Susan White
Head of Strategy and Portfolio Marketing at Netcracker Technology
Generative Artificial Intelligence (Gen AI) continues to evolve at a rapid pace across numerous key industries. As a result, businesses are keen to invest - but aren’t always entirely sure how.
For the telco industry in particular, AI has the potential to improve the efficiency and sustainability of operations, boost positive customer experiences and drive continued innovation. Telco business leaders stand to benefit from harnessing AI to move beyond legacy systems and enter the next era of the digital age.
With this in mind, Mobile Magazine speaks with Susan White, Head of Strategy and Portfolio Marketing at Netcracker Technology, about the impact of Gen AI within the telco industry. Susan’s expertise encompasses a wide range of technologies, ranging from SDN/NFV to 5G and core networks, with her core focus on developing and improving business strategies.
In this exclusive interview, she speaks about how telco operators are implementing Gen AI, how they can overcome associated challenges and what Netcracker Technology is doing to capitalise on the AI boom.
How are operators currently working to implement Gen AI?
“Critical to driving growth and remaining competitive, operators across the globe are vested in Gen AI success. However, this is where the similarities end, especially when it comes to implementation strategies. To this point, research, conducted by TM Forum, found there’s not a unanimous approach to Gen AI implementation.
“Approximately half of operators plan to devise new implementation strategies, while those that employ AI and machine learning (ML) (50%) plan to integrate Gen AI to complement their existing strategy. Implementation strategies continue to differ with 59% opting to use an open-source cloud model and off-the-shelf large language models (LLMs) for quick deployment and scalability, while 32% are electing on-premise models to mitigate data security concerns, and 21% plan to develop their own proprietary technology.”
What are some of the benefits for telcos that utilise Gen AI?
“While Gen AI has the potential to significantly increase productivity across the telco business and greatly enhance the customer experience, especially in customer care, sales, and operations, the real value of Gen AI lies in its ability to tap into the telco’s business support system and operations support system (BSS/OSS), data and knowledge base systems.”
How can telcos best overcome challenges posed by Gen AI?
“Regardless of the industry, there are many challenges associated with the widespread use of Gen AI.
“This is especially true for the telecom industry who face unique obstacles. Choosing the right model strategy is the first hurdle operators need to address. On-premise custom models appeal to some operators to alleviate security concerns. However, these models require considerable training data, consume vast compute resources, and need AI expertise.
“Other operators are keen to work with readily available advanced public LLMs but are concerned about the leakage of proprietary customer-sensitive data. Plus public models have no knowledge of the telco business or processes which can result in flawed responses.
“There is also a considerable amount of telco data that is constantly in a state of flux, some of which changes in real time – making it unsuitable for fine-tuning techniques on both public and on-premise LLMs.
“Given that approximately 90% of telco Gen AI use cases will require data from BSS/OSS, Netcracker launched its Gen AI Telco Solution, which bridges any Gen AI model with the telco’s BSS/OSS, data analytics, and domain knowledge. This solution sits at the intersection of Gen AI models, users, and the telcos BSS/OSS data, enabling operators to securely harness valuable telecom data and knowledge to deliver high quality and accurate responses. The solution addresses telco-specific scenarios such as care assistant, agent partner, sales assistant, catalogue assistant, and digital operations technician.”
How important is AI to the telco industry overall? How do you see this evolving for the rest of 2024?
“The transformational power of Gen AI has the potential to reshape the telecom industry and resolve some of today’s most pressing issues. Over the next 12 months, it’s envisioned that telcos will use Gen AI to improve their engagements with customers and partners with LLM-based assistants taking on more complex tasks and supporting agents to speed up resolutions.
“It will also play an increasing role in automating areas of business operations, such as catalogue configurations and gathering complex enterprise requirements to create fast service designs for quotation purposes. And then we expect Gen AI to be used in increasingly innovative ways such as automating network planning, installation, configuration, and operations – which has the potential of lowering critical incidents by 35% and reducing performance problems by 60%.
“Every operator will have its unique problems to solve with Gen AI.”
What efforts are you seeing from the telco industry to upskill employees to best handle Gen AI?
“A topic of boardroom discussions across the globe, operators are proactively setting up Gen AI centres and identifying families of use cases. Communications service providers (CSPs) are already making progress in their adoption of Gen AI with 31% running experimentation trials to develop proof of concepts. Getting hands-on with this technology is essential.
“Operators will need to acquire new skills in areas such as prompt engineering and retrieval augmented generation (RAG) to build a variety of use cases. In addition, data experts will be needed to make sure the right data is available from the systems to make the models useful.
“Upskilling employees extends beyond just the technical teams, it needs to encompass the entire organisation to build a culture of Gen AI fluency.”
How can businesses boost their Gen AI use cases?
“The ability for operators to realise Gen AI’s full potential relies on getting telecom domain knowledge and data into Gen AI models. This means enriching Gen AI models in real-time with the right data, context, instructions and knowledge – in a safe and secure way.
“An effective method to access domain-specific information is to augment user prompts with additional information – in the form of real-time BSS/OSS data, context, and instructions. This context-prompt enrichment technique leverages technologies such as prompt engineering and RAG to provide the highest quality responses, securely work with real-time data through API calls to BSS/OSS, access up-to-date knowledge base systems, work with a mix of public and custom-built Gen AI models and lessen the time to resolve a request.
“Operators will also need a choice of Gen AI models. No single model will be optimal for all use cases at the right price points. Building a flexible multi-model strategy from the start will be advantageous in the long run – as this technology is evolving at a rapid rate.”
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