Generative AI's impact on customer service

Our latest research into the middle market, found that 53% of middle market business leaders believe generative AI can be used to improve customer engagement and 60% believe it will increase productivity. So, what does this mean for consumer-facing industries such as retail, leisure, hospitality and hotels, travel and tourism where human interaction has traditionally been placed at the very heart of the sector?

Consumer interaction and Chatbot 2.0

Our latest consumer sentiment survey, carried out earlier this year in May, found that 68% of consumers prefer to communicate with customer service representatives in written form, most popularly email. That preference for written interaction is the same across all age groups and trumps in-person interactions. This presents a massive opportunity for consumer businesses to incorporate generative AI into their customer interactions.

Many consumer facing businesses already deploy online chatbots as a first line of defence to solve basic customer queries. But Large Language Models (LLMs) – a form of generative AI – could take this a big step further.

LLMs are trained on wide ranging datasets, including a business’s website, FAQs, historic written dialogue with customers, and internal communications between customer service representatives and their managers. The LLMs can harness this data and converse with consumers in a convincingly human-like way to solve queries based on that existing information.

By implementing generative AI, businesses can create the ultimate digital customer service representative. This could be deployed across multiple customer communication channels, from website chatbots to email, WhatsApp and social media direct messages (DMs).

However, super-intelligent online chatbots shouldn’t be misconstrued as a replacement for in-person customer service representatives. And in fact, our survey respondents concur with this. When asked to what extent the use of generative AI within their business would have an impact on employee headcount, 16% said they expected a significant increase in headcount and 45% said they expect a slight increase, emphasising that this technology will not replace people, but rather enable them and their outputs.

These generative AI chatbots should be seen as just the first step in the customer interaction process, freeing up time for existing customer-service colleagues to add more value to the high priority tasks and deliver a high quality experience for customers to engender brand loyalty.

It is also worth noting that larger corporates have the resource available to implement these tools now. They will be investing heavily in enhancing their chatbots, and liberating time for their customer team members to focus on the value-add interactions. The smaller players in the market will need to bear this in mind, as while they might not have the immediate resource available to invest in these tools, it is important to work towards incorporating them to some degree, or risk being left behind and losing any competitive advantage.

Supporting existing customer service teams

With businesses reporting margin busting input cost hikes across 2022 and 2023, productivity gains are the hot topic on every board agenda. Customer service teams are not immune to this pressure, but generative AI could offer a solution.

Of our 411 survey respondents 315 felt that generative AI can be used to improve their business, either to a great extent or to some extent. Of that 315, 60% felt that it could help with increased productivity (as stated above), and again 60% felt it could increase operational efficiency, illustrating quite how engaged the middle market is with generative AI and how much of an opportunity it offers.

A recent study by Stanford and MIT found that generative AI boosted customer support worker productivity by 14%. The study examined the staggered deployment of a chat assistant for a Fortune 500 software firm. The tool, trained on data from over 5,000 agents at the company, monitored customer chats and offered employees real-time suggestions for how to respond to customers. Interestingly, the productivity gains were most evident with less experienced workers. This was due to the AI model’s ability to disseminate knowledge from more experienced workers thus helping the newer workers move up the knowledge curve more quickly.

In a market where consumer industries face workforce shortages and high vacancy rates, this study highlights the potential of generative AI as a valuable tool for upskilling entire teams. This enhancement can greatly improve their efficiency, particularly during periods of limited resources.

The risks

There are ethical factors to consider when incorporating generative AI into your customer service function. For example, making consumers aware that they are talking to AI as opposed to a human.

Data privacy also has to be a key consideration with any use of generative AI tools in these interactions. And the middle market does seem to be aware of the risks around data security. 47% of our panel of middle market business leaders told us they had major concerns surrounding data security and privacy from the use of generative AI in their business. Putting strong guardrails in place to prevent confidential or customer data being shared by ‘well meaning’ Chatbots will be of paramount importance.

Given this technology is relatively new, it's advisable to approach it cautiously, whilst still allowing room for innovation. The potential of generative AI to serve as a first defence for customer service operations and drive substantial efficiencies in customer service teams is evident. Integrating generative AI can not only enhance the efficiency of operations but also significantly contribute to enhancing the overall customer experience. But, these prospects should not be overlooked due to apprehensions around the perceived risks.