February 21, 2024

Unlock the Full Potential of GenAI

Generative AI (GenAI) has the potential to revolutionize many industries, and telecoms is no exception. At the heart of GenAI are foundational models, trained on massive amounts of data and dialogues.

Examples include Large Language Models (LLMs), such as Open AI’s GPT-4 and Google’s PaLM 2 for text-based communications, but there are many models in the market now that specialize in other forms of content. These GenAI models are powerful, as they can conduct dynamic human-like interactions, as well as craft original content by identifying word patterns, relationships, and the context of a user’s prompt. While these advancements are transformative, the true power of GenAI in telecom requires access to specialized data and a deep knowledge of telecom business processes – and this is no easy task. By adopting a new approach that augments GenAI models with the right data and context, communications service providers (CSPs) gain the ability to significantly boost business productivity and elevate customer satisfaction.

By harnessing the value of GenAI and BSS/OSS data, operators will be able to:

1. Reduce costs: By improving first-contact resolution and reducing cost per contact, customer support costs will significantly decrease, while the quality of the customer experience will greatly improve. On the network and business operational side, staff will be used more efficiently.

2. Enrich provisioning and troubleshooting support: With GenAI, digital assistants can be used to provide real-time support for provisioning and maintenance. It can also provide troubleshooting assistance for premise-based network problems, which can be personalized based on intelligent customer segmentation.

3. Increase revenue: Through the rapid creation of business ideas such as offers, promotions, and discounts, telcos will be able to close deals faster, as well as quickly design and test new services, increasing revenue.

4. Improve prediction and optimization: Using GenAI to produce synthetic data, a sparse data set for model training of predictive maintenance or the detection of unusual calling patterns indicating fraud will be greatly improved. Additionally, the generation of new data has a wider implication in the training of predictive models and improving the optimization of systems.

5. Deliver exceptional customer experiences: The data GenAI pulls from the operator’s BSS/OSS will result in higher net promoter scores, enhanced customer satisfaction, and improved customer effort scores.




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