Customer service is a priority for companies in every industry—and for good reason. Research from Forrester shows that organizations that communicate clearly and transparently and solve customer problems quickly are more likely to retain customers and drive them to higher spending in the future. However, providing high-quality customer service is often seen as a cost center that requires more human resources, investment in technology, and expertise than is available.

To tackle this challenge, many companies are turning to generative AI to automate and improve customer service operations across the front, middle and back office to cut costs while simultaneously driving better experiences.

Generative AI is already improving customer service processes, helping companies boost productivity and increase customer and employee satisfaction.

Boosting Agent Productivity

Generative AI is already improving customer service processes, helping companies boost productivity and increase customer and employee satisfaction. One of the most immediate use cases for many customer service organizations is helping employees work more efficiently by summarizing information and serving as a copilot to agents as they work.

For example, after cases are complete, generative AI tools minimize manual work through automatic case summarization, ultimately improving the experience for human agents.

Historically, customer service agents had to spend many minutes reviewing all the previous case notes and conversation transcripts to understand what had occurred previously. And at the conclusion of the case, they needed to manually draft and submit case resolution notes. While these processes are important, they are often tedious and time-consuming. Generative AI can automatically analyze and distill case information—including customer details, previous touchpoints, actions taken by customers and agents, and the eventual resolution—into case summaries in seconds. This removes the manual and repetitive steps from the reporting process, boosting agent productivity and creating better support experiences.

Generative AI tools can also go beyond summarization to recommend responses for agents when chatting or emailing with a customer, using data from previous cases or specific customer history to provide options for the most appropriate resolution, even incorporating the most appropriate tone. This allows agents to quickly respond to customers and avoid wasted time searching for information, which improves customer experiences as well.

Improving Customer Experiences

Generative AI integrated into virtual agents is also improving self-service. Customer service chatbots are ubiquitous, but their limited ability to solve complex problems, understand tone, interface with customers naturally, or access detailed customer information limit their efficacy. As a result, many customers today rush through their chatbot interactions to get to a live agent, defeating the purpose of the self-service tool.

Organizations are starting to embrace generative AI to drive more organic and engaging virtual agent conversations using natural language understanding (NLU) and natural language generation (NLG) capabilities. Generative AI-powered virtual agents can handle more complex customer queries, understanding nuance, intent, sentiment, and context, and deliver relevant responses. When fully connected to an organization through a digital platform, they can action customer requests with limited oversight, freeing human agents to focus on more strategic or complex tasks.

As organizations train generative AI models on their unique products and solutions and train their employees on how to effectively use the tools, they will become even more impactful. Generative AI-powered virtual agents will get smarter, allowing organizations to provide a conversational experience that no one could provide before.

Other Types of AI Still Have an Important Role

While generative AI has captured the collective imagination of everyone, including those not in technology or customer service, and has many transformative applications, there are many other types of AI that can be brought to bear to support customer service processes.

Before the proliferation of generative AI, predictive AI was already providing relief for many agents from the soul-crushing work of manually processing emails. In some customer service teams with email inboxes, there were often specific team members or specific hour shifts required for workers  to spend time manually reading and categorizing all incoming emails.  This process has been streamlined through the power of predictive AI, which can take on the initial task of categorizing requests for a human to review and approve.

In other organizations that typically deal with copies of documents during support cases, such as copies of receipts or invoices, customer service team members were similarly tasked with reading each document attached and re-typing the details into a customer case. AI-powered optical document recognition now automatically reads text from the document attachments or images and turns those into structured data, and automatically routes the case to the best team member. These improvements in the way teams work are in service of resolving customer issues faster while freeing customer service agents to do what they originally set out to do, which is helping people.

Realizing the Potential of Generative AI

In 2024, organizations will speed up their integration of generative AI with the goal of achieving higher levels of performance, efficiency and customer satisfaction. Gartner predicts that by 2025, 80% of customer service and support organizations will be applying generative AI technology.

This fast adoption speaks to the transformative power of the technology: Agents will work more productively, cases will be deflected, customers will be happy and operational costs will go down. And as generative AI models become more advanced and accurate, they will handle more complex and diverse customer service scenarios and tasks to truly transform the way customer service is done across industries. The ability to integrate generative AI with other systems and platforms will provide a seamless and holistic experience that agents need and customers expect.

But transformation requires responsibility. Organizations must prioritize a foundation built on data security, privacy and governance to recognize real success with generative AI.