Generative artificial intelligence (GenAI) is a ground-breaking technology which has been made possible by revolutionary foundational models trained on massive amounts of data and dialogues. Advanced Large Language Models (LLMs), such as GPT-4 from Open AI and PaLM 2 from Google, not only enable dynamic human-like interactions, but can also craft novel content by identifying word patterns, relationships, and the context of a user’s prompt. However, the power of GenAI in the telecom industry lies in its ability to access valuable telco’s BSS/OSS data, enabling communications service providers (CSPs) to achieve significant productivity across all areas of the business, says Sue White, head of strategy and marketing, Netcracker Technology.
How GenAI is transforming the telecom industry
Harnessing proprietary telco data and business knowledge are essential to create meaningful use cases that add significant value to the business. With this data, customer care can incorporate LLM-based digital assistants to solve more complex problems and provide agents with valuable support to solve issues faster. From a marketing perspective, GenAI can quickly create personalised promotions and campaigns with text and images generated in a fraction of the time. On the operations side of the house, GenAI can provide valuable assistance to field technicians with instant access to knowledge about networks, topology and planning data to fix issues or install new systems faster. It can also create network and service designs in seconds.
Leveraging the valuable BSS/OSS data will enable operators to improve IT business and network operations to significantly enhance productivity, boost operational efficiency, and elevate customer satisfaction to unprecedented levels. For instance, using customer data, such as billing history and usage data, in combination with GenAI’s real time data analysis and contextual understanding, CSPs can adopt a personalised approach when discussing customer issues and requests, making GenAI-based digital assistants an indispensable front-end tool that replace today’s chatbots. Human agents will also benefit by using GenAI as personal assistants that pulls data from knowledge bases and understanding the customer sentiment, enabling them to quickly provide more accurate responses and in multiple languages.
However, as with all emerging technologies, there are challenges that will need to be overcome before CSPs will reap its full benefits. And this endeavour is not as straightforward as merely training or fine-tuning GenAI models on telco data.
Turning GenAI challenges into opportunities
Much of the data needed for CSPs to unlock the full potential of GenAI is highly sensitive. However, this data is imperative for operators to realise all the benefits this technology offers. Training the GenAI models with open access to sensitive customer data is too risky and will violate privacy laws. Naturally, this is the top concern for CSPs.
In addition, a significant amount of telco data is constantly changing, including usage and inventory data, and some of it in real time. This makes it unsuitable for fine tuning techniques that may work with more static proprietary data in other industries.
To have a real impact in telecom, GenAI interactions and creations also need to be extremely accurate and yet the models are only as good as the data it knows. Advanced LLMs such as ChatGPT have no knowledge about the telco business and its processes. As a result they may produce incorrect responses as the input is ambiguous and not tuned to the specifics of the industry. This know-how is essential to bridge the power of LLMs and with BSS/OSS data. And telcos will need multiple GenAI foundational models that are tailored for specific business needs including those focused on images, designs, or code.
Lastly, large, advanced LLMs to the quality of ChatGPT are extremely costly to build in-house, with significant running costs requiring vast compute resources. OpenAI CEO Sam Altman was quoted in saying GPT-4 models cost over $100 million to train with daily running costs in the order of $700k. These investments are prohibitive for many CSPs.
To overcome these challenges, new approaches are needed to mediate between different types of GenAI models, users, and valuable telco data. By isolating proprietary data from direct access by public models, new techniques can be used to enrich GenAI models with the telco data, context, and knowledge it needs in a safe and secure way to create the highest quality responses.
With these new approaches, some of the most immediate GenAI use cases will most likely be in customer care, with GenAI digital assistants and agent support providing immediate benefit to call centre efficiency. However, with the potential of significant productivity gains across all areas of the business including sales, marketing, business operations, and network operations, it is not surprising GenAI is now a topic of discussion in boardrooms. Teams are being rapidly formed to accelerate the adoption across the entire business.
Realising the true value of GenAI
When GenAI works in conjunction with BSS/OSS data, operators will realise the following benefits:
- Lower costs: Customer support costs will significantly decrease, while the quality of the customer experience will improve. This includes improved first-contact resolution, quicker time to resolution, and reduced cost per contact. On the network and business operational side, telcos can more efficiently use their staff, helping them work much faster.
- Improved support of provisioning and troubleshooting: GenAI has the potential to be highly useful for workforce assignment and dispatch to customer premises. It can offer digital assistants that provide real-time support for provisioning and maintenance. The technology can also provide troubleshooting assistance for premise-based network problems, which can be further personalised based on intelligent customer segmentation.
- Improved time to value: Telcos will be able to increase revenue through the rapid creation of business ideas such as offers, promotions, and discounts. This will enable telcos to close deals faster, as well as quickly design and test new services.
- Improved prediction and optimisation: GenAI can produce synthetic data to improve a sparse data set for model training for predictive maintenance or the detection of unusual calling patterns indicating fraud. The generation of new data sets also has a wider implication in the training of predictive models and improving the optimisation of systems.
- Delivering exceptional customer experiences: Telcos will find that the data GenAI pulls from their BSS/OSS will result in higher net promoter scores, enhanced customer satisfaction, and improved customer effort scores.
It’s only when GenAI uses data from the telco’s BSS/OSS and telco business knowledge that they will realise the power of this revolutionary technology.
Sue White, head of strategy and marketing, Netcracker Technology
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