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The transformative potential of generative AI for the communications industry

Generative AI can achieve millions or even billions of pounds of value for a CSP across the many potential use cases for the industry.

Guest author

May 2, 2023

8 Min Read
The transformative potential of generative AI for the communications industry
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Telecoms.com periodically invites expert third parties to share their views on the industry’s most pressing issues. In this piece Andy Walker, global Communications & Media lead and Davide Bellini, managing director, Accenture, look at some of the implications of generative AI technology for the telecoms sector.

Generative AI is dominating headlines right now and all and most, if not all, CSP leaders are grappling with what it means for their business. It’s no secret the communications industry is challenged at present, so could this be the silver bullet to transform the sector? In short, no. But it can drive efficiencies, cut costs and therefore, free up time and money that is critical for CSPs to innovate and make crucial changes to their businesses to allow that transformation to happen.

The large language models (LLMs) and foundation models powering these advances in generative AI are at a significant turning point. They’ve cracked the code on language complexity enabling machines to learn context, infer intent and be independently creative; they can also be quickly fine-tuned for a wide range of tasks. In fact, 40% of all working hours can be impacted because of these new systems.

So, what exactly does it mean for telecoms? Generative AI can achieve millions or even billions of pounds of value for a CSP across the many potential use cases for the industry. Our recent Technology Vision 2023 report found that 64% of CSP executives expect AI foundation models to improve customer service, and 61% believe these models will accelerate new innovations. And some of those use cases can directly impact the core of the industry’s struggle – product and services.

Where to start

Many common challenges in the CSP industry can be addressed by easily accessible generative AI solutions that are available now, but most companies will also need to customize models, by fine-tuning them with their own data, to make them widely usable and valuable. The immediate focus for CSPs should be on customer care and support which is a big cost for the industry and has the potential to realize millions of pounds of benefits from generative AI.

For instance, most CSPs have invested heavily in chatbots but it hasn’t always paid off. Many customers experience frustrations as they haven’t been understood and what should have simplified the process when contacting a call centre, has increased the time spent dealing with a problem. It also has diminished customers’ respect and loyalty for the brand.

Generative AI will prove to be a game changer because as previously mentioned, the technology has solved the issue when it comes to understanding complex language. And contrary to many headlines, generative AI will enhance, not erase customer service jobs. The large language models the fuel generative AI can be useful in tackling the roughly 70% of customer service communication that is not straightforward and can benefit from a conversational, powerful and intelligent bot, understanding a customer’s intent, formulate answers on its own and improve the accuracy and quality of answers.  In some cases, before they even say what they are calling about, the virtual assistant will already know what they want as they are sitting on a goldmine of customer history data. Additionally, if a call can be cut by 15 or 20 seconds each time, it frees up a lot of time that can be better spent creating new products, services and experiences.

It’s not just the customer that can benefit in the call centre either. Think about your local post office. They used to sell stamps and handle posting letters and packages. Now, many of them manage utility and telco related services too. Whilst the person working there won’t necessarily have any prior knowledge of those industries or offerings, they can easily ask generative AI a simple question about how to handle requests in those areas and they’ll be immediately given the information about how to support. This makes the process efficient but also means they won’t have to deal with frustrated customers by spending time working through manuals and systems trying to find the answers they need.

Sales and marketing is another area that CSPs need to consider straight away. We’ve talked about personalisation for years, but generative AI can take that up a level to offer true hyper personalisation at little to no extra cost. For example, as the days are getting longer and we’re starting to think about summer holidays, the CSP might want to send a message to their customers about roaming offers. Within seconds, messages can be tailored so that someone in Rome gets the message in Italian with a picture of the Colosseum, while someone in London gets it in English with Buckingham Palace in the background. Small changes like that can go a long way to making people feel like the company is tailoring offers for them personally and that they’re not just one of many targets.

If the CSPs make improvements in both care and sales, the results could be huge.

Additional areas of opportunity

Generative AI can also be used by CSPs to accelerate innovation in their networks. While traditional AI can deliver value in optimizing deployment orchestration, planning and supply chain, new benefits enabled by generative AI can design and automate network site configurations, allowing engineers to easily validate or fine tune projects, therefore reducing time-to-market.

Other areas for consideration include product development, testing and execution, and quality management. HR processes such as drafting job descriptions can be augmented, procurement can improve standardization of terms and conditions across different suppliers and contracts. Or requests for proposals in the B2B space for bids can be drafted using the technology. Of course, humans will have to validate many of these tasks, but it will reduce the time spent on them.

Embracing a culture shift 

These new ways of working will mean a shift in mindset for the CSPs. Success with generative AI requires an equal attention on people and training as it does on technology. Companies will need to ramp up investments in talent to address two distinct challenges: creating AI and using AI. This means both building talent in technical areas like AI engineering and enterprise architecture, and training people across the organization to understand and work effectively with AI-infused processes. Working with generative AI solutions can free the human workforce from more tedious tasks, freeing up time for new ideas and innovations.

Cultural scepticism also is one reason – amongst others – that the communications industry has been reluctant to move their data to the public cloud, but generative AI may be the catalyst to change that. Like other technologies, it can be delivered both in the cloud and on premise and the pace of innovation in both cases is similar. But because generative AI requires very high computational power, there is an argument that using the cloud would be a more cost-effective and sustainable option.

With opportunities, come challenges…

It’s important to remember that while there are huge opportunities ahead for CSPs using generative AI, it isn’t a magic wand and needs to be treated with care from a technical, legal and governance perspective. It’s critical that generative AI technologies are responsible and compliant: designed, built and deployed AI in accordance with clear principles to engender trust in AI and provide the ability to scale with confidence. AI systems need to be created with a diverse and inclusive set of inputs, so they reflect the broader business and societal norms of responsibility, fairness and transparency.

Taking the opportunities and challenges into consideration, there is a four-step approach CSPs should think about taking. First, define the vision with a business-driven mindset and a people-first approach to identify priority use cases. Second, experiment. Use curated foundation models to rapidly prototype priority generative AI use cases and measure the impact, adoption and overall readiness. Third, set out a comprehensive activation strategy with a practical implementation roadmap and sustainable technology foundation. Finally, perhaps most importantly, the adoption of generative AI brings fresh urgency to the need for every company to have a robust responsible AI compliance program in place. This includes controls for assessing the potential risk of generative AI use cases at the design stage and a means to embed responsible AI approaches throughout the business. Once those steps have been executed with success, it’s time to scale and make huge strides towards a total reinvention of the industry.


AndyWalker-150x150.jpgAs Communications and Media Industry Leader, Andy is responsible for the strategy, offerings, and business, as well as the network of professionals who serve Accenture’s Media and Communications clients around the world. Andy has consulted to Communications, Media and High Tech clients for more than 20 years, and has advised leaders across the industry on issues ranging from their fiber strategy to the profitability of their customers and products to margin improvement efforts.  Andy has been an advisor on multiple mergers, acquisitions and divestitures across the industry, and has helped a number of clients launch new businesses, including new wireless businesses, technology services businesses, and segment specific businesses.

Davide-Bellini-150x150.jpgDavide is an Accenture Managing Director with more than 23 years of consulting experience with Communications and Media industry clients across the world. Currently, Davide is the Global Lead for Communications, Media and Entertainment Industry in Applied Intelligence, Accenture’s division focused on transforming clients’ business with Data and AI at the core.


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