The introduction of Generative AI promises a significant shift in the domain of customer service. As someone spearheading application development, you now possess the remarkable capacity to exploit this state-of-the-art technology for direct and notable value. It’s high time to form alliances with pioneering customer service technology (CST) vendors to incorporate Generative AI solutions that will advance your service functions exponentially.

Understanding the Influence of Generative AI on Customer Service

The future has arrived, and it is fueled by Generative AI. Trailblazing CST vendors are employing large language models (LLMs), a powerful subset of Generative AI, to remarkably improve their services.

Gartner recently released a report on “How Can Generative AI Be Used to Improve Customer Service and Support?” 

According to Gartner, “Customer service and support technology (CST) vendors are adding new, Generative-AI-powered features to their solutions. This first wave of features using large language models (LLMs), a subset of Generative AI, will improve:

  • Employee productivity by reducing average handle times (AHT)
  • The quality and accuracy of interactions by creating reusable knowledge content
  • Self-service containment rates through better conversational virtual agents.

Here’s how Generative AI can metamorphose your customer service operations:

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1. Enhance Employee Efficiency

Deploying Generative AI can drastically curtail average handle times (AHT), leading to an outstanding surge in employee efficiency. Visualize a scenario where your team can address more inquiries in less time, without compromising the level of their support.

2. Refine Interaction Standards

Generative AI facilitates the creation of reusable knowledge content, escalating the standard and precision of your customer interactions. Say goodbye to the monotonous chore of maintaining knowledge bases and welcome a newfound level of consistency and accuracy in your responses.

3. Simplify Self-Service

Conversational virtual agents, powered by Generative AI, can substantially optimize self-service containment rates. Offering customers immediate, proficient self-service options not only alleviates the burden on your support team but also enriches the overall customer experience.

Strategies to Capitalize on LLM

Application leaders should consider three primary strategies to interact with LLM:

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  • Pre-designed model usage: This involves using services like ChatGPT as-is, without directly interacting with the underlying GPT-3.5/4 models. While it’s a cost-effective, fast-to-launch approach, the restricted control over outputs can lead to hallucinations, rendering it unsuitable for enterprise use in the immediate future.
  • Prompt engineering: This strategy involves the utilization of custom-built tools to formulate, refine, and evaluate input and output prompts that interact with the LLM. It offers more focused outputs and results, but requires assimilation with business systems and knowledge bases to introduce organizational data. Furthermore, there are costs associated with acquiring an apt prompt engineering tool and onboarding prompt engineers.
  • Utilizing custom models: This entails building or licensing GPT or similar LLMs directly, allowing for tailor-made models, data, parameters, and tuning to align with your enterprise’s unique needs. However, this strategy requires considerable investment and expert skills, making it unsuitable for most enterprise customer service environments.

Before implementing any of these strategies, it’s vital to set up experiments to identify which use cases to automate using LLM capabilities. Upon completion, evaluate the results before implementing solutions for production use in customer service and support operations.

For a quicker value realization, a fourth strategy involves leveraging partnerships with existing CST vendors to evaluate and adopt their Generative-AI-powered product enhancements for immediate value.

The Immediate Business Value of Generative AI

According to Gartner, “Through new Generative-AI-powered features, CST vendors are delivering immediate business value in several areas. CST vendors are adding new generative-AI-powered features to their existing solutions. Through these solutions, generative AI can deliver near-term business value in the following areas:

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1. Summarization

Vendors are incorporating automated after-call work (ACW) functionality, enabling customer service reps (CSRs) to close out customer interactions and cases with only a few clicks. This reduces AHT and increases overall rep productivity. Summarization capability is also showing up in self-service to assisted-service transitions, where the application presents the CSR with a “story so-far” summary of the interaction.

2. Knowledge Asset Creation

This uses a combination of the content creation, summarization and classification capabilities of LLM-powered applications. Vendor solutions use unstructured textual data in call transcripts and case notes to identify new content. These content snippets then get presented to downstream knowledge management (KM) processes and systems for further curation before being added to the enterprise knowledge corpus.

3. Virtual Assistants

LLMs improve intent recognition and classification, which are important steps in answering end users’ questions presented to VAs in natural language. Additional capabilities such as content augmentation, tone of content, summarization and content classification are combined to implement VAs capable of engaging in human-like conversational interactions.

While evaluating CST vendor innovations, application leaders should also collaborate with cross-functional leaders in data and analytics and corporate governance to develop governance frameworks and an enterprise use policy for LLM-based applications.

Are you prepared to revolutionize your customer service domain? 

Download the report to learn how you can transform your customer service experience.

Gartner, How Can Generative AI Be Used to Improve Customer Service and Support? Published 24 May 2023 By Analyst(s): Pri Rathnayake 

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.


Manisha Mishra

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