Adopting Generative AI for Telcos

Generative AI, like in many industries, has the potential to help telecommunications providers stay ahead of the curve in an increasingly competitive market. By using Generative AI, telcos can improve network efficiency, optimise customer experiences, and create new revenue streams. With 60% of households concerned about monthly price increases and 44% believing their broadband provider could do more to direct them to the best deal, there has never been more pressure to provide exceptional customer service at low prices. 

In this blog post, we’ll explore how providers can tackle pressing challenges using Generative AI, and look at the approach needed to successfully adopt and drive value from this rapidly growing technology.

Current Challenges in the Telecommunications Industry

The telecommunications industry faces various challenges, ranging from common data + AI deployment issues to others that are more industry-specific. These include:

• Low data impact – dealing with high data volumes that are unfriendly and unusable to most, or require significant time and effort to process.
• Network efficiency and automation – growing consumption requires new capabilities to meet demand at a low cost.
• Customer-centricity – the need to provide value to customers, with a seamless experience at every stage of their journey.
• Monetisation and revenue challenges – pressure to create innovative revenue streams after reduced Average Revenue Per User (ARPU) + monetise data through partnerships.

 

AI + ML is helping Telcos optimise operations and satisfy customers…

Traditional AI + ML has already been applied in the telco industry in various ways. Businesses, such as Inmarsat, are using ML for anomaly detection to address any anomalies before they cause an issue and impact customer service. 

Our work with Vodafone and BT has involved developing large-scale MLOps platforms to reduce the time it takes to deploy AI models and enable faster and more innovative data science projects at scale across the business. 

Yet, Generative AI is beginning to uncover a wide range of use cases that have not been accessible with other types of AI + ML. In the next section, we’ll look at how Telcos can get started with GenAI from the perspective of people, processes, and technology. 

Enabling your teams with the right skills for GenAI

Before diving in and developing key use cases, it’s important to ensure that teams are enabled with the right skills and familiarity with the tools that they will be required to use. This is even more relevant for Generative AI, as the tools and services, along with the skills required to use them, are constantly evolving. 

The requirement for upskilling extends beyond just technical teams; in order to effectively implement Generative AI use cases and ensure the business is ready to adopt them, it is essential to build a culture of AI fluency, by:

• Giving your non-technical teams the knowledge to identify opportunities for Generative AI within their roles.
• Enabling technical teams with the skills and expertise to implement use cases effectively.

 

Building technical capability in-house is challenging, and our in-house Academy was built to solve this problem. With courses customised to your business needs, we provide instructor-led upskilling to turbocharge your teams. 

Datatonic Academy is an Authorised Training Partner for Google Cloud and AWS, delivering world-class training to enable businesses to upskill their existing teams and new hires. To find out more, visit Datatonic Academy to hear how you can prepare your team for Generative AI adoption. 

Generative AI must be approached with best practices in mind… 

Processes are an important, yet often overlooked, part of technology development. Especially with innovative and less-tested technologies, users must approach them with best practices in mind; having clear processes can reduce the risk of errors, and improve security & transparency. 

With Generative AI in particular, this includes considering various aspects of Responsible AI, such as thorough testing and implementing guardrails to ensure data is used responsibly and ethically, and to prevent hallucinations (incorrect information that may appear to be true). 

Some easy first steps to ensure the successful adoption of Generative AI are: 

• Choose your use cases carefully – prioritise use cases carefully while considering the risk of each option combined with the need or value of the use case to your business.
• Set expectations correctly – communicate the capabilities and limitations of the system to prevent over-reliance. Anything that is produced by a generative model, should be made clear to the end user that it was indeed generated by AI.
• Apply conventional cybersecurity techniques – ensure that you have applied the established security techniques we have to mitigate security vulnerabilities that can arise from user input.
• Recognise and check for known attack patterns – build up a database of known malicious prompt patterns, and ensure that all user inputs are checked against this for similarity. 
• Use content moderation to filter out unexpected or unwanted behaviour – even when utilising pre-aligned models, the non-deterministic output generated by LLMs should still go through content moderation checks to reduce negative outcomes. 

More details can be found in our blog, Generative AI: Using Large Language Models Responsibly.

Technology is advancing rapidly to enable new GenAI use cases…

Identifying relevant use cases and prioritising them is the first step to understanding what technology is required and where you’ll need to start. Below are some of the most impactful Generative AI use cases for telecommunications companies.

Generative AI in the Telecommunications Industry

Generative AI has several applications in the telecommunications industry, from creating personalised marketing messages to optimising network performance. For example, Generative AI can be used to generate text messages or email communications that are tailored to individual customers based on their usage habits. 

Additionally, Generative AI can be used to optimise network performance by generating synthetic data that simulates network behaviour, enabling engineers to test and optimise network configurations without disrupting real users. Let’s take a look at some types of use cases in more detail.

Transforming customer or employee experiences

Generative AI can help telcos improve customer experiences by enhancing Contact Centres with technology and freeing up support teams’ time to provide better and more personalised interactions. Businesses can apply foundational models, conversational AI, and search technologies to quickly and easily create multimodal chatbots and personalised self-serve customer experiences. 

This reduces the strain on current support teams, allowing them to focus on high-priority issues, and provides customers with faster and more personalised interactions, leading to lower waiting times and higher customer satisfaction, potentially reducing customer churn.

Improving network planning and operations

Generative AI can also be used to improve network planning and operations, through Network Capacity Planning. This includes processing customer experience and unstructured inputs to enhance capacity prediction models, which can be further augmented with historic usage data. Using GenAI to generate recommendations that feed into and help automate the network design cycle can also help to improve planning.

This has several benefits, for both the business and its customers. Firstly, this will increase prediction model reliability, leading to better and well-planned spending on new infrastructure. For the customer, this leads to improved network performance and enhanced customer satisfaction.

Employee Knowledge Search

In an industry with such large amounts of data, teams can spend hours navigating thousands of pages and tables of unstructured data in PDF and HTML formats, looking for the information they need. This reduces the time available for analysis and other important tasks.

Generative AI provides a solution to this, enabling users to quickly find the most relevant data and content via natural language search to boost employee productivity and knowledge sharing. Enterprise search makes this task easier by providing a summary of search results, as well as pointing to all related documents that discuss the same content, significantly boosting the efficiency of analysis. 

Conclusion

Integrating and ensuring the adoption of Generative AI, like any new solution, requires businesses to consider its people, its processes, and the integration of the right technology. By doing so, Telcos have the potential to drive business growth through enhanced customer experience, and improved network planning, alongside other efficiency gains.

To find out more about how your business can benefit from GenAI, visit our Generative AI page, and read our Telco insights and solutions here.