“Why is my broadband so slow?”
This simple customer question hides immense complexity. Telecommunications networks consist of multiple generations of technology and many interconnected systems, making it extremely challenging to predict and prevent network failures. With 6G on the horizon and consumer demand for seamless service greater than ever, providing high-performing connectivity is paramount.
This is where artificial intelligence (AI) shows enormous value. But for AI to succeed, telecommunication companies (telcos) must ensure they have access to large volumes of high-quality data.
From Reactive Fixes to Predictive Maintenance
An Accenture analysis estimates that AI can potentially reduce network downtime by up to 50% for telcos.
This is because AI systems can identify patterns that humans simply cannot. Take outages and network faults. An AI can analyse patterns of weather, layering them with machine learning (ML) algorithms trained on past incidents. By analysing previous instances of adverse weather correlated with network mix and loading, these technologies can recommend preventative measures engineers can take to mitigate an outage or even avoid it altogether.
For example, AI can predict the change in load on different parts of the network when a storm blows through, anticipating whether network consumption might surge in the suburbs and slump in the cities, creating pressure points that could lead to service degradation. AI and ML take much of the guesswork out of engineers’ hands, enabling them to address problems before they become major issues.
AI can also proactively predict surges of traffic and advise customers. Systems can be trained to autonomously manage and optimise network workloads, enabling telcos to make informed decisions about what technologies—from 3G-5G in wireless to copper and fibre in wired networks—should be used at times of high demand.
The pandemic, for instance, put a huge strain on networks as parents worked from home while children streamed entertainment. This stretched fibre networks and slowed speeds dramatically. But with AI powering decisions and identifying bottlenecks, telcos were able to devise solutions, such as advising customers to stream TV on wireless networks instead of fibre to relieve pressure. These data-driven strategies made a huge difference in keeping the network stable and customers happy.
AI Is Only as Strong as Its Data
As AI continues to mature, other use cases will emerge. However, organisations must understand that AI is only as good as the data it learns from before deploying it across the network. Models trained on data from only a subset of an organisation’s data may miss crucial insights or provide incomplete recommendations.
Modelling customer experience based solely on billing data, for example—which remains the standard in many telcos today—ignores the impact of service quality and network performance. Growing billing amounts may indicate a growing dependency on the network and could be interpreted as a satisfied customer. But if intermittent quality issues accompany that dependency, higher spending may actually indicate a higher churn risk.
With the global AI market for telcos projected to grow from $1.2 billion in 2021 to almost $40 billion by 2030, the technology must be unbiased, fair, secure, and well-rounded, relying on clean and accurate data.
Therefore, organisations must build AI use cases on a robust data foundation, giving them access to a complete, clean, and trusted dataset. This will require a modern data architecture built around a unified data platform that enables AI to draw insights from data across the enterprise—from cloud environments to on-premise data centres. Strict governance must also be enforced to ensure consistent levels of data quality.
The Foundation for Future Networks
AI will play a crucial role as telcos look to deliver better service, but the landscape will only become more complex with 6G networks on the horizon. Telcos must prepare now and unify their data so future services are unhindered.
To fully unleash AI’s potential, the industry must prioritise curating diverse, unbiased datasets coupled with thoughtful data practices. AI has the potential to improve the industry, but only if it’s built on a foundation of high-quality, trusted data.
