What is Conversational AI? Examples and Benefits

Traditional chatbots are typically based on rule-based algorithms and they follow a set of pre-programmed rules to determine how to respond to user queries. For example, a chatbot to provide customer support might be programmed to respond with a specific set of replies depending on the user’s query. Traditional chatbots are restricted to specific conversational flows but can be effective for simple tasks, such as answering basic queries, FAQs or sharing product information, and more. Major ecommerce platforms are a great example of arenas enjoying better support.

A caller could call in with a simple question, like wanting to check their balance; the voice menu alone could help with that. But financial services is more than just banking—what if the caller has questions about specific investments, retirement planning, or insurance? The AI could understand their question, identify the agent with the best skills to help with that topic, and forward the call to that agent. That way every agent gets to provide financial advice for the topic they know the most about, and customers get the best help possible. Conversational AI does not rely on manually written scripts to answer customer queries.

Conversational AI chatbot use case: ChatGPT

With smart AI, you can automatically gather customer feedback, opinions, and insights by engaging customers in interactive conversations. This helps service teams understand customer experiences, identify areas for improvement, and make data-driven decisions to enhance products, services, and overall customer satisfaction. Conversational AI models are trained on data sets with human dialogue to help understand language patterns.

Plus, 62% of consumers prefer talking to a chatbot over a human agent (sorry, humans). Smart AI uses techniques like voice biometrics and recognition to swiftly and naturally verify customer identity, enhancing security and convenience. Over 32 million customers have engaged with Erica since its release, making it one of the most popular AI tools in the US.

Tips for choosing the right Conversational AI provider

Zobot is compatible with various AI technologies, including IBM Watson, Dialogflow, Microsoft Azure, Haptik, and Zia Skills, enabling seamless integration. By using Zobot, service teams can answer customer queries, automate responses, and provide instant assistance. ChatSpot empowers service teams to manage conversations effectively, streamline communication, and provide personalized support. Bank of America uses advanced AI to improve accessibility for all customers, including those with disabilities.

• For customer support, chatbots are one of the main applications of conversational AI.
• If you want to know more, we highly recommend our AI chatbot Buyer’s Checklist.
• First things first, conversational apps are not one of the technologies you can build and leave for them to “do their thing.” You need to continuously work on them and improve them to get the best results.
• Clocks and Colours’ bot is integrated with the brand’s traditional customer service channels.
• It can also learn from past interactions and enhance its responses over time.
• Virtual assistants such as Siri, Alexa, or Cortana include a vital component that helps people – machine learning.

NLP algorithms analyze sentences, pick out important details, and even detect emotions in our words. With NLP in conversational AI, virtual assistant, and chatbots can have more natural conversations with us, making interactions smoother and more enjoyable. Yellow.ai has it’s own proprietary NLP called DynamicNLP™ – built on zero shot learning and pre-trained on billions of conversations across channels and industries.

If you don’t have a FAQ list available for your product, then start with your customer success team to determine the appropriate list of questions that your conversational AI can assist with. Machine Learning (ML) is a sub-field of artificial example of conversational ai intelligence, made up of a set of algorithms, features, and data sets that continuously improve themselves with experience. As the input grows, the AI platform machine gets better at recognizing patterns and uses it to make predictions.

Conversational AI uses machine learning, natural language processing, and natural language generation to understand and engage in conversations–as well as extract important information from conversations. Machine Learning (ML) is a sub-field of artificial intelligence, made up of algorithms, features, and data sets that continuously improve to meet customer expectations. Natural Language Processing (NLP) is the current method of analysing language in tandem with machine learning and deep learning.