Presented by TDCX
Customer service impacts every facet of the business. While automation and other tech have continued to evolve to support customer interactions, generative AI and large language models (LLM) are an enormous leap forward, says Ben Sun, SVP of TDCX AI, the artificial intelligence (AI) consulting arm at TDCX.
“Gen AI will impact human interactions and the way we serve customers to a much greater degree than any technology before it,” Sun says. “It’s set to disrupt how people work, as well as the way companies continue to develop and thrive and serve their customers. It will impact proficiency and productivity, and add new ways to enrich customer interaction. It’s essential for companies to embrace gen AI and be at the forefront to help drive that change.”
How gen AI is impacting CX now
A full 80% of consumers say speed, convenience, knowledgeable help and friendly service are crucial elements of a positive customer experience. That’s why companies should be prioritizing gen AI in their CX strategies, Sun says, in part because of the tremendous productivity and efficiency gains that can be achieved. That’s essential as customer service becomes more and more complex as the world becomes increasingly digital.
New problems emerge every day, and classic problems never go away, but just get knottier. Customer service agents are tasked with managing a growing mass of data and knowledge in order to help the consumers they speak with. Knowledge bases are common tools, but typically agents need to dig through disparate systems and many screens to provide answers – and know what keywords it will take to surface them. And the longer that process takes, the less satisfied your customer will be.
Gen AI, however, eliminates the lengthy search. It can parse a natural language query, synthesize the necessary information and serve up the answers the agent is looking for in a neatly summarized response, slashing call times dramatically.
“We’ve done a number of studies about how the longer you spend handling the case, the worse the customer experience gets,” Sun says. “With gen AI, I can ask, ‘What’s the best way to solve this, and what are my options?’ And I’ll be given very specific, concrete answers to address whatever the customer concerns are.”
Not only will gen AI improve the way agents work, it will enable them to do a lot more multitasking as well, tremendously enhancing productivity – freeing up their time so they can turn to the more complex, interesting scenarios and cases, now with a tool that enhances their ability to solve those problems.
Sun also points to the way gen AI is also disrupting the education space and transforming how people learn. In the larger world, students are abandoning their online education platforms in favor of asking ChatGPT to explain. In return, these companies have seen a significant drop in subscriptions and revenue. But there’s opportunity there.
“The technology will greatly impact learning, training and development,” he says. “What we call speed to proficiency, and the learning curve, can be reduced dramatically by generative AI, helping agents get up to speed and get a good handle on the product knowledge for a particular service.”
Gen AI can be used to monitor interactions and provide pointblank, very specific, highly detailed feedback to help customer service agents improve their skill sets, as well, Sun adds, driving a significant improvement in the way customer service agents support consumers, and directly impacting customer satisfaction.
The essential human-AI connection
As AI becomes more integrated into customer service experiences, the elephant in the room is where the human agent fits in. But agents are embracing the technology, Sun says.
“First, everyone realizes this is going to make their job easier,” he explains. “Secondly, as we progress with the technology, it’s going to make a lot of routine tasks much simpler, and many things will be automated, and agents will be doing more interesting work.”
But transitioning to these AI-powered tools requires careful change management, he warns, and that’s how many company initiatives fail.
“This is perhaps one of the hardest things to do, and most of the time it’s not the technology that fails people, it’s the change management that fails people,” he explains. “It’s about showing your employees the possibilities and the opportunities, as well as taking small steps, working with them as they experiment with the technology, and only embracing and adopting new solutions and new processes once they’re comfortable.”
The challenges and risks of gen AI
Public-facing LLMs are trained using a huge array of information and data – they’re essentially open source in terms of training and development. And sometimes, the AI loses the plot, synthesizing information in a way that has them confidently producing false information or conclusions. That can’t be allowed to happen in a corporate setting. The challenge is developing an internal LLM, fed from a defined set of corporate knowledge and sources of data, and monitoring it to ensure it’s not drifting off message.
“We need to ensure that the technology, the information that’s being provided, is accurate, so that people can trust it, and trust an agent’s responses,” Sun says. “And it’s not only ensuring accuracy of information when we serve our customers, but preserving consumer privacy and protecting confidential internal information.”
But perhaps the first challenge and initial hurdle is actually figuring out where, and how, to innovate. Saying you ought to integrate gen AI into everyday workflows and embed it into customer service strategy is one thing; knowing what pain points can be solved is another. Many companies are taking an incremental approach, since we’re still right at the beginning of the gen AI revolution, he says.
“Everyone is waiting for the truly impactful use cases to emerge and prove effective in driving positive change,” Sun says. “But that will be an ongoing process. We’re developing internally, and we’re aggressively working with the market, with our clients and customers, trying to explore innovations and new initiatives so we can push gen AI to the next level.”
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