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 analyze conversations for context, generate coherent and contextually appropriate responses, and handle customer inquiries and scenarios more effectively. They 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 exciting use cases where generative AI can change the game in customer service:

1. 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.
2. 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).
3. 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.
4. Call center operational and data optimization: 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.
5. 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).

Transforming the contact center with AI

IBM Consulting™ can help you harness the power of generative AI for customer service with a suite of AI solutions from IBM. For example, businesses can automate customer service answers with watsonx Assistant, a conversational AI platform designed to help companies overcome the friction of traditional support in order to deliver exceptional customer service. Combined with watsonx Orchestrate™, which automates and streamlines workflows, watsonx Assistant helps manage and solve customer questions while integrating call center tech to create seamless help experiences.

With the roll out of watsonx, IBM’s next-generation AI and data platform, AI is being taken to the next level with three powerful components: watsonx.ai, watsonx.data and the upcoming watsonx.governance. Watsonx.ai is a studio to train, validate, tune and deploy machine learning (ML) and foundation models for Generative AI. Watsonx.data allows scaling of AI workloads using customer data. Watsonx.governance is designed to provide an end-to-end solution to enable responsible, transparent and explainable AI workflows.

To deliver generative AI solutions tailored for contact centers, 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 call center operations with enterprise-ready generative AI capabilities. Prior to this phase, the European telco engaged with IBM to scale its first four cloud-native AI apps across Amazon Web Services (AWS) cloud, an IBM ecosystem partner.

Despite having 8 million customer-agent conversations full of insights, the telco’s agents could only capture part of the information in customer relationship management (CRM) systems. What’s more, they did not have time to fully read automatic transcriptions from previous calls. IBM Consulting used foundation models to accomplish automatic call summarization and topic extraction and update the CRM with actionable insights quickly. This innovation has resulted in a 30% reduction in pre- and post-call operations and is projected to save over $5 million in yearly operational improvements.

In another instance, Lloyds Banking Group was struggling to meet customer needs with their existing web and mobile application. Within weeks, the IBM team of data scientists, UX consultants and strategy consultants built a proof of concept (POC) to prove that LLMs could improve the virtual assistant  experience by reducing unsuccessful searches, improving virtual assistant performance and personalizing search performance for its customers. The LLM solution that was implemented has resulted in an 80% reduction in manual effort and an 85% increase in accuracy of classifying misclassified conversations.

Navigating the challenges of generative AI

In a 2023 study conducted by the IBM Institute of Business Value, 75% of CEOs surveyed believe the organization with the most advanced generative AI will have a competitive advantage. However, these executives are also concerned about navigating risks such as bias, ethics and security.¹

To help clients succeed with their generative AI implementation, IBM Consulting recently launched its Center of Excellence (CoE) for generative AI. It stands alongside IBM Consulting’s existing global AI and automation practice, which includes 21,000 skilled data and AI consultants who have completed over 40,000 enterprise engagements and specialize in helping organizations across every industry adopt and scale AI to detect and mitigate risks, and provide education and guidance.

No matter where you are in your journey of customer service transformation, IBM Consulting is uniquely positioned to help you harness generative AI’s potential in an open and targeted way built for business.

1. CEO decision-making in the age of AI, IBM Institute for Business Value, July 2023.