Leveraging Generative AI for Enhanced Customer Service and Support

September 14, 2023

The Rise of Generative AI

Generative AI represents a significant leap forward in automation, offering new possibilities for customer service technology vendors and application leaders. Customer service technology vendors are at the forefront of incorporating generative AI into their solutions, leveraging large language models (LLMs) to achieve remarkable results. Let's explore how generative AI is poised to make a substantial impact in several key areas:

Employee Productivity Enhancement:
One of the primary benefits of generative AI in customer service is its ability to reduce Average Handle Times (AHT). AHT is a crucial metric in customer service, as it directly affects customer satisfaction and operational efficiency. With the introduction of generative AI, customer service representatives can streamline their interactions with customers, resulting in quicker problem resolution and improved productivity
Elevating Interaction Quality and Accuracy:
Generative AI, powered by LLMs, excels at creating reusable knowledge content. This means that customer service agents can access a wealth of information to provide more accurate and comprehensive responses to customer inquiries. By tapping into the vast knowledge contained within these AI models, businesses can enhance the quality of their interactions, resulting in more satisfied customers.
Enhancing Self-Service Containment Rates:
Self-service containment rates are a key indicator of an efficient customer service operation. Generative AI can improve these rates by enabling more effective conversational virtual agents. These AI-powered agents can engage in human-like conversations, guiding customers through solutions and reducing the need for human intervention. As a result, businesses can achieve higher containment rates while providing a seamless customer experience.

Choosing the Right Approach

While the benefits of generative AI in customer service are evident, it's essential for application leaders to choose the right approach for their organization. Three primary approaches are worth considering:

Out-of-the-Box Model Usage:
This approach involves using generative AI services as-is, without direct access to the underlying models. While it offers a fast and cost-effective solution, it comes with limitations in control over outputs and potential risks of incorrect or misleading responses, making it unsuitable for enterprise use in the short term.
Prompt Engineering:
Prompt engineering entails using purpose-built tools to create and fine-tune input and output prompts that interact with the LLM. This approach offers better control over generated responses and allows for customization to align with organizational requirements. However, it does require integration with existing business systems and knowledge bases, along with an investment in prompt engineering tools and personnel.
Leveraging Custom Models:
The most advanced option is to build or license LLMs directly, allowing for highly customized and optimized models tailored to specific enterprise needs. While this approach offers unparalleled control, it demands significant investment and specialized expertise, making it suitable for only a select few enterprise customer service environments.

Driving Quicker Time to Value

To expedite the benefits of generative AI in customer service, there is a fourth approach: partnering with existing customer service technology vendors. These vendors are actively integrating generative AI-powered features into their solutions, offering near-term business value in various areas:

Automated After-Call Work (ACW):
This functionality simplifies the process for customer service representatives, enabling them to efficiently close customer interactions and cases. It reduces AHT and enhances overall rep productivity, contributing to a seamless customer experience.
Prompt EngineerinKnowledge Asset Creationg:
By harnessing the content creation, summarization, and classification capabilities of LLM-powered applications, businesses can identify and curate new knowledge content from unstructured data sources. This content can then be integrated into the enterprise knowledge corpus, improving the overall knowledge base for customer service operations.
Virtual Assistants (VAs):
Generative AI enhances the capabilities of virtual assistants by improving intent recognition, content augmentation, tone analysis, summarization, and content classification. These enhancements enable VAs to engage in more natural and effective conversational interactions with customers.

Conclusion

Generative AI is poised to redefine the landscape of customer service and support. By leveraging the power of large language models and choosing the right approach for implementation, businesses can enhance employee productivity, improve interaction quality, and boost self-service containment rates. As customer service technology vendors continue to innovate and integrate generative AI features into their solutions, organizations have a unique opportunity to achieve quick and tangible benefits in their customer service operations. To stay competitive in today's fast-paced business environment, embracing generative AI is not just an option—it's a strategic imperative.