What is Conversational AI?

Conversational artificial intelligence (AI) is a technology that allows software to process and respond to human voice- or text-based conversations. Traditionally, human chat with software has been limited to preprogrammed inputs where users input 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 to deliver personalized customer experiences.

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 guides users to specific topics. For instance, your users can ask chatbots about product details or for step-by-step instructions on how to use your services. Another example is AI-driven virtual assistants, which can answer user queries with real-time information, ranging from shortened meeting minutes to highlights from a presentation.

These interactions use natural language processing to interpret human language and deliver an accurate response. Some advanced systems can even use automatic speech recognition to support voice-based customer service within web and mobile messaging apps.

Data capture

Instead of using a form, 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.

Conversational AI works by analyzing both the user's intent and the query’s context. For submission forms that seek user feedback, AI systems can collect information more naturally.

Transactional

In transactional scenarios, conversational artificial intelligence facilitates tasks that involve any transaction. For instance, customers can use AI chatbots to place orders on ecommerce platforms, book tickets, or make reservations. 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.

Machine learning helps these AI systems routinely automate these tasks without manual intervention. With the correct training and guardrails, an AI agent can ensure transactions are completed securely and accurately to given standards.

Proactive

In proactive conversational AI, the system initiates conversations or actions based on specific triggers or outcomes from 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 on shipping or service disruptions so that customers do not have to wait for a human agent.

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, such as sarcasm, metaphors, variations in sentence structure, and exceptions in grammar and usage. Machine learning (ML) algorithms for natural language processing allow conversational AI models to continuously learn from vast amounts of textual data and recognize diverse linguistic patterns and nuances.

Natural language understanding

Natural language understanding (NLU) is concerned with the system's comprehension. 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, based on a precise understanding of the user's needs.

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 context-aware responses. Moreover, as AI chatbots interact more with users and human agents, their responses become refined and more flexible over time.

Conversational AI comprises systems designed to interact with users in natural, human-like ways via text or voice commands.

Chatbots

Chatbots have conversations with users via text chat. Traditionally, chatbots were rule-based and operated on predefined scripts to handle straightforward tasks like answering FAQs. Large language models power modern conversational AI chatbots and can understand conversation context and user sentiment. They can manage more complex interactions and solve customer problems more proactively. They also learn and adapt to user behavior over time, providing increasingly relevant responses.

Voice assistants

Voice assistants can interact with users through natural language speech. Systems like Amazon Alexa interpret and respond to spoken commands, integrating with smart devices to facilitate hands-free assistance. They can be used for tasks such as controlling home-automation devices, retrieving information, or managing schedules.

AI Assistants

AI assistants, also known as copilots, are conversational AI solutions integrated into employee and business user workflows. They integrate with the organization's knowledge sources and digital systems to assist with employee tasks. For example, copilots provide code suggestions, answer questions about code, or generate analytics reports from natural-language conversations. They can take over administrative and other digital tasks, allowing employees to focus on problem-solving.

Other types

Conversational AI can be implemented outside of traditional software applications, such as virtual shopping assistants or interactive kiosks. They can also have social use cases for casual, empathetic conversations. You may also have industry-specific chatbots, such as educational bots that help students with learning and tutoring, or healthcare bots that assist with patient health management.

Modern conversational AI systems combine multiple AI technologies to process human language and help out customers. By using natural language processing (NLP), natural language understanding (NLU), and natural language generation (NLG), models can interpret a customer’s input and produce a useful output to assist them. The better the system performs, the better the customer experience.

Instead of a customer having to navigate a rigid menu or select from predefined commands, these systems can understand user intent, respond to customer inquiries in real time, and point to the right solution. They also simulate human conversation, making these chatbots fit more naturally into the customer support process.

As conversational AI technologies have continued to evolve with advances in machine learning, they can now personalize each interaction using customer data. When addressed and engaged with in a personalized way, users experience higher customer satisfaction with your support process.

Alongside just serving customers, you can also use conversational AI to make support available around the clock. This is especially helpful for smaller teams that may not have human agents across different time zones.

Conversational AI technology brings several benefits to an organization's customer service teams.

Improved customer satisfaction and 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 brand engagement.

Additionally, you can integrate past customer interaction data with conversational AI to deliver a personalized experience. For instance, it can make recommendations based on past customer purchases or search inputs. Chatbots can use dialogue management from prior conversations for context to improve customer engagement.

Improved operational efficiency

You can use conversational AI capabilities 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 worldwide.

Wider accessibility through human language

Conversational AI can 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.

Cost savings through automation

You can use conversational AI to reduce operational costs by handling common inquiries and automating routine tasks. Conversational AI systems help decrease the total number of interactions that actually require intervention from human agents. That means you can scale customer care without proportionally increasing staff costs.

Conversational AI technology can operate around the clock and across different time zones. Instead of needing to have a global customer service team to cover each time zone, you can cut costs by having a centralized conversational AI chatbot agent.

Enhanced employee productivity

An AI agent can help point your employees in the right direction if they need guidance, access to information, or need to complete a task. An internal conversational AI system can, for example, manage customer service tickets via natural-language queries. Instead of completing manual workflows, this significantly speeds up response times and keeps your business fully operational.

Conversational AI solutions greatly offload administrative responsibilities, allowing your employees to focus elsewhere. Human agents can jump in for more complex tasks, while your conversational AI can handle routine internal processes and typical customer interactions.

Conversational AI is just as useful within the organization as it is as an employee-facing application. Here are some examples of conversational AI within the business.

Knowledge base

Conversational AI can be positioned atop your file systems to understand your business and answer your team’s questions. For example, the marketing team might ask their department’s chatbot to find the number of page visits from the United States for the previous month.

Co-pilot assistant

Employees can become empowered to build out their own automations and agents using a co-pilot assistant. Co-pilot software is sophisticated enough to build complete end-to-end workflows for automations based on natural language input. This replaces the need for custom-coded workflows. The co-pilot must have access to the right tools and permissions for these automations to work successfully.

Faster collaboration

Using conversational AI, employees can schedule meetings, derive meaning from written or recorded conversations, pull information from multiple sources, and perform other collaborative tasks.

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, augment data with synthetic datasets, and more.

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 creates 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 as well as conversational AI.

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. In this way, generative AI both enhances conversational AI and helps overcome its challenges.

Amazon Web Services (AWS) has a range of services to support your work in combining conversational AI in your business:

Amazon Connect is a complete AI-powered contact center solution for delivering personalized customer experiences at scale.

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. Amazon Lex provides high-quality speech recognition and language understanding capabilities. With Amazon Lex, you can add sophisticated conversational AI software to new and existing applications.

Amazon Q is a generative AI assistant that transforms how work gets done in your organization. With specialized capabilities for software developers, business intelligence analysts, contact center employees, supply chain analysts, and anyone building on AWS, Amazon Q helps every employee gain insights into their data and accelerate their tasks.

The AWS Solutions Library makes it easy to set up chatbots and virtual assistants for implementing conversational AI. 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 a free account today.