What is Conversational AI?
Conversational artificial intelligence (AI) is a technology that makes software capable of understanding and responding to voice-based or text-based human conversations. Traditionally, human chat with software has been limited to preprogrammed inputs where users enter or speak predetermined commands. Conversational AI goes much beyond that. It can recognize all types of speech and text input, mimic human interactions, and understand and respond to queries in various languages. Organizations use conversational AI for various customer support use cases, so the software responds to customer queries in a personalized manner.
What are the benefits of conversational AI?
Conversational AI technology brings several benefits to an organization's customer service teams.
Improved customer experience
Conversational AI chatbots can provide 24/7 support and immediate customer response—a service modern customers prefer and expect from all online systems. Instant response increases both customer satisfaction and the frequency of engagement with the brand.
Additionally, you can integrate past customer interaction data with conversational AI to create a personalized experience for your customers. For instance, it can make recommendations based on past customer purchases or search inputs.
Improved operational efficiency
You can use conversational AI solutions to streamline your customer service workflows. They can answer frequently asked questions or other repetitive input, freeing up your human workforce to focus on more complex tasks.
You can also gain cost benefits at scale. It can be costly to establish around-the-clock customer service teams in different time zones. It’s much more efficient to use bots to provide continuous support to customers around the globe.
Wider accessibility
Conversational AI can be used to improve accessibility for customers with disabilities. It can also help customers with limited technical knowledge, different language backgrounds, or nontraditional use cases. For example, conversational AI technologies can lead users through website navigation or application usage. They can answer queries and help ensure people find what they're looking for without needing advanced technical knowledge.
What are some use cases of conversational AI?
Conversational AI has several use cases in business processes and customer interactions. We’ve grouped these use cases into four broad categories.
Informational
In an informational context, conversational AI primarily answers customer inquiries or offers guidance on specific topics. For instance, your users can ask customer service chatbots about the weather, product details, or step-by-step recipe instructions. Another example would be AI-driven virtual assistants, which answer user queries with real-time information ranging from world facts to news updates.
Data capture
You can use conversational AI tools to collect essential user details or feedback. For instance, you can create more humanlike interactions during an onboarding process. Another scenario would be post-purchase or post-service chats where conversational interfaces gather feedback about the customer journey—experiences, preferences, or areas of dissatisfaction.
Transactional
In transactional scenarios, conversational AI facilitates tasks that involve any transaction. For instance, customers can use AI chatbots to place orders on ecommerce platforms, book tickets, or make reservations. Some financial institutions employ AI-powered chatbots to allow users to check account balances, transfer money, or pay bills. These uses are convenient for your customers and improve their experiences.
Proactive
When you use conversational AI proactively, the system initiates conversations or actions based on specific triggers or predictive analytics. For example, conversational AI applications may send alerts to users about upcoming appointments, remind them about unfinished tasks, or suggest products based on browsing behavior. Conversational AI agents can proactively reach out to website visitors and offer assistance. Or they could provide your customers with updates about shipping or service disruptions, and the customer won’t have to wait for a human agent.
How does conversational AI work?
Conversational AI works using three main technologies.
Natural language processing
Natural language processing (NLP) is a set of techniques and algorithms that allow machines to process, analyze, and understand human language. Human language has several features, like sarcasm, metaphors, sentence structure variations, and grammar and usage exceptions. Machine learning (ML) algorithms for NLP allow conversational AI models to continuously learn from vast textual data and recognize diverse linguistic patterns and nuances.
Natural language understanding
Natural language understanding (NLU) is concerned with the comprehension aspect of the system. It ensures that conversational AI models process the language and understand user intent and context. For instance, the same sentence might have different meanings based on the context in which it's used.
NLU uses machine learning to discern context, differentiate between meanings, and understand human conversation. This is especially crucial when virtual agents have to escalate complex queries to a human agent. NLU makes the transition smooth and based on a precise understanding of the user's need.
Natural language generation
After understanding the user's input, the system formulates a coherent and contextually appropriate response. Natural language generation (NLG) enables virtual agents to construct humanlike sentences in a clear, relevant, and linguistically natural manner. NLG uses powerful deep learning algorithms to formulate responses in context. Moreover, as AI chatbots interact more with users and human agents, their responses become refined and more flexible over time.
What is the difference between conversational AI and generative AI?
Generative artificial intelligence (generative AI) is a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music. Like all artificial intelligence, generative AI is powered by ML models. In particular, they use very large models that are pretrained on vast amounts of data and commonly referred to as foundation models (FMs).
Apart from content creation, you can use generative AI to improve digital image quality, edit videos, build manufacturing prototypes, and augment data with synthetic datasets.
Read about foundation models »
Conversational AI vs. generative AI
Conversational AI and generative AI have different end goals. The goal of conversational AI is to understand human speech and conversational flow. You can configure it to respond appropriately to different query types and not answer questions out of scope.
In contrast, generative AI aims to create new and original content by learning from existing customer data. In one sense, it will only answer out-of-scope questions in new and original ways. Its response quality may not be what you expect, and it may not understand customer intent like conversational AI.
Having said this, it’s important to note that many AI tools combine both conversational AI and generative AI technologies. The system processes user input with conversational AI and responds with generative AI. This solves challenges for use cases beyond the scope of conversational AI.
How can AWS support your conversational AI requirements?
Amazon Web Services (AWS) has many offerings to support your work with conversational AI.
Amazon Lex is a fully managed AI service with advanced natural language models. You can use it to design, build, test, and deploy conversational interfaces in applications. Powered by the same conversational engine as Alexa, it provides high-quality speech recognition and language understanding capabilities. With Amazon Lex, you can add sophisticated, AI-powered chatbots to new and existing applications.
Amazon Kendra is an easy-to-use conversational search service. It allows you to discover information stored within the vast amount of content spread across your company. For example, you can find data from manuals, research reports, FAQs, human resources documentation, and customer service guides. When you type a question, Amazon Kendra understands the context and returns the most relevant results, whether that means a precise answer or an entire document.
The AWS Solutions Library make it easy to set up chatbots and virtual assistants. You can build your conversational interface using generative AI from data collection to result delivery. Use the foundation model that best fits your needs inside a private, secure computing environment with your choice of training data.
Get started with conversational AI on AWS by creating an account today.
