Communications service provider (CSP) leaders are likely looking at the myriad of headlines about generative AI, trying to understand what it means for their business, and more importantly, what they need to do about it. In simple terms, there is no doubt this new technology will affect the industry. In fact, 40% of all working hours can be impacted by generative AI. So, it’s vital that leaders understand the opportunities the technology offers if implemented and used responsibly.
It won’t be the magic solution to solve all of the industry’s problems, but generative AI can improve efficiencies, reduce costs and improve customer satisfaction, all while freeing up the time and money that is critical for CSPs to innovate and improve their businesses.
So why is generative AI making such massive waves when AI has been used by businesses for some time now? 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.
Generative AI can realize millions or even billions of dollars of value for a CSP across the many potential use cases for the industry. This is something industry leaders are already acutely aware of. 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. Significantly, some of those innovations can directly affect the core of the industry’s struggle — product and services.
A strategic approach
Today, many common challenges CSPs face can be addressed by easily accessible generative AI, but most companies will also need to customize models by fine-tuning them with their own data to make them widely usable and valuable across the business. So that begs the question, where to start?
Although there are use cases across many areas of the CSP business, leaders shouldn’t try and tackle everything at once, but rather put a clear strategy in place for when and where to invest. Customer care and sales are significant cost areas for CSPs, and also likely to see the most significant returns from generative AI, so should be tackled first.
If you look back at how AI has been used in customer care historically, it hasn’t always been the success story businesses hoped it would be. Chatbots were introduced to create efficiencies, but often took too long to resolve issues, leaving customers frustrated and diminished their loyalty for the brand.
Generative AI is set to do what chatbots couldn’t and actually enhance customer service jobs, not replace them. 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 that understands a customer’s intent and can formulate answers on its own and improve the accuracy and quality of answers. That will result in happier customers, and the time saved on dealing with issues will free up time and money that can be invested in creating new products, services, and experiences.
Another area where CSPs should invest in as soon as possible is sales and marketing. For years, we have talked about the importance of personalization and while we have made strides in this area, generative AI makes hyper personalization possible within seconds. For example, a CSP might want to send out a message to offer less expensive data packages add-ons or ecosystem partner offers for live music events. Within seconds, they can tailor it so someone in New York gets the message with a picture of Madison Square Garden, while someone in Nashville gets the Grand Ole Opry. Those minor changes go a long way to making people feel like the company is tailoring their offers for them personally and as a result, will be more likely to sign up.
The next phase of opportunity
If the CSPs make improvements in both care and sales, the results could be huge, but that’s only the starting point for where new value can be found. Generative AI can also be implemented by CSPs to make improvements to their networks. While traditional AI can deliver value in optimizing deployment orchestration, planning and supply chain, incremental benefits brought by generative AI can go as deep as designing network site configurations. This allows engineers to quickly validate or fine tune projects, therefore reducing time-to-market. Other areas for consideration include core operations, product development, testing and execution, and quality management.
More generic tasks across the enterprise — not limited to the communications industry — can benefit too. Take HR for example: generative AI can write up job descriptions and sift through hundreds of CVs, saving upfront time and allowing humans to focus on recruiting and retaining talent. People’s time can be better spent elsewhere making sure employees are happy and getting the most out of their careers.
With massive technological advances, comes a culture shift
Success with generative AI requires equal attention to people and training as it does on the technology itself. CSPs 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. It’s a huge shift in culture but one that is needed for the industry to thrive.
Cultural skepticism 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 about to change that. Although it can be delivered both in the cloud and on-premise, because generative AI requires extremely high computational power, there is an argument that using the cloud would be more cost-effective and sustainable.
Be mindful of challenges
Generative AI must 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.
At Accenture, we believe there is a four-step approach CSPs should take to get the most out of any investments in generative AI:
- Step 1: Define the vision with a business-driven mindset and a people-first approach to identify priority use cases.
- Step 2: Experiment. Use curated foundation models to rapidly prototype priority generative AI use cases and measure the impact, adoption and overall readiness.
- Step 3: Set out a comprehensive activation strategy with a practical implementation roadmap and sustainable technology foundation.
- Step 4: Put a robust responsible AI compliance program in place with a sense of urgency. 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 these four steps have been taken successfully, it’s time for the CSP to reap the rewards from making those critical transformational changes that will help the industry not just survive in the future but thrive.
