Revolutionizing telecommunications with AI-powered customer and employee experiences

How can AI be used in telecommunications?

To meet new customer demands, empower employees, reduce total cost to serve and grow revenue, telecommunications providers should embrace and adopt AI solutions. Here are some options to consider.

• Conversational AI-driven interactive voice response (IVR)
• Knowledgeable, empathetic customer virtual assistants over chat and SMS
• AI-enabled agents to provide real-time coaching
• Interactive kiosks that provide a curated, profile-based experience each time a customer sets foot in a store

These tools, if designed correctly, can help reduce complexity, change the way information is processed, streamline workflows, and free up time for higher value-add activities — all of which can help drive cost savings, improve customer and employee experiences, and boost revenue.

We’ve already helped organizations realize that value for their business.

By implementing GenAI, a telecommunications company enhanced its data validation process for workflows, resulting in improved employee satisfaction (ESAT) scores. This improvement in workflow efficiency led to a better than 10% increase in Net Promoter Score (NPS), indicating higher customer satisfaction levels.

Another telecommunications company successfully deployed AI simulation modeling and optimization techniques to enhance its field service operations. This approach helped improve service tasks and orders, resulting in increased overall revenue, improved order compliance on commitment dates, and reduced operating expenses through a decrease in overtime hours.

Customer AI assistant: A personalized journey to self-sufficiency

Customer journeys are no longer linear. Customers expect brands to meet them where they are with personalized offerings pushed through their preferred channel. Accomplishing that means focusing on solving the right customer issue, at the right time, via the right channel using a multi-modal or omnichannel approach.

To provide a seamless, omnichannel experience to customers, you should offer around-the-clock support in a cost-efficient manner. This is where AI comes in, supported by unified data and context.

We use a hybrid approach with both conversational AI and GenAI to address customer queries. Conversational AI can be used for queries that don’t require frequent updates and don’t have legal implications. GenAI can be a fallback option to address all other customer FAQs by integrating customer-facing platforms with a centralized knowledge repository. By adopting this approach, your organization can significantly reduce call volumes and improve self-service to reduce overall operating costs.

So, what does this look like in practice? As a customer navigates your branded digital footprint, like your website, AI can make personalized offer recommendations throughout the process based on a holistic view of their preferences and previous interaction history. When needed, the AI can smoothly transition to a human agent, providing detailed summaries that allow the agent to pick up where the customer left off.

Service agent assistant: A well-equipped agent is an empowered agent

By the time a customer inquiry reaches an agent, that agent should be adequately equipped with the tools and information they need to effectively engage with the customer. The less time they spend on rote activities, the more time they can spend on revenue-generating and brand-building efforts, such as upselling a customer on a higher speed tier, convincing a company to renew its contract, tacking on a security product to a customer’s SD-WAN purchase — or just building rapport with the customer as the first line of engagement.

Once a customer issue is escalated to an agent, an AI-powered assistant should provide an employee with the interaction history and context of the conversation, and it should offer real-time coaching based on sentiment analysis and natural language processing. The AI assistant could then prompt the agent with next best action/offer recommendations and promote relevant knowledge articles as the service request evolves. GenAI can even produce a conversation summary and transcript in other languages to improve agent comprehension. Automated post-call work can free up more of the agent’s time so they can focus on helping the next customer with renewed empathy and a human touch.

Field operations: Tech rollout or auto-resolution

While many telco providers have already started automating network maintenance activities and sending proactive outage messages to customers, some issues inevitably require involvement from the field. But before a technician is dispatched, an attempt at preemptive or self-resolution should have already taken place. The technician’s daily appointment schedule should be updated in real time using a smart scheduling model. This model can use advanced simulation and analytics to manage priorities and tasks based on changing factors like customer service level agreements (SLAs), equipment availability, task dependencies, system provisioning, traffic patterns and network availability.

By the time they arrive on-site, technicians should receive relevant tasks in an optimized order based on simulations and advanced analytics. Prior customer sentiment analysis can provide guidance on how to interact with the customer. Having this information at a technician’s fingertips can help improve customer satisfaction, reduce the time spent on the job and reduce the need for repeat rollouts. Automated post-interaction surveys could collect even more customer data. This data could be shared with service operations through an ML-driven feedback loop for continuous improvement.

Behind the scenes, a digital twin can help manage your workforce by adjusting staffing levels and skills to match changes in demand. With GenAI, telco providers can use large language models (LLMs) to analyze historical data and provide step-by-step procedures for issue resolution.

By combining customer segments and resolution data, you can better understand how specific issues and resolution time impact customer lifetime value (CLV). This information can be used to recommend personalized training and coaching for field agents, improving both employee satisfaction and customer service.

Retail footprint: The in-store experience

When a customer sets foot in your store, you should strive to figure out their intent as quickly as possible. Are they making a purchase? Inquiring about an issue? Looking for service or repair help? Customers could “check-in” at an interactive kiosk that authenticates them, offers personalized, contextual help based on their interaction history and unique profile, and provides a wait-time forecast based on the issue and qualified on-site staff availability. If a simple equipment return or device swap is needed, computer vision technology could assess and either accept or reject the device using defect/damage detection. Quick resolution of basic requests allows your retail employees to remain engaged with customers looking to make a purchase or learn more about your products.

While that employee talks with a customer, their AI assistant should provide customized upselling and cross-selling prompts based on the customer’s interaction history and persona cluster. By starting the conversation with the right level of empathy and personalization — and consistently identifying these opportunities in real-time based on contextual data points — retail employees can boost their sales potential and productivity.

In the background, forecasting and simulation models could be used to better understand more granular, store-level staffing needs to identify trends that might not be linked to peak hours or holiday shopping. Used in tandem with smart scheduling and automated inventory management solutions, organizations can make sure their retail stores are stocked with the right equipment — and staffed with the right specialists — to help meet customer needs.

Getting started

An organization should reimagine its entire value chain to get more return on its AI investments. Technical implementation is only one part. Understanding where and how to start can be overwhelming. Each company is at a different stage of adopting AI and GenAI solutions into its everyday operations.

Here are some areas to focus on as you start your journey.

1. Be data-driven and outcomes- and value-oriented.
2. Funding big programs can be a challenge. Think about structuring AI programs to fund themselves with incremental value drops at regular intervals to justify ongoing investment.
3. Move beyond use cases, learn quickly and don’t invest too much time in an individual use case. Concentrate on validating a proof of concept, and then look at building a holistic solution that integrates into strategic and operational service solutions used across functions.
4. Review your operating model. Look to unify AI initiatives under a single governing body or center of excellence (CoE) to avoid silos and gain combined advantages across teams.
5. Don’t let imperfect data get in the way of making progress. Use the data you have available now while broader data modernization initiatives are being stood up.

How we can help

PwC has already helped several telecommunications clients with our industry-leading approach to enterprise AI and GenAI architecture. We use a value-focused factory model that scales capability — and a repeatable process to achieve AI first or AI augmented transformation to drive increased value.

We’ll meet you where you are on your AI journey.

• Just starting? We recommend an AI Strategic Assessment for service value chain.
• Do you already know you want to improve your AI capabilities but are unsure how? We recommend an AI Capability, Architecture and Vendor Assessment.
• If your employees are well-versed in AI technologies but lack the structure and direction to deploy tangible solutions, we recommend a service domain-relevant AI Mobilization.

We’re committed to helping our clients reimagine their businesses through the power of GenAI, and we’re investing $1 billion through 2026 to expand and scale our own AI offerings. We’ve built up expansive alliances with large players and emerging solutions. Our AI specialists, data scientists and engineers, and industry specialists are focused on driving AI-powered transformational change for your customers and employees.