To date, AI has primarily been used in the telecoms sector for purposes such as chatbots and network optimisation. However, there is significant potential for further applications, particularly in predictive AI and forecasting methods for Communication Service Providers (CSPs) and Mobile Network Operators (MNOs).
These service providers have access to a wealth of business support system (BSS) data, and AI offers a unique opportunity to derive valuable, predictive and actionable insights from this information. To achieve optimal results, it’s essential to consider the most effective approaches to implementing AI in wholesale telecoms.
Choosing the right solution for the right reasons
An organisation’s AI solution must align with its business objectives. While many companies are eager to adopt the latest AI tools to drive innovation, investing based solely on popularity can be risky. AI is not just a single tool; it encompasses a range of applications that can address a variety of challenges. Following trends and hype cycles without careful consideration can lead to wasted resources. Successfully implementing AI requires selecting tools that are best suited to an organisation’s processes, goals, and needs, rather than choosing the most popular option.
Researching potential AI applications will help an organisation identify the right solution for its needs. For instance, with predictive AI and forecasting, CSPs and MNOs already possess the historical BSS data needed to predict future trends and will traditionally have used models like ARIMA and exponential smoothing, which are effective but can be limited. In contrast, AI can improve forecasting accuracy by employing machine learning techniques such as recurrent neural networks and transformers, which can handle longer data sequences for more precise forecasts. By taking a research-driven approach to identifying the right application, telecom providers can tailor AI solutions to their specific challenges.
Evaluating an effective solution
Having the right AI solution is essential, but it is not enough in itself. For service providers to make informed and accurate decisions, they need a tailored BSS solution with AI capabilities that addresses their specific needs and challenges. It’s important, therefore, to evaluate the various components that contribute to a successful AI-powered BSS solution.
The effectiveness of such a solution largely depends on the quality of the algorithmic models it employs. The variety and complexity of algorithm approaches can vary significantly, making it crucial for an organisation to explore and compare its options. For example, does the tool use “traditional” algorithms that rely on well-established statistical concepts, or does it employ machine learning algorithms? While machine learning algorithms tend to be more complex, they can overcome the limitations of more traditional approaches by analysing larger and more varied data sources. Additionally, it is worth considering whether the solution employs a single algorithm or multiple algorithms. Combining several high-performing models can lead to more accurate forecasts.
To effectively translate an organisation’s BSS data into actionable insights, the solution must integrate seamlessly with existing data sets and systems, processing multiple sources for the most accurate output. The ideal predictive AI and forecasting tool should be able to ingest and analyse all relevant data types; the more data it can process, the more valuable its forecasts will be. A solution that excludes certain data types may limit its ability to deliver comprehensive insights.
Importantly, while AI tools and solutions can appear complicated, their usage shouldn’t be. A complex tool that requires extensive training or specialised knowledge can create barriers to adoption. Users should focus on gaining insights rather than struggling with technical operations. Otherwise, it can take longer than expected for the solution to deliver tangible value.
Striking a balance
While each of the features mentioned above is important, the key is to find a solution that balances all of them. An AI-powered BSS solution should not only possess advanced technical capabilities but also provide deep data integration and an intuitive user interface.
The adoption of AI and machine learning tools is becoming increasingly common across telecom networks and infrastructures. However, until recently, a viable solution for the BSS layer has been lacking. MNOs and CSPs need access to data that can be converted into predictive and actionable insights to enable proactive decision making, while meeting their specific forecasting and business intelligence needs.
An effective AI-powered BSS solution will empower telecom operators to manage capacity, pricing and traffic fluctuations, thereby removing risk and uncertainty. Furthermore, by analysing huge volumes of complex network and billing data, such a solution will enhance decision-making, helping operators to identify new opportunities and continuously adapt to support emerging use cases.
