Transforming wholesale telecoms data into profitable decisions with AI

On October 16th, we hosted a webinar with Vanilla Plus covering the AI strategies you need for wholesale telecom growth. Alongside Haris Hasić, an Artificial Intelligence and Machine Learning Consultant, and Jim Morrish, moderator and Co-Founder of Transforma Insights, I explored AI’s current standing in telecoms, where there is potential for growth, and how you can take advantage of that opportunity.

Didn’t manage to make it? No problem! In this blog, I’ll take you through some of the key takeaways and insights that we discussed in the webinar.  

AI in telecoms: Opportunities and challenges

At ZIRA, our mission is to enrich our product portfolios and bring greater benefits to our customers. However, there are challenges to doing so.

With the huge growth of AI, it’s important to determine the most suitable field for its application. We look to strike the right balance of embracing next-generation technology without venturing into tech overkill. There’s also an ethical balance to maintain, ensuring AI implementations avoid any harm.

In the telecom sector, AI has so far been primarily used in chatbots and network optimization, but we recognized the potential for much more, especially in time series forecasting for CSPs and MNOs. With telecom providers sitting on troves of historical data, AI offers a unique chance to unlock valuable, predictive insights.

But what is the optimal approach when you look to successfully implement AI in wholesale telecoms?

Three approaches to AI in wholesale telecom:

1. The business approach

Many companies focus on the latest AI tools as a path to innovation. While adopting trending technologies can drive growth, it’s risky to invest based solely on popularity.

AI is not just one tool; it’s a spectrum that can be applied in countless different ways to address myriad challenges. Simply following trends and hype cycles is the approach that is likely to lead to wasted resources. It’s not about using the most popular tool but what is the best fit for your organization’s processes, goals, and needs.

2. The research approach

A research-based approach carefully assesses the long-term practicality of using AI.

By researching how AI could be used, you can uncover what the right solution is. You might even realize that a certain challenge doesn’t need AI and, therefore, avoid technology overkill.

In time series forecasting, CSPs and MNOs already have the historical data needed to predict future trends, traditionally using models like ARIMA and Exponential Smoothing. While effective, AI can provide more precision by utilizing machine learning methods, such as Recurrent Neural Networks and Transformers, which handle longer data sequences for highly accurate forecasts.

Approaching AI forecasting from a business perspective, one might consider popular tools like Generative AI, but these models can fall short in accuracy compared to traditional methods and often demand higher investment.

If we instead take a research approach, rather than forcing things to fit, we can use our creativity to find how certain technologies and approaches can be applied to a situation. At ZIRA, a research-driven approach allowed us to fine-tune AI specifically for telecom needs. This involved using Generative AI as an action model to generate insights from forecasts, enhancing decision-making for our clients and providing clear, actionable results tailored to their industry.

3. The implementation approach

Implementation is where feasibility meets reality. As the costs of developing and deploying AI soar, careful consideration of ROI and long-term investment is essential. In fact, by 2027 the cost of AI models could reach higher than the predicted US GDP, showcasing just how expensive implementations can be. While investment is necessary, it must both be feasible and capable of delivering ROI. This means trying to balance what investment level is possible for your organization with the results you’re looking to achieve.

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AI-enhanced time series forecasting

CSPs and MNOs have mountains of human-generated data that is full of value. But right now, most providers aren’t using it to the best of their ability. At ZIRA, we took this data and fed it into AI models. And we got great results.

But this wasn’t enough; we wanted to go a step further. As organizations, certain data, such as profitability data, is sensitive and can’t be entered into public AI models. So, we created our own self-sustained closed AI model that our customers could use for time series forecasting without concerns over security.

We use a cluster approach, using multiple AI models to create forecasts. Why? Well, if you ask five random people a question, you’re not likely to get the same answer five times. Likewise, AI models won’t all produce the same results. By taking a cluster approach, we can use the variations in each AI model to create a more accurate result. Then, a Gen AI assistant summarizes the results and suggests the next steps. Our models are also constantly being trained to ensure they continue to deliver the most accurate results.

Conclusion

Our approach to AI in wholesale telecoms demonstrates the transformative potential of strategic AI integration. By examining AI’s capabilities through business, research, and implementation perspectives, ZIRA has discovered ways to maximize AI’s impact on telecom-specific challenges, such as time series forecasting. Our AI solution not only enhances operational efficiency but also empowers CSPs and MNOs to uncover new opportunities in an increasingly data-driven landscape.

Watch the full webinar