The use of conversational AI chatbots is transforming the way businesses engage with their customers. With the ability to comprehend and answer customer inquiries in a natural and human-like fashion, these AI-driven chatbots enhance the customer experience, making it more tailored and efficient.
Conversational AI technology began with a click-based user interface, progressed to keyword-based searches, and evolved into AI/NLU-based intent categorisation and entry extractions. Today, it has reached the stage of deep learning/NLG-based LLM/generative AI, which is the driving force behind the current buzz around conversational AI.
One of the main advantages of conversational AI chatbots is that they can handle a large volume of customer queries at a time, 24/7, without the need for human intervention. Additionally, conversational AI chatbots can be programmed to handle a wide range of tasks, including answering frequently asked questions, troubleshooting technical issues and even completing cross-channel transactions.
Common Use Cases For Conversational AI
Given conversational AI’s many use cases, below are just a few of the most common examples.
One of the most common use cases for conversational AI chatbots is in the customer service industry. Many companies are now using chatbots to handle customer queries, allowing their human customer service representatives to focus on more complex issues. This not only improves the customer experience but also increases the efficiency of the customer service department.
Another popular use case for conversational AI chatbots is in the e-commerce industry. Many online retailers are now using chatbots to assist customers with their shopping experience, from answering product questions to recommending products and even completing transactions—including payment. This can help improve the customer experience and increases sales and conversion rates.
Human-centric conversational AI virtual assistants with conversational commerce (like chat commerce, voice commerce and video commerce) are now ready to provide a natural buying experience for consumers, which will help increase the adoption of digital commerce, as current global e-commerce is estimated to be only 22% of sales.
The Nuances Of Conversational AI
While conversational AI chatbots have many benefits, it’s important to note that they are not a replacement for human customer service representatives. They are best used as an additional tool to improve the customer experience and increase efficiency.
Additionally, it’s important to ensure that the chatbot is properly trained and can handle a wide range of customer queries and tasks. A recent report predicts that AI-powered chatbots will handle up to 70% of customer conversations by the end of 2023.
Other Conversational AI Factors To Consider
It’s possible that generative AI like ChatGPT, Bard and other AI language models can act as a catalyst for the adoption of conversational AI chatbots. The ability of AI language models to generate human-like responses in a conversational manner has made it possible to develop chatbots that can effectively mimic human interactions.
However, it’s important to note that while generative AI language models can be a valuable component of chatbot systems, they are not a complete solution on their own. A chatbot system also requires other components, such as a user interface, a dialogue management system, integration with other systems and data sources, and voice and video capabilities in order to be fully functional.
The specific use case and requirements of a chatbot will determine which type of AI language model is best suited for the task. For example, some chatbots may require advanced knowledge and understanding of specific domains while others may need to handle more complex conversational flows. In these cases, a specialised AI language model or a hybrid approach that combines multiple models may be more appropriate.
Other factors to consider are the quantity and the quality of the training data that AI language models are trained on. This will directly impact the accuracy and effectiveness of the generated responses. This is why it’s important for chatbot developers and organisations to carefully evaluate the training data and choose an AI language model that is trained on high-quality, relevant data for their specific use case.
Multi-lingual, multi-channel and multi-format capabilities are also required to increase the adoption of chatbots. Hence, AI language models can play a valuable role in the adoption and development of chatbots, but they should be used as part of a broader solution that takes into account the specific requirements and constraints of each use case.
On the other hand, enterprise conversational AI platforms (like Google DialogFlow, Meta WIT.AI, Kore.ai, CoRover.ai, Amazon Lex, etc.) have most of the capabilities to create a holistic chatbots/virtual assistants, but they usually lack the generative AI capabilities which can be leveraged by the large language models like ChatGPT, Bard/LaMDA, Glam, BERT, BLOOM and others.
Conversational AI chatbots are transforming customer communication for businesses. These AI-powered virtual assistants respond to customer queries naturally, improving customer experience and efficiency.
While proper training is necessary for chatbots to handle a wide range of customer queries, the specific use case will determine the best AI language model, and the quality and quantity of training data will impact the accuracy of responses. By carefully considering these important factors of conversational AI, this new technology can best be implemented to ensure it benefits your desired use case.