Building a Human-Centric, AI-Driven Customer Service Experience

Imagine you’re a customer who has just had an unfortunate accident. You need answers, quickly. You find yourself staring at a chatbot, hoping it can guide you through the process of reporting the damage and understanding your insurance options. The stress builds as you realise that your time is limited, and you expect a quick, effective resolution from the company you trust.  

This is the reality of customer service today: people expect immediate, helpful answers delivered with understanding and compassion. Companies have a responsibility to meet these expectations by being continuously available to provide responses without frustrating wait times. But it’s more than just fixing problems fast. Delivering an exceptional experience fosters loyalty, making customers far more likely to stay loyal to your brand. In fact, according to the Harvard Business Review, companies with strong brand loyalty see revenues grow 2.5 times faster than their peers. When customers feel genuinely supported, brand loyalty becomes a powerful driver of profit and long-term growth. 

Chatbots have led to a new era in customer service with many benefits. Some companies are leveraging automation and analytics to offer around-the-clock, cross-platform support. This allows customers to receive instant responses, regardless of their location or the time of day. 

Consider the cost savings: By (even partially) automating repetitive conversations, organisations can reduce the need for human effort, freeing up valuable resources for more complex customer interactions. And from a customer experience standpoint, the more conversation data is available, the better your understanding is of customer needs and points of improvement.  

While chatbots are transforming customer service, the current generation of chatbots are not without their limitations. Picture this: a customer reaches out with a question about their policy, only to be met with a chatbot that struggles to understand the nuances of their inquiry. Our market research indicates that most chatbots in the insurance industry, for instance, struggle with these limitations. This often results in frustrating customer experiences and unclear handovers to human agents.  

Many chatbots are unable to address the full scope of customer inquiries. Their reliance on pre-written answers can create a robotic interaction that lacks the human touch customers expect. Additionally, maintaining these systems can be time-consuming, as they are often disconnected from knowledge banks, agent assistance tools, and FAQs. 

Generative AI brings the potential to combat the previously discussed limitations. By using pre-trained models like large language models (LLMs), we can significantly improve the quality of question recognition and the flexibility of answer generation. This gives way to scenarios where customer inquiries are met with natural, human-like responses that can address most of their questions with ease. 

However, we can also acknowledge that with this advancement comes a set of risks when not implemented correctly. In the past, we’ve seen instances where generative algorithms produce unpredictable outcomes. There’s the potential for chatbots to deliver incorrect answers, make unethical comments, or even generate negative poems about an organisation! This can make stakeholders hesitant to get started with generative AI solutions or bring them to operation. 

All of this is not to say that it can’t be done without major risks. There are ways to make your chatbot behave in the desired way through a robust governance framework and an advanced technical setup. Further measures to reduce risks associated with generative AI include making sure that your systems have effective information retrieval capabilities and safety measures to protect against hallucinations, where the AI generates inaccurate, irrelevant, or even harmful responses. 

Moreover, bringing the right people into your organisation is important. Teams need to understand and implement these technologies responsibly. Engaging with AI boards and governance bodies to demonstrate your commitment to ethical AI deployment will instill confidence among both these stakeholders and customers. 

Read more about ADC’s approach to implementing Ethical AI. 

As we look beyond traditional chatbots, we can also think about larger opportunities to implement AI in customer service. The future lies in developing centralised knowledge management systems that analyse all conversations across various channels. These could link conversations to relevant knowledge articles, while also assessing the quality of those articles and identifying gaps in information that need to be addressed. 

Customer service has the potential to evolve from being reactive to proactive. We can create an environment where agents have access to real-time insights, allowing them to deliver better service across both voice and non-voice channels through generative AI. 

By embracing the full potential of automation and generative AI, we can create a more efficient customer service landscape. At the same time, developing solutions that address the associated risks ensures that this landscape remains genuinely human-centric. 

Is your organisation interested in building on a culture of innovation that also prioritises customer trust and satisfaction? Reach out to one of our experts, Tom van der Woude (Manager, Technology & Innovation) to learn more.