A new way to connect and relate to customers using AI chatbots - The Khuram Dhanani Foundation
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A new way to connect and relate to customers using AI chatbots

A new way to connect and relate to customers using AI chatbots

Customer service is essential in many e-commerce settings. Live chat interfaces are becoming a more and more popular method of providing real-time customer care. Conversational bots regularly take the place of human chat support agents in the modern era. Software agents also referred to as chatbots, are automated systems that use natural language and artificial intelligence (AI) to connect with human users (Adam et al., 2020).

The communication volume between businesses and their consumers increases as they expand. This indicates that more live agents have traditionally been added to manage incoming questions and issues as a solution. However, more personnel also means more company expenses. Chatbots, a sophisticated technology-based solution, are offered to us. Customers who are used to outdated chatbots with limited features, awkward grammar, and a negative tone may complain about this alternative. Even so, chatbots may be bothersome, but they should not be.

The benefits of adopting chatbots are interacting with several users at once and providing the necessary information quickly, which offers the chance for timeliness and practical problem-solving in contacting and assisting clients in the initial stage of customer engagement. 

Businesses must study for their client interactions as online communication grows. Intelligent digital assistants can help companies expand and improve by removing obstacles to communication between customers and companies and giving their consumers control over how they communicate freely with organizations. Research from Adam et al. (2020) stated that many consumers continue to have unpleasant interactions with chatbots (for instance, high failure rates), which could lead to doubt, and a possible barrier to the technology is resistance to it that users follow instructions and suggestions created by a chatbot. After all, text-based communication is a productive strategy to improve relationships between companies and their clients.

Client engagements with chatbots should be liberating, enabling people to communicate with businesses online in a way that feels easy and helpful. Customers should find it simple to resolve their questions and issues in a way that makes them feel pleased and perceived. In a recent Amos study, some AI chatbots focus on customer service, which might shift consumers’ perceptions of this technology. It’s crucial to consider both assisting others and conducting business well. After all, customer courtesy is essential. Chatbots enable companies to provide better customer service and an enhanced customer experience, but they do not take the human agent position. Instead, with rich and engaging information, they are here to assist us in a way that feels natural. 


Since even the best AI chatbots will have limitations, they ought to be able to produce and forward help requests when they don’t know the answers, much like a live agent could escalate a query they can’t answer. In addition, after a user’s question or issue has been resolved, an AI chatbot should wrap up the conversation naturally.

AI chatbots present an opportunity to train real customer support representatives as well. For instance, chat transcripts allow live staff to follow up by informing them of encounters that go well and those that may be improved. As a result of these interactions, agents are better able to foresee issues and waste less time looking for solutions.

Using an AI chatbot is an intelligent business decision. Thanks to contemporary technologies, these digital assistants may assist organizations in connecting with their clientele and supporting end users like never before. Deployment may seem scary to small businesses without in-house IT departments, but there are fewer barriers to entry than ever. As the use of AI chatbots extends across industries, the platforms that apply machine learning and have the most crucial input data from which to learn will be in the lead.