What is Conversational AI?

Conversational AI covers a wide spectrum of technologies that allow humans to talk with computers. Conversational AI is what powers applications like chatbots or AI agents and assistants. It allows customers to engage and complete tasks easily: by simply having a conversation.

At Conversation Design Institute (CDI), we see Conversational AI as a collective term that describes both the skillset and technology that allows people to engage with automated systems through language. That can vary from chatting with adidas’ chatbot about the delivery date of your new sneakers to speaking with your health service provider’s voice assistant, to having ChatGPT help with your homework assignment.

To make this work, Conversational AI leverages design and writing skills, human language datasets, machine learning algorithms, natural language processing (NLP), and of course, large language models (LLMs). Regardless of the technology used, the main goal is to simulate human interactions by interpreting spoken or written inputs, recognizing speech patterns, and comprehending meaning – across languages and channels.

As we dive deeper into Conversational AI, here’s a short glossary of some terms that will make it easier for you to understand the field. You and your team will need to be familiar with the following:

Natural Language Processing (NLP)

NLP is the backbone of traditional Conversational AI, this technology allows machines to understand and process human language. It involves tasks such as text parsing, sentiment analysis, and language translation.

Large Language Models (LLM’s)

Generative Conversational AI assistants, such as ChatGPT or Google Gemini are powered by LLM technology. Trained on terabytes of content, this technology allows for open ended conversations and generation of content on the fly. For business applications, LLM power needs to be guard railed and grounded by different means, to prevent incorrect answers.  

Speech Recognition

Speech recognition technology allows Conversational AI systems to understand spoken language. By converting audio input into text, it enables seamless communication through voice interfaces.

Intent Recognition

Intent recognition is crucial for understanding customer requests and providing relevant responses. Conversational AI systems analyze customer input to identify the underlying intent, allowing them to offer personalized and contextually appropriate solutions.

Dialogue Management

Dialogue management orchestrates the flow of conversation between customers and AI systems. It manages turn-taking, context retention, and response creation/generation to ensure coherent and natural interactions.

Conversation Design & Prompt Design

Conversation design is the art of mapping flows of conversation and turning knowledge into a human-centric conversational flow. AI-powered conversations need to offer correct levels of empathy and helpfulness to make the interaction between your customers and your AI assistant successful. Conversation design also entails the mapping of customer journeys to ensure that when you leverage LLMs, you can prompt it to suggest the best next action. This is also known as Prompt Design.

Integrations

Integrating with backend systems allows conversational AI to access and leverage data from various sources. Integrations enable chatbots to perform tasks such as retrieving information, making reservations, and processing transactions. In short: getting things done for your customers.  

Multimodal Interfaces

Multi-modal interfaces support interactions through multiple channels, including text, voice, and visuals. By offering flexibility in communication, they enable customers to engage with conversational AI in the way they prefer.

Automation can be a confusing term as it can refer to both a goal and a method of achieving a goal. For the purposes of this chapter, we’ll be referring to different types of automation possibilities that can help you reduce repetitive human effort to do things.

Seen in this light, automation plays a pivotal role in enhancing the efficiency, scalability, and effectiveness of conversational AI systems. By automating repetitive tasks, streamlining processes, and augmenting human capabilities, organizations can unlock new opportunities for innovation and growth.

Task Automation

Conversational AI systems can automate a wide range of tasks and processes, including customer service inquiries, appointment scheduling, order processing, and information retrieval. By deploying chatbots, virtual agents, and voice assistants, you can automate routine tasks, reduce manual effort, and improve operational efficiency.

Contextual Automation

Contextual automation involves leveraging contextual information, customer preferences, and historical data to automate personalized interactions and anticipate customer needs. By analyzing customer behavior, transaction history, and previous interactions, conversational AI systems can deliver more relevant recommendations, responses, and offers tailored to individual customers.

Workflow Automation

Workflow automation enables you to automate complex business processes and workflows using Conversational AI. By integrating conversational interfaces with backend systems, databases, and third-party applications, you can streamline processes such as lead qualification, sales pipeline management, and customer onboarding, reducing manual effort and accelerating time-to-value.

Self-Service Automation

Conversational AI systems empower your customers (or employees) to self-serve and find solutions to their queries or issues independently. By providing access to knowledge bases, FAQs, troubleshooting guides, and interactive tutorials, you can empower customers to resolve common issues, access information, and complete tasks without requiring human intervention, enhancing customer satisfaction and reducing support costs.

This may come as a surprise, but your AI assistant is never done. Just like people, they can grow and become better, when giving attention and a little bit of love.

Analytics are an important part of making your chatbot or voice assistant better. By leveraging analytics in conversational AI, organizations can gain actionable insights, drive continuous improvement, and deliver exceptional customer experiences that drive adoption and ultimately improve your bottom line.

Here are some things you should be focusing on: