The top three ways telecom operators can use AI to enhance their operations in 2024

This article was published in Vanilla Plus on April 3, 2024. You can read the original article here.  

In the rapidly changing world of telecommunications, the potential of Artificial Intelligence (AI) has gained significant attention. Recent statistics show that a staggering 60% of C-suite executives are already acknowledging its potential and plan to integrate AI into their operations by 2024. However, amidst the challenges faced by communications service providers (CSPs) and network equipment providers (NEPs) in cost management and network efficiency, the emergence of generative AI (gen-AI) holds immense promise.

Given the challenges and expenses involved in managing extensive networks, it is not surprising that operators are seeking AI solutions. The technology is already expected to significantly transform operations in three critical areas: network planning, optimisation, and fault identification and resolution.

This piece will explore how AI is poised to reshape the telecommunications landscape at the heart of the network while continuing to drive efficiency and enhance quality for end-users.

Network planning

AI can enhance more responsive network planning by introducing a higher level of responsiveness and enabling the correlation of numerous factors. A core determinant for operators to keep pace with demands comes from relying on historical data to predict growth. However, human planners often struggle to identify emerging patterns and deviations from past trends. AI can help transcend these limitations by leveraging sophisticated algorithms to analyse vast datasets in real-time, allowing operators to anticipate changing demands with precision, resulting in more efficient network architecture and resource use.

This enhanced capability enables AI to trigger capacity upgrades in specific locations and optimise network infrastructure accordingly. This is probably why a recent survey found that 70% of solution providers anticipated the highest returns from AI adoption in network planning. Furthermore, AI’s utility extends to identifying underserved areas and devising targeted deployment strategies to reduce network disparity.

However, AI must address concerns regarding data privacy, algorithmic biases, and the need for qualified humans to analyse the results. Additionally, it is challenging to incorporate this technology into existing systems and ensure compatibility with legacy infrastructures, paving the way for disaggregated systems to become the solution.

Network optimisation 

Telcos rely on network optimisation to effectively distribute subscribers and manage traffic across their infrastructure, ensuring the delivery of high-quality service at a reasonable cost. Traditionally, optimising networks was a manual and labour-intensive process, complicated by the sheer volume of nodes, equipment types, and subscribers, so naturally achieving 100% efficiency seemed impossible. However, AI systems have revolutionised these tasks by leveraging real-time data to predict user behaviour and fine-tune network performance accordingly.

So much so, that the same network team can now manage networks 4x larger than before through the use of AI. By analysing data at a highly detailed level, the tech empowers operators to make proactive adjustments, optimising bandwidth allocation and mitigating congestion in real-time. This approach enhances the user experience and maximises operational efficiency for telcos

Fault resolution

Faults and equipment failures are unavoidable realities in any network. However, by using AI as a critical tool for detecting faults that may not be immediately apparent and identifying intricate root causes, the chances can be significantly reduced. This allows telecom providers to take proactive steps to fix problems and prevent outages. For example, some companies are using AI to predict network congestion and proactively reroute traffic to avoid outages. Some CSPs are even building self-optimising networks (SONs) to support this growth, which can optimise network quality based on traffic information by region and time zone. It’s clear that AI’s most notable capability lies in its potential to predict and preemptively resolve faults before they occur, thereby enhancing network reliability and minimising disruptions before they even happen.

AI in a disaggregated network

It is widely known that the effectiveness of AI depends on the quality of input data. Therefore, to utilise AI in improving networks as outlined above, how can we ensure that AI does not lag behind?

Network disaggregation, which separates hardware and software components, offers a straightforward, extensive, and fast data source for networks. By integrating bare-metal switches and managing hardware with software from various vendors, AI can access more data at higher speeds to fulfill its potential. Disaggregated network operating systems can provide more information compared to legacy systems, allowing extraction of various data, such as packet forwarding statistics and hardware fan speeds. This extraction process is made even simpler with a modern Network Operating Systems (NOS) to streamline processes. A cloud-native NOS enables AI systems to subscribe to events and receive instant notifications, facilitating quicker responses to network changes. Moreover, a cloud-native NOS’s microservices grant visibility into network functions, enabling behaviour learning and interaction correlation, to allow for predictive maintenance, fault diagnosis, resource optimisation, and threat prevention. Ultimately, the quality of input data directly impacts AI performance, underscoring the significance of network disaggregation in enhancing AI capabilities within telecommunications.

It’s clear that, as with any process in life, the quality of input directly affects the output. This holds true for AI operations, as the greater the value infused into AI systems, the greater the returns. With network disaggregation, this becomes a whole lot easier. As telcos and the world at large anticipate further capacity demand, AI can help prioritise quality data input through network disaggregation to maximise benefits for telcos and deliver innovations directly to the consumer.