Studies Reveal Reluctance Towards AI in Customer Service

In a 2024 Gartner study, two-thirds of respondents indicated that customers are hesitant about AI being used in customer service operations. This presents a quagmire for businesses, as they increasingly look to AI to trim costs, obtain insights about business processes and stay up-to date with innovative technologies, all while maintaining customer satisfaction.

Some of the key concerns highlighted in the report include the loss of quality of the interactions with customer service representatives (CSRs). Customers fear that generative AI might create an additional layer between them and human agents, causing confusion and errors. A related apprehension involves the risk of AI providing inaccurate information, especially in B2B sectors where financial stakes are high. And then there’s the unease that inaccurate AI responses can have significant downstream effects, for example by negatively impacting a company's brand image.

How can a business address these concerns while maintaining a competitive edge in a world that increasingly turns to technological innovations like AI?\

One simple answer involves utilizing specialized AI rather than an extensive and costly application that tackles every aspect of the customer service operations. Specialized AI means that the AI capabilities are applied only to specific tasks, such as order processing. This not only decreases tech investment costs, but can better meet customer needs and reduce errors. For example, a "frugal" AI optimized for order capture can interpret specific data like order numbers and delivery addresses efficiently, all with a high degree of accuracy. 

Specialized AI also uses fewer resources compared to general-purpose AI, which makes it more economical and a bit more environmentally friendly. Additionally, one of the newer developments includes Retrieval-Augmented Generation (RAG), which allows AI tools to consult knowledge bases in real-time, improving the relevance of generated content. In fact, instead of creating an additional layer between the customer and the CSR, these capabilities offer the opportunity for an improved customer experience by quickly providing correct information.

Other obstacles to fully embracing AI tools can be traced back to company-internal apprehensions. According to a 2024 OpinionWay survey, 56% of managers see AI as an opportunity to transform the Customer Service department, but change management remains difficult. Equally mystifying is actually being able to measure the ROI for AI technologies, as 42% of respondents indicated.

While these are legitimate concerns, there are fixes for all of these issues. Practicing inclusive change management will already remove a large amount of barriers of implementation, both internally and externally. Involving customers, employees, and partners in decision-making processes fosters a collaborative environment, which can also enhance operational efficiency and increase trust in the brand. 

A gradual implementation that focuses on specific use cases and progressively extending AI applications can both optimize ROI and ensure a high-quality customer experience at the same time. As with many things, sometimes taking the slower approach will result in a more satisfactory outcome. 

The idea is to eschew automation simply for automation’s sake. A well-placed, thoroughly and conscientiously implemented AI-powered solution that places the focus on providing users with accurate and timely information will be a valuable asset to both the business and its customers. By avoiding the rapid deployment of generalist AIs, companies can better control resources and limit potential errors, leading to a smoother transition to an AI-enhanced customer service.