By Jared Danaraj, Vice President, Sales and Solutions Engineering, Asia Pacific and Japan, UiPath
In today’s digital age, a single social media post about a bad customer experience can quickly spread like wildfire and damage a brand’s reputation. As some of these bad experiences stem from poor communication, it is essential for businesses to strengthen their communication strategy and improve communication touchpoints.
Contact centers, which serve as the direct point of contact for customers, can sometimes be challenging to manage. Companies have started using chatbots powered by artificial intelligence (AI) and machine learning (ML) to help resolve the large volume of customer inquiries. Chatbots are very useful at handling simple queries like order status checks and password resets. However, they fall short of addressing complex sentiments and providing empathy and support—which can harm customer experience.
With today’s high customer expectations, personalised interactions and connected experiences are vital. To differentiate themselves in a crowded market, brands need to add the & human touch to their customer service delivery while maintaining efficiency and performance.
This is where the synergy of a powerful generative AI chatbot and automation comes into play. Natural Language Processing capabilities enable generative AI tools to understand and respond to human language quickly and coherently. As generative AI chatbots can provide human-like responses, brands can better deliver engaging customer experiences. With advanced AI models now accepting both text and image inputs, generative AI is becoming even more powerful.
By combining the conversational empathy and intelligence of generative AI with AI-powered automation tools, the customer experience can be improved. These automation tools can extract specific information or generate personalised responses based on user input, revolutionising the way businesses interact with customers. This combination strikes a balance between human-like experiences and operational concision.
The following are some examples of how this works.
Use Case #1: Analyse Customer Feedback
Businesses can enhance their analysis of customer feedback received through online chatbots by using some form of generative AI. By instructing software robots to send a set of feedback to generative AI and prompt it to identify the sentiment as positive, negative, or mixed, businesses can gain deeper insights. The robot can collate the sentiment data for further review and processing downstream, providing the team with useful feedback for consideration and action.
Businesses are already leveraging automation solutions to collect conversations from various channels and analyse customer sentiment using tools such as communications mining or generative AI. Some solutions go a step further by categorising sentiment across product categories and customer interactions into specific buckets like issue, inquiry, or escalation. This categorisation aids businesses in efficiently routing queries to the appropriate agent or software robot for further processing downstream.
Use Case #2: Create a Customer Response Email
Businesses can use generative AI to automate drafting responses for customers who have provided negative feedback. In this process, a software robot can prompt the generative AI model with the negative feedback and content of the email. The generative AI tool will then create a relevant response, which can be reviewed by a human before being shared with the customer for better accuracy While generative AI tools have significantly improved over the last few months, hallucination remains an issue. New automation solutions that incorporate ‘human in the loop’ capabilities can bridge this gap and prevent inaccurate information.
Use Case #3: Organise large open mailboxes
Managing a high volume of emails can be challenging for businesses, but automation can greatly streamline the process. A software robot can efficiently organise incoming emails by placing them into designated folders and extracting essential information, such as purchase order numbers, which can be seamlessly entered into a CRM system. The system generates a unique ticket number and assigns a dedicated agent to handle the request. Furthermore, utilising a generative AI model allows businesses to generate personalised email responses to customers based on the collected information. This approach significantly improves response times and enhances the overall customer experience.
What’s Next?
Advancements in generative AI present businesses with the opportunity to enhance the customer experience and achieve comprehensive digital transformation. Leading vendors have developed connectors that seamlessly leverage the capabilities of generative AI. These connectors allow businesses to focus on digital transformation while harnessing the exceptional value proposition offered by generative AI tools. Additionally, these capabilities extend beyond customer interactions and contact centers, encompassing areas like IT service management, human resources, and sales operations. A diverse ecosystem of plug-ins is emerging, with leading vendors leveraging APIs that connect with generative AI capabilities, empowering businesses to derive greater value from automation, AI, and ML for operational excellence and improved customer experiences.
