Harun Hasić
Product Manager
Haris Hasić
Machine Learning and Artificial Intelligence Consultant
Ready to implement an AI-powered forecasting solution into your wholesale telecoms business? Here’s what to consider before purchasing your own.
What to Look for in an AI Forecasting Solution
AI is continuing to revolutionize the communications industry, and CSPs are responding accordingly. In fact, recent research shows that more than 1 in 6 telco executives are planning to use AI in 2024.
Under these circumstances, it’s important that CSPs differentiate between solutions that use AI for the sake of using AI and those which implement it thoughtfully and bring real benefits. We have already discussed the most popular existing use cases of AI, as well as why it lends itself well to trading and routing management.
Here, we’ll explore what makes a strong AI forecasting solution and provide you with a list of features your platform of choice should have.
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Understanding AI Forecasting Solutions
As we have discussed before, the complexity of the telecommunications industry is such that it can no longer be managed with traditional technology stacks effectively. Traditional BSS tools struggle to combine the wide range of factors that influence an effective prediction. To take the example of trading and routing again, we know that supplier prices change based on a variety of circumstances, from the time of year to geopolitical situations and demand from other customers. This is not something that an outdated software solution can manage accurately and quickly, as it is not suited to the complexity of the modern communications ecosystem.
With the addition of AI and machine learning techniques, forecasting solutions can automatically take into account every market trend and external condition necessary to produce a reliable prediction that can help CSPs build a powerful strategy.
Benefits of AI-Powered Forecasting
Implementing AI-powered forecasting solutions can unlock powerful benefits for CSPs:
- Accurate forecasts: Combining large and complex data sets over long periods of time results in more accurate and reliable forecasts which can inform CSPs’ decisions.
- Higher efficiency: AI can make forecasting processes faster as well as more reliable, saving CSPs time and costs.
- Scalability: Unlike many traditional forecasting methods, AI-empowered forecasting can scale with your business as it is designed to cope with large datasets.
Key Features to Look for in an AI Forecasting Solution
As telcos know all too well, not all AI forecasting solutions are created equal. To get the most benefit out of your investment in AI solutions, you should ensure that it has these necessary features.
Advanced Analytics and Machine Learning Algorithms
Your AI forecasting solution is only as good as the algorithms it relies on. Forecasting algorithms vary greatly in complexity and the approach they take to processing data and making predictions.
Some algorithms, such as ARIMA and Exponential Smoothing were developed very early on (the 1970s and 1950s, respectively) but are still used today. Such “traditional” algorithms rely on well-established statistical concepts to make informed time-series predictions. On the other end of the spectrum are machine learning algorithms, such as Long Short-term Memory and Transformer neural networks, which employ more intricate strategies to overcome the limitations of the “traditional” algorithms.
ZIRA’s Telco AI Platform enables the use of five different forecasting algorithms, allowing CSPs to manage various tasks and data types successfully by training and deploying suitable models. Moreover, the platform offers the option to combine multiple individual models. This method, called ensembling, further enhances the performance by aggregating forecasts made using different strategies.
Using and even combining well-performing models ensures that the forecasting tasks are carried out without removing the complexity of data sets, resulting in more reliable and valuable insights for CSPs.
Data Integration Capabilities
It’s also important that your AI forecasting solution can process different kinds of data and integrate well with your existing systems. The power of AI forecasting can be used across various business areas, from forecasting customer demand to identifying the best routing paths—but only if your forecasting solution can accommodate data sources of all types.
By way of example, ZIRA’s Telco AI Platform can work with virtually any data source, either by using an API or by establishing a simple database connection. As a result, any of a CSP’s time series data can benefit from forecasting without lengthy implementations or a negative impact on data consistency.
User-Friendly Interface
The best AI forecasting solutions take away the complexity of back-end technology for users with intuitive and accessible interfaces that do not require technical expertise to operate. This ensures that the widest possible number of teams can make use of the AI platform, bringing the benefits of AI to the entire organization.
Having a user-friendly interface also decreases the time it takes to train employees so that teams can start enjoying the advantages of forecasting more quickly. For example, the ZIRA Telco AI platform offers a detailed but simple interface, with forecasts made in just a few clicks and rendered in an easily readable format.
Conclusion
The benefits of AI forecasting for wholesale telecoms are significant. However, to maximize them, CSPs should choose their AI forecasting solution carefully, taking into account a variety of factors such as its usability and flexibility and how quickly it can be integrated into their business. In doing so, CSPs can find the correct solution and enjoy the enhanced decision-making and improved profitability that AI solutions can bring them.
