How Generative AI is Changing the Customer Service Game

How Generative AI is Changing the Customer Service Game

Whether placing an order, requesting a product exchange or asking about a billing concern, today’s customer demands an exceptional experience that includes quick, thorough answers to their inquiries. They also expect service to be delivered 24/7 across multiple channels.

While traditional AI approaches provide customers with quick service, they have their limitations. Currently chat bots are relying on rule-based systems or traditional machine learning algorithms (or models) to automate tasks and provide predefined responses to customer inquiries.

Generative AI has the potential to significantly disrupt customer service, leveraging large language models (LLMs) and deep learning techniques designed to understand complex inquiries and offer to generate more natural conversational responses. Enterprise organizations (many of whom have already embarked on their AI journeys) are eager to harness the power of generative AI for customer service. Generative AI models can handle complex customer queries, including nuanced intent, sentiment, and context, and deliver relevant responses. Generative AI can also leverage customer data to provide personalized answers and recommendations and offer tailored suggestions and solutions to enhance the customer experience.

How Generative AI Can Disrupt Customer Service

Generative AI represents a powerful opportunity for businesses to increase productivity, improve personalized support and encourage growth. Here are five top use cases where generative AI can change the game in customer service:

  • Conversational search: Customers can find the answers they’re looking for quickly, with natural responses that are generated from finely tuned language models based on company knowledge bases. What’s different is that generative AI can provide relevant information for the search query in the users’ language of choice, minimizing effort for translation services.
  • Agent assistance – search and summarization: Customer support agents can use generative AI to help improve productivity, empowering them to answer customer questions with automatically generated responses in the users’ channel of choice based on the conversation. Generative AI auto-summarization creates summaries that employees can easily refer to and use in their conversations to provide product, service or recommendations (and it can also categorize and track trends).
  • Build assistance: Employees who create chatbots and other customer service tools can use generative AI for content creation and build assistance to support service requests, getting generated responses and suggestions based on existing company and customer data.
  • Contact center operations: Generative AI can perform the repetitive tasks needed to gather the information needed to enhance the feedback loop within a call center. It can summarize and analyze complaints, customer journeys and more, allowing agents to dedicate more time to customers. The insights produced make evaluating performance improvements for enhanced services much easier, so call centers can contribute to revenue generation.
  • Personalized recommendations: Generative AI considers the history of a customer’s interaction with the brand across platforms and support services to provide them with information that is specific to them (and delivered in their preferred tone and format).

To deliver generative AI solutions tailored for each enterprise, IBM Consulting works closely with ecosystem partners including Salesforce, Amazon, Genesys, Five9 and NICE to help clients benefit from open source and other technologies.

Generative AI for Customer Service in Action

As part of a multi-phase engagement, Bouygues Telecom has been working with IBM Consulting to transform its contact center operations with enterprise-ready generative AI capabilities.

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