes, AI enables operators to fix issues before they cause outages.
Some telecom companies use AI to predict congestion and reroute traffic proactively. Others are developing self-optimising networks (SONs) that adjust performance based on traffic patterns across regions and times. These AI-driven approaches improve network reliability and minimise disruptions for end users.
AI in a Disaggregated Network
The effectiveness of AI depends on the quality and speed of the data it processes. Disaggregated networks, which separate hardware from software, provide a rich and fast source of data that AI can leverage.
Using bare-metal switches and software from different vendors, operators can gather detailed data such as packet forwarding stats and hardware metrics. Modern cloud-native Network Operating Systems (NOS) enhance this by allowing AI systems to subscribe to events and react instantly to network changes.
Cloud-native NOS with microservices offers visibility into network functions, enabling AI to learn behaviour patterns and correlate events. This capability supports predictive maintenance, fault diagnosis, resource optimisation, and threat prevention.
Simply put, better input data means better AI output. Disaggregation makes it easier to collect high-quality data, helping telcos maximise the benefits AI offers. As demand for network capacity grows, prioritising quality data through disaggregated infrastructure will be key to delivering innovations that directly benefit customers.
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