Conversational AI: What It Is and How It Works

examples of conversational ai

Black Box will be discussing the latest trends in conversational AI and some of the most interesting use cases they are seeing with their Clients around the world. PandaDoc is an example of a chatbot use case exploited to its full potential. MVMT, a fashion-brand that develops watches and sunglasses and especially targets millennials, uses this strategy to great effect with their chatbot use case.

Free up your agents to focus on complex tasks, reducing operational costs and improving employee experience. Offer support, recommendations, services and personalised, friendly advice to customers 24/7  which helps boost revenues and brand loyalty. These are just a few examples of how conversational AI is being used today. As the technology continues to develop, we can expect to see even more innovative applications of conversational AI in the future. Privacy and security are also major concerns when it comes to conversational AI.

Conversational AI Cloud

This technology allows virtual agents to go beyond basic, scripted answers and interact with customers in a human-like manner. ChatGPT is short for “Chatbot Generalized Pre-Training Transformer.” It was developed by OpenAI, an AI research laboratory based in the U.S. ChatGPT was trained on a huge amount of data using natural language processing examples of conversational ai (NLP), enabling it to learn global facts, grammar, and a certain level of reasoning ability. After learning these, ChatGPT was then trained to respond to specific queries. Chatbots are frequently used to improve the IT service management experience, which delves towards self-service and automating processes offered to internal staff.

examples of conversational ai

In the context of conversational AI, labeled data may include examples of user input and the appropriate response. Machine learning is a critical component of conversational AI, as it allows machines to learn and improve over time based on user interactions. Machine learning algorithms are used to train conversational AI models, which can then be used to interact with users. NLP algorithms use statistical and machine learning techniques to analyze and understand human language. These algorithms can identify key features of language such as syntax, semantics, and context.

#6. Customer Service Agent efficiency

Other companies who deal with many different products (or even just a few) can apply this chatbot use case to quickly answer customer requests for price quotes. It shows customers whether it has a product in stock – and then lists its price. Notice how the chatbot also shows the product images and has a ‘shop now’ button underneath so customers can quickly visit the page and buy the product whose price the chatbot quoted.

Their chat functionality serves to direct website visitors to content relevant to their enquiry. Recognising that some people have complex health challenges, there is also the option to route the visitor to a doctor for more personalised feedback. AI also allows you to come with better knowledge that can be helpful in any industry or domain. This is a powerful feature that can be re-configured as per the requirement. This can help automate a lot of conversations so that human support agents can focus on their challenging work. Say a fashion retailer is missing an automated bot that can resolve post-sale queries, for example.

While businesses should try giving a variety of choices to their customers, they should do so cautiously. That’s because if companies go overboard giving customers too many choices, customers may not go through with their purchases. That’s because research has shown that too many choices can confuse and frustrate customers,  making them doubtful about their purchases rather than confident. Based on the answers a visitor gives, the company can add their email address to the right kind of marketing campaigns. Only with a chatbot can such advanced segmenting be made possible right from the very start.

Is Sophia a chatbot?

Criticism. According to Quartz, experts who have reviewed the robot's partially open-source code state that Sophia is best categorized as a chatbot with a face.

While this is a good option, the chance of converting your customers with a lead generation form is between 2.5% to 5%. While this is a respectable conversion rate, businesses should also apply the ‘second net’ strategy, which is effective for those website visitors who do not convert with landing pages and forms. No wonder many customers prefer asking a customer support agent to provide their product’s shipping status. One of the most common requests customer support agents get from customers is for refunds and exchanges. Companies often have a clear policy in place for processing such requests. This means, for customer support agents, performing most refunds and exchanges is a repetitive and monotonous task.


This is because a rule based chatbots give answers to your client’s questions from a set of predefined rules you create from known scenarios. For example a chatbot will present your firms service options, the client then select which they want. Additionally, Bing AI can power other Microsoft products and services, such as Cortana, the company’s virtual assistant, and Microsoft Edge, its web browser. Although Bing AI still needs some improvements, it is becoming a crucial component of Microsoft’s efforts to stay relevant and competitive during this new wave of AI-based conversational chatbots. Conversational AI and other AI solutions aren’t going anywhere in the customer service world.

  • NLU is even built to overcome obstacles such as mispronunciation, sub-optimal word order, slang, and other natural parts of human speech.
  • By using data and imitating human communication, conversational AI software helps computerized systems talk with humans in a more natural manner.
  • To do this, script your idealized interactions and compare these ideal interactions with the outcomes your solution generates.
  • ” There are also plenty of other projects which address the same problem, not least Microsoft’s own Guidance project.
  • It considers the context of the conversation, including previous messages, to generate more relevant and coherent replies.

Previously the company’s business model involved students paying to have questions answered by human specialists. After its stock fell in value by 40 percent when CEO Dan Rosensweig announced the company’s growth was being impacted by the emergence of ChatGPT, it quickly put together plans to build it into its services. UK-based energy supplier Octopus Energy has built ChatGPT into its customer service channels and says that it is now responsible for handling 44 percent of customer inquiries. Company boss Greg Jackson has said the app now does the work of 250 people and receives higher customer satisfaction ratings than human customer service agents. Expedia – one of the world’s most popular travel planning websites and apps – has integrated conversational AI assistance into its services. This means that rather than searching for flights, hotels, or destinations, customers can plan their vacations as if they are chatting with a friendly, knowledgeable travel agent.

Customer Frontlines

ChatGPT has gained immense popularity due to its natural language understanding, versatility, availability, continuous learning capabilities, ease of use, large-scale training, advancements in AI, and community engagement. OpenAI ChatGPT can be fine-tuned and updated with new data to improve its performance or adapt it to specific tasks. This allows the model to evolve and learn from user interactions, leading to potential enhancements in its responses over time.

Chatbots provide a less-annoying, more engaging way of collecting leads. Unlike forms, which simply demand email addresses in exchange for a lead magnet, a chatbot tries to start a thoughtful conversation asking the visitor what they would like to do. Instead, a better option would be to add a chatbot to your website’s homepage. This chatbot can be designed to ask sales-oriented questions to your audience and guide them to and through the checkout process.

The origin of the chatbot arguably lies with Alan Turing’s 1950s vision of intelligent machines. Artificial intelligence, the foundation for chatbots, has progressed since that time to include superintelligent supercomputers such as IBM Watson. The use of Conversational AI, including Chatbots and Virtual Assistants, in HR is a promising application of Artificial Intelligence, Natural Language Processing, examples of conversational ai and Machine Learning. These technologies can improve the overall efficiency of HR operations, create a more engaging and personalized employee experience, and promote inclusivity and diversity. The purchase order, with a specific order number, is created automatically because the conversational AI chatbot triggers an RPA bot in the background to create a purchase order in the cloud system.

AI customer experience examples add to CX playbook – TechHQ

AI customer experience examples add to CX playbook.

Posted: Tue, 14 Mar 2023 07:00:00 GMT [source]

That’s because your traffic is anonymous and there is no way for a company to identify and contact visitors who visited their website. In fact, you can ‘bake’ this function right into the chatbot’s chat window with an option clearly labeled ‘get a free refund’. And of course, in the same way, a chatbot can make a refund, it can also process item exchanges as well. Sometimes, the only thing standing between you and a sale is a customer’s inability to perform a simple action themselves in order to find what they want and make a buying decision. For example, PVR Cinemas own one of the largest chains of movie theatres in India. And on their website, you’ll find a chatbot that helps visitors quickly book movie tickets, view offers, and leave feedback.

  • Additionally, they can proactively reach out to your customer to offer support.
  • Both the benefits and the limitations of chatbots reside within the AI and the data that drive them.
  • And to interact like a human, conversational AI uses large amounts of data, machine learning, deep learning, and NLP (Natural Language Processing).

Your customers expect high levels of service, which is why only the most powerful AI-based solutions will be suitable in this capacity. Your AI solution will feed off the data it gathers from customers, developing its understanding of consumer queries and honing the service it delivers. The conversational AI application will also serve as a valuable data resource for you and your business. Statistics show that customers are certainly warming to the idea of AI-based technology, provided that this tech provides a high level of service and is deployed responsibly.

examples of conversational ai

Usually offering little other than a source of frustration for visitors, these chatbots are infamously repetitive and tedious to navigate. Rule based chatbots do have some advantages over AI, machine learning chatbots but they also have short comings that need to be fully considered. Generally speaking, chatbots do not have a history of being used for hacking purposes.

examples of conversational ai

Like most of the chatbots in this article, Bard was designed to compete with ChatGPT. So far, Google Bard is at an experimental stage, so we have yet to learn much about what the AI chatbot can accomplish or how much of a competitor it will become for ChatGPT or other AI-based conversational chatbots. Known as the “ChatGPT with superpowers,” ChatSonic is an innovative AI-based conversational chatbot built by Writesonic.

Conversational AI certainly provides better customer service compared to chatbots. Moreover, AI-powered virtual agents can share sales lead recommendations with employees. ‍The critical component of conversational AI is its use of natural language understanding (NLU). We predict that 20% of customer service will be handled by conversational AI agents in 2022. And Juniper Research forecasts that approximately $12 billion in retail revenue will be driven by conversational AI in 2023.

What is the difference between a chatbot and a digital assistant?

The main difference between virtual assistants and chatbots is their AI capabilities. Due to advanced NLU, IVAs can automate both complicated and repetitive tasks. On the other hand, rule-based chatbots are associated with easier deployment. Therefore, they tend to be economic customer service automation tools.