The widespread interest in generative artificial intelligence (AI) has created a renewed focus on the power of AI to solve for business challenges, especially for customer service. Generative AI is a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music.
According to McKinsey, customer experience (CX) is one of the top use cases for generative AI. They estimate a 30 to 45 percent productivity cost improvement, from applying generative AI to customer care functions.
At Amazon Web Services (AWS), we believe improving customer experiences is one of the top use cases for generative AI because of the opportunity to further improve assistance for agents, insights for managers, and self-service experiences for customers.
Generative AI is helping reinvent customer experiences
LLMs bring an exciting potential to CX use cases because they can help improve how contact centers manage and process large amounts of data, and provide real-time CX enhancements. For example, a LLM’s ability to produce human-like text generation is an area of immediate opportunity for CX. This can be used for generating text like conversation summaries and providing real-time agent assistance.
LLMs will also improve the natural language processing of voice- and chat-bot conversations, which are fundamental to success of automation in contact centers. This is because LLMs are able to leverage vast amounts of metadata for context (such as call history, previous conversation transcripts, and prior transactions) in an active conversation. Additionally, complex, non-linear sentence structures can be more easily understood by LLMs to accurately determine contact intent.
Outline real CX business outcomes you’re looking to drive with generative AI
As you consider how generative AI can be valuable in your contact center, it’s important to start with the business outcomes you’re trying to achieve. This will help narrow in on specific use cases for generative AI in your contact center. The following three use cases for generative AI provide immediate business value by increasing agent efficiency, more accurately processing data, and helping customers get answers to more complex queries.
Generative AI has the potential to help further reduce handle times and increase first call resolution by improving agent efficiency and accuracy when responding to customer issues. Generative AI can enhance agent assist capabilities to generate real-time suggested responses and actions, summarized from company knowledge content to help agents solve customer problems quickly and accurately. For example, when a customer calls to inquire about their auto insurance claim, an LLM can leverage information about the customer's claim and policy, repair shop details from the insurer's website, and policy documents from internal repositories to provide the agent with a comprehensive response and next actions to help resolve the customer's issue.
Generative AI can also enhance real-time and post-contact analytics and quality assurance efforts by analyzing all contacts, instead of just a smaller sample, making it faster for managers to identify insights and ensure agents are adhering to policies. Generative AI can be used to concisely summarize conversations to reduce the time agents and managers spend taking/reviewing notes or sharing context when transferring contacts. For example, generative AI can condense a long conversation about a cable subscription inquiry to: “the customer cancelled their cable subscription after rejecting a $10 rebate offered by agent”. LLMs will also provide further insights to managers regarding agent performance driving business outcomes. It can then provide recommended actions, like coaching points and agent training, to further improve performance.
Furthermore, you may be looking to optimize self-service experiences to improve call deflection rates and reduce the cost of development of automated self-service experiences. Generative AI can help here too, by making it easier for companies to understand the complex nuances of a customer's intent. It can also deliver LLM-powered recommendations for improving contact center configurations that make it easier to design, build, and update self-service experiences.
Explore what’s possible with generative AI and Amazon Connect
When we launched Amazon Connect in 2017, we built it from the ground up with integrated AI capabilities that help customers like National Australia Bank, Traeger, Accor, andJust Energy realize outcomes like reduced handle times and improved customer satisfaction. We see an opportunity to enhance Amazon Connect’s existing built-in AI capabilities using generative AI, to deliver additional business value.
Check out this demo video for a look at how generative AI can be used for three contact center use cases – agent assistance, manager assistance, and customer self-service – with Amazon Connect:
Demo: Generative AI for Customer Service with Amazon Connect