What is conversational AI?

Conversational AI is a technology that enables machines to understand, interpret, and respond to natural language in a way that mimics human conversation.

For example, there are AI chatbots that offer a more natural and intuitive conversational experience than rules-based chatbots. AI chatbots use probabilistic machine learning models, natural language understanding (NLU), and conversational flow management (CFM) to understand what the user wants, hold contextual conversations, make real-time decisions, and proceed with the next actionable next steps.

Conversational AI technology can be used to power various applications beyond just chatbots. Voice assistants, like Siri, Alexa, and Google Assistant, are examples of conversational AI tools that use voice as the primary input to interpret and respond to user requests.

Virtual assistants are another type of conversational AI that can perform tasks for users based on voice or text commands. These can be standalone applications or integrated into other systems, such as customer support chatbots or smart home systems.

With ChatGPT and GPT-4 making recent headlines, conversational AI has gained popularity across industries due to the wide range of use cases it can help with. But simply making API calls to ChatGPT or integrating with a singular large language model won't give you the results you want in an enterprise setting.

For conversational AI to create a lasting impact within an enterprise, it needs to connect each system and application across an enterprise, allow them to communicate seamlessly, and then take action based on the user's needs. Without deep integrations with company-specific data and the systems and apps within your organization, conversational AI use cases will be lackluster at best and downright useless at worst.

For instance, while you could ask a chatbot like ChatGPT to add you to a sales distribution list, it doesn’t have the knowledge or ability to understand and act on your request.

If, on the other hand, an enterprise uses conversational AI specifically tailored to their organization and integrated with their tech stack, it would be able to comprehend the request and add you to the correct list.

Although the spotlight is currently on chatGPT, the challenge many companies may have and potentially continue to face is the false promise of rules-based chatbots. Many enterprises attempt to use rules-based chatbots for tasks, requiring extensive maintenance to prevent the workflows from breaking down.

It’s clear that rules-based chatbots dependent on brittle dialogue flows and scripts simply don't work, but up until recently, they were the only option available. With recent advancements in conversational AI, that's changed. Now, businesses can use this technology to build custom conversational AI use cases without sacrificing the integrity of the output.

With conversational AI, building these use cases should not require significant IT resources or talent. Instead, conversational AI can help facilitate the creation of chatbot use cases and launch them live through natural language conversations without complicated dialog flows.

Conversational AI changes how companies interact with customers, streamline operations, and enhance user experiences. Unlike traditional rules-based chatbots, conversational AI employs advanced probabilistic machine learning models, natural language understanding, and conversational flow management to comprehend user requests, hold context-rich conversations, and make real-time decisions. This technology goes beyond chatbots and extends to voice assistants and virtual assistants, enabling versatile applications across industries.

The importance of conversational AI lies in its ability to connect systems and applications within an enterprise, allowing seamless communication and meaningful action based on user needs. Deep integrations with company-specific data and tech stack are essential to unlock the full potential of conversational AI in an organizational setting. Conversational AI addresses the limitations of rules-based chatbots by offering more flexibility and adaptability, making it a valuable tool for businesses looking to improve customer engagement, automate tasks, and stay competitive in the evolving digital landscape.