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Friday, April 12, 2024

Three Ways Companies Can Leverage Generative AI To Enhance Customer Service

Rohan Joshi is the CEO and co-founder of Wolken Software, a leading IT service management and customer service desk software provider.

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Customer experience is a top priority for leaders at many companies in 2024 ↗. As companies push for more investments in AI and other technology to address customer service, the overall sentiment from consumers has been met with skepticism. In fact, 60% of people still prefer to speak to a live customer service representative ↗. Yet, 50% of customer calls still go unresolved or require escalation ↗. This results in long open rates for tickets, negative customer experiences and increased costs for companies. Companies must reconsider the deployment of GenAI so that it supports and enhances the customer experience.

It has already become evident that looking for GenAI to completely replace the agent is a mistake. Instead, companies must create a structure that addresses the habits of all. This means incorporating GenAI solutions in three buckets:

DIY users. The customers that simply want to read through knowledge-based pages on their own, and find their own solutions.

Chatbot users. Those who are comfortable solving their problems through digital platforms.

Live customer agents. These represent the largest group of Americans who still want to interact with a live agent.

Regardless of whether a customer chooses any of these three approaches, there is a single goal. To quickly provide a seamless solution that addresses the customer’s query. The good news is that there are ways that GenAI solutions can enhance customer service at all three levels.

Neural Search With Generative AI

It is too often overlooked, but the first step in enhancing customer services should be empowering an enterprise’s search function by harnessing the capabilities of generative AI and neural networks to decipher intricate queries and perform organizationwide searches encompassing all data repositories and business applications, including databases, discussions, tickets, knowledge bases and response logs.

Why is this important? This ensures that GenAI tools will have the ability to identify and pull relevant data from within larger datasets, knowledge base pages, and other resources that provide the information that is needed for specific customer queries. It also sets the foundation for creating personalized, precise, uniform and access-controlled search outcomes.

Most importantly, it is necessary to deliver an optimized personalized experience for every customer inquiry.

Generating Unique Responses

Rarely do two customer queries match exactly. More often than not, each customer has a particular situation or experience that needs to be addressed. As a result, a formulaic knowledge base page truly addresses a customer’s entire issue, which means that a user must read multiple pages before finding a solution for their particular problem. The same is true for case agents who are looking to address a customer’s query.

By leveraging an enterprise’s neural search, generative AI can be leveraged to pull the relevant data points, match the relevant information to the customer’s profile preferences and prior interactions, and generate tailored real-time responses and summaries that address the specific query.

The customized response can be delivered instantaneously to the user online, or to the customer service agent so they can be informed while supporting the customer. The necessary information will reach the customer faster regardless of the approach, which enhances the efficiency and experience for the customer.

Co-Pilot Chat

While the information pulled from the neural search can be useful for addressing specific issues, companies should use the same approach as chatbots. This can be built using enterprise-level language models and tailored to the needs of individual companies in any industry.

Companies should consider developing two versions of these co-pilots. The initial one should be built to interact directly with customers so they can troubleshoot issues directly and collaborate with the co-pilot in real time. Once the initial version is created, additional data such as case summarization, answer generation and precise guidance should be layered so that agents can leverage this technology to address customer queries.

Conclusion

There is no question that AI will continue to improve customer service in the future, especially as people become more comfortable interacting with technology to solve their problems. But along the way, it will be essential for companies to implement GenAI solutions in the best possible way to meet the habits of their customer base.


Forbes Technology Council ↗ is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?


Michael Maren
Michael Maren
Former marine biologist who likes to spend as much time in the tropics as possible, due to a horrible time I once had in Alaska. Brrrr.

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