Communication Service Provider (CSP) leaders will likely be looking at the myriad headlines about generative AI, what it means for their business and, more importantly, what they need to do about it. It is assumed that you are trying to understand .Simply put, there is no doubt that this new technology will impact the industry. In fact, 40% of all working hours can be affected by generative AI. It is therefore important that leaders understand the opportunities that technology presents when implemented and used responsibly.
While not a magic solution to all of the industry’s problems, generative AI can improve efficiency, reduce costs, and improve customer satisfaction, while also helping CSPs save critical time and money to innovate and improve their businesses. funds can be released.
AI has been around for a long time in the enterprise, so why is generative AI making such a big wave? I’m here. They deciphered the intricacies of language, enabling machines to learn context, infer intent, and create on their own. It can also be quickly fine-tuned for a wide range of tasks.
Generative AI can deliver millions and even billions of dollars in value to CSPs across many potential use cases in the industry. Industry leaders already recognize this. The latest Technology Vision 2023 report found that 64% of CSP executives expect their AI-based models to improve customer service, and 61% believe these models will accelerate new innovation. rice field. Importantly, some of these innovations have the potential to directly impact products and services that are at the core of industry struggles.
strategic approach
While many of the common challenges facing CSPs today can be solved with easily accessible generative AI, most companies need to You also need to fine-tune and customize the model with your own data. So the question arises, where do we start?
There are use cases in many areas of the CSP business, but leaders should have a clear strategy for when and where to invest rather than trying to tackle it all at once. Customer care and sales are key cost areas for his CSP and should be tackled first, as they are likely to benefit the most from generative AI.
Looking back at how AI has been used in customer care so far, it hasn’t always been the success story that companies hoped it would be. Chatbots were introduced to increase efficiency, but they often took too long to resolve issues, leaving customers frustrated and less loyal to the brand.
Generative AI is set to do what chatbots couldn’t do and actually enhance rather than replace the job of customer service. Generative AI-fueled large-scale language models help tackle about 70% of non-trivial customer service communications, providing powerful, intelligent conversational models that understand customer intent and can independently formulate responses You can benefit from bots that are smart. Improve the accuracy and quality of your answers. The result is happier customers, less time spent addressing issues, and freed up time and money to be spent creating new products, services and experiences.
Another area CSPs should invest in as soon as possible is sales and marketing. We’ve been talking about the importance of personalization for years. While progress has been made in this area, generative AI enables hyper-personalization within seconds. For example, a CSP may want to send inexpensive data package add-ons or ecosystem partner-provided messages for live music events. Within seconds, anyone in New York can be customized to receive a message with a picture of Madison Square Garden, while someone in Nashville receives the Grand Ole Opry. Small changes like this go a long way in making people feel that the company is tailoring their offer to them, and as a result, they are more likely to sign up.
next step opportunity
If CSPs can improve both care and sales, the results can be great, but that’s just the starting point for finding new value. Generative AI can also be implemented by CSPs to improve their networks. While traditional AI can provide value in deployment orchestration, planning, and supply chain optimization, the incremental benefits offered by generative AI can extend to designing network site configurations. This allows engineers to quickly validate and fine-tune projects, thus reducing time to market. Other areas to consider include core operations, product development, testing and execution, and quality control.
It is not limited to the telecommunications industry, but also benefits more general tasks across the enterprise. Taking HR as an example, generative AI creates job descriptions and sifts through hundreds of resumes, saving time upfront and allowing humans to focus on recruiting and retaining talent. People’s time can be better spent doing other things that keep employees happy and making the most of their careers.
Major advances in technology bring about cultural shifts
Success with generative AI requires as much attention to people and training as the technology itself. The CSP needs to invest more in people to meet her two distinct challenges of creating AI and using AI. This means both building talent in technical areas such as AI engineering and enterprise architecture, and training people across the organization to understand and work effectively with AI-infused processes. Generative AI solutions free human labor from tedious tasks to free up time for new ideas and innovation. This is a big change in culture, but it is necessary for the industry to thrive.
Cultural skepticism is also part of the telecom industry’s reluctance to move data to the public cloud, but generative AI may be changing that picture. It can be delivered both in the cloud and on-premises, but there are arguments that using the cloud is more cost-effective and sustainable because generative AI requires so much computational power.
beware of challenges
Generative AI should be treated sensitively from a technical, legal, and governance perspective. It is important that generative AI technology is responsible and compliant. This means that AI must be designed, built, and deployed according to clear principles that create trust in it and provide capabilities that can be scaled with confidence. AI systems should be built with a diverse and comprehensive set of inputs to reflect broader business and social norms of responsibility, fairness, and transparency.
At Accenture, we believe CSPs should take a four-step approach to getting the most out of their generative AI investment:
- step 1: Define your vision and identify priority use cases with a business-driven mindset and people-first approach.
- Step 2: experiment. Rapidly prototype high-priority generative AI use cases with curated foundational models to measure impact, adoption, and overall readiness.
- Step 3: Develop a comprehensive activation strategy with a practical implementation roadmap and a sustainable technology foundation.
- Step 4: Implement a robust and responsible AI compliance program with a sense of urgency. This includes controls for assessing potential risks of generative AI use cases during the design phase and means of embedding responsible AI approaches throughout the business.
Once these four steps are successfully executed, it’s time for CSPs to reap the benefits of making significant changes that will help the industry not only survive, but thrive in the future.
