What is conversational AI?

Conversational AI lets humans simulate conversations with machines. By combining natural language processing (NLP) and machine learning (ML), conversational AI uses data to receive speech or text inputs and produce speech or text outputs to essentially have a conversation with its user. Chatbots, virtual AI assistants, and voice-activated assistants are all examples of conversational AI.

Conversational AI might seem new, but its first iteration was a chatbot developed in the mid-sixties by Joseph Weizenbaum¹. ELIZA was the world's first robot psychotherapist and used NLP to match patterns in inputs and produce responses. At a basic level, conversational AI still operates like this today with pattern recognition and predictive analytics. The major difference: ELIZA was limited to the scripts that were programmed into the product. Today, the development of large language models (LLMs), advancements in computational power, data availability, and ML let conversational AI continuously "learn" using a vast amount of data to respond to queries. As a result, contemporary conversational AI has a variety of uses from virtual assistants to customer service chatbots and automated support systems.

Conversational AI is helping democratize access to information, goods, and services, all while improving customer experiences. Making this exchange accessible to all — so that users don't need to be coders to receive answers from queries — requires the ability to have a natural language conversation. From the business end, conversational AI can lighten employee load and soften learning curves, ultimately improving operational efficiency.

Conversational AI consists of natural language processing (NLP), machine learning algorithms (ML), speech recognition, and dialog management systems.

Natural language processing

Natural language processing helps computers process and communicate human language using tokenization, part-of-speech tagging, named entity recognition, and sentiment analysis. Computational linguistics underpins these techniques, using data analytics to break down and analyze language and speech. A lexical analysis assigns data values to characters in a query. Grammatical analysis identifies the order of words in a query and tags them. Syntactical analysis then identifies the meaning of a word through its syntactical value.

For example in the sentence "I'm amazed at how light this laptop feels," syntactical analysis would identify the word "light" as an adjective describing "this laptop." Semantic analytics uses the syntactical output to determine the meaning of a word in context to understand the sentiment or the intent of a query.

To continue with the previous example, although "light" could refer to weight, color, or lack of substance, the syntactical context — combined with "amazed" and "feels" — suggests a positive sentiment. Semantic analysis interprets "light" as conveying appreciation for the laptop's portability rather than any negative connotation.

This is how conversational AI sounds conversational.

Machine learning

Machine learning uses data and algorithms to mimic the human ability to learn. Machine learning algorithms make a prediction based on data they have been trained on, evaluate the quality of the prediction against some predefined parameters, and update or optimize the decision process to improve future predictions.

A machine learning model can be trained in three different ways: supervised, semi-supervised, and unsupervised. Supervised learning uses labeled datasets to train an algorithm to make predictions. Unsupervised machine learning models are trained on data that isn’t labeled, enabling the algorithms to identify patterns and data groupings without human intervention. Semi-supervised learning is a combination of supervised and unsupervised models, applying learnings from smaller labeled datasets onto larger unlabeled datasets.

Machine learning works hand in hand with NLP for application recommendation engines, virtual assistants, and speech recognition technologies.

Speech recognition

Speech recognition, also known as speech-to-text, is a machine’s ability to convert spoken language into written text. Speech recognition relies on NLP and ML algorithms to understand the grammar, syntax, structure, and composition of an audio signal while language weighting, speaker labeling, acoustics training, and profanity filtering techniques train their model.

Language weighting improves recognition accuracy by classifying recurring words. Speech recognition can also label different participants as it transcribes speech — this is speaker labeling. Acoustics training enables the recognition software to adapt to different qualities of audio, from different ambient acoustic levels to various vocal attributes. Profanity filtering can be used to sanitize transcriptions by identifying profane language. This is commonly used in transcription services or dictation applications.

Dialog management systems

Dialog management systems interpret and contextualize conversations between machines and users. In both speech and text contexts, dialog management systems represent the set of processes a machine will perform to "understand" and "communicate" with a human user. For example, a user queries a travel booking chatbot to book a flight. The bot responds with a set of follow-up questions:

User: I want to book a flight.
Bot: Hi! Sure, I can help with that. What departure city?
User: Montreal.
Bot: Depart from YUL airport. What's your destination?
User: Paris.
Bot: Great! Depart from YUL and arrive at CDG.

This conversation is powered by a dialog management system that uses policy learning and feedback mechanisms to provide contextual and accurate responses. It identifies the request, follows the necessary next step, and continues the dialog in a natural, conversational way. Effective dialogue management ensures that the AI can handle multi-turn conversations and context switching.

Conversational AI has changed the way users meaningfully interact with everyday technology, and its importance can be seen in its benefits:

• Improved operational efficiency: Conversational AI, from virtual assistants to semantic search applications, helps employees find answers quickly and automate tasks so they can focus on more complex and creative endeavors. With less time spent on minute, time-consuming work, productivity, and operational efficiency skyrocket.
• Enhanced customer support: Conversational AI provides instant, personalized responses, improving customer satisfaction and engagement by offering 24/7 availability, reducing wait times, and handling a wide range of inquiries efficiently. It can address common issues quickly and accurately, freeing up human agents to focus on more complex tasks, leading to a more effective and responsive customer support system that builds stronger customer relationships and fosters long-term loyalty.
• Increased scalability: Conversational AI positively impacts an organization's ability to handle a high customer interaction volume, since it can respond to multiple users simultaneously. The scalability of interactions also improves operational efficiency through streamlined processes, reduced response times, and decreased operational costs. This enables the organization to serve more customers with fewer resources and enhance customer satisfaction as customers can troubleshoot their problems without waiting for a representative.

Ultimately, conversational AI is important because it is cost-efficient. Small- and medium-sized businesses benefit from conversational AI's ability to handle a high volume of interactions simultaneously, around the clock, thus reducing significant business costs related to training and salaries.

Conversational AI is a specific application of generative AI. However, they can have different goals, produce different outputs, and rely on different training. The difference is conversational AI continues, and maintains, a two-way interaction using prediction while generative AI produces content based on prompts.

When conversing with a user, conversational AI generates answers through analysis of the query, parsing of a knowledge base — a local knowledge base specific to a business, retrieved via retrieval augmented generation (RAG), or the whole of the internet in the case of ChatGPT — and pattern recognition.

The output a conversational AI generates is a prediction, which is the same principle that enables generative AI to "create" new content. This enables both conversational and generative AI to produce responses that are contextually relevant.

Learn more about generative AI

From "Hey, Siri," to virtual assistants and chatbots in banking apps, conversational AI is being used across industries. Some common use cases include:

Customer service and support

Conversational AI is commonly used in customer service and support applications, mainly in the form of chatbots to respond to common queries and perform some tasks. Chatbots can answer frequently asked questions related to shipping, billing, return policies, and more. They can also provide online support as a shopping assistant, offering personalized recommendations based on search history or previous purchases.

Healthcare

Conversational AI, when used in healthcare, can improve patient engagement and remove administrative load from providers. Speech recognition technology is used in some doctor’s offices to transcribe patient-doctor interactions and help keep updated and thorough notes relating to the patient and their care. Virtual assistants can also schedule appointments, provide medical information, and offer reminders for medication adherence.

Security and observability

Virtual assistants are an increasingly desirable component of any security or observability tech stack. By harnessing search technology, linking to local knowledge banks, and using data analytics, virtual assistants help IT users answer a variety of context-specific questions, access specialized knowledge and data, and automate certain tasks.

Ecommerce

Conversational AI is widely used in ecommerce to provide customer assistance to online shoppers. Whether in the form of a chatbot or a virtual assistant, conversational AI supplements human customer service reps by fielding commonly asked questions and offering personalized recommendations. The 24/7 availability of conversational AI also means it can positively impact customer experience, and as a result, drive sales.

Education and training

In an educational context, conversational AI is used to provide personalized tutoring, answer student queries, and facilitate interactive learning experiences. It offers additional resources to students, and in so doing, supports educators.

While chatbots are a form of conversational AI, they are only one application of conversational AI. Conversational AI is an umbrella term that covers a variety of NLP and ML applications, such as voice assistants, text-to-speech (TTS), and speech-to-text (STT) technologies.

Chatbots are trained on rule-based algorithms to handle specific tasks and provide predefined responses. Because of this, they are used for straightforward interactions and can sometimes struggle with complex or nuanced conversations.

Voice assistants are more advanced chatbots and use speech recognition technology to interact with the user. The benefit of voice assistants is that they are hands-free, and can perform a variety of commands in diverse contexts — while users are cooking, cleaning, driving, and so on.

Underlying the voice assistant’s ability to understand and respond to user interactions are TTS and STT technologies. When a voice assistant hears a prompt — "Hey, Siri" — it relies on STT to identify and understand it. In order to respond, "Yes?", it must use TTS, converting the "learned" text answer into a voice answer.

Other technologies also integrate with conversational AI, such as sentiment analysis. A chatbot might need to recognize negative language in order to respond empathetically and provide effective customer service.

Developing and implementing conversational AI begins with identifying the context in which it is required and the objectives it must accomplish. From these decisions, you can design effective conversational flows, choose the right platform, and establish the metrics that measure success.

Cloud-based solutions offer businesses scalability and flexibility, without the need for extensive on-premises infrastructure. Hosting conversational AI applications on cloud platforms allows businesses to leverage powerful AI tools that scale as needed, and can be deployed much more easily.

Designing conversational flows

To design conversational flows, map your user journeys and rely on the most frequently asked questions (FAQs). FAQs are foundational to the development of a conversational AI tool, and the user journey can help inform which dialog prompts to prioritize in development. Starting small is key.This enables you to test a use case and adjust the output tone (friendly, neutral, etc.) to fit your brand and needs.

Choosing the right platform

Choosing the right conversational AI platform starts with clear objectives. Is it a customer service chatbot, do you require multilingual capabilities, or are you looking for automation features? From there, you can identify the capabilities of various platforms and test pre-trained models to see if they're the right fit.

Consider whether the platform can seamlessly integrate into your current systems, and assess human-in-the-loop capabilities — how much manual labor is required or possible to make adjustments? Choosing the right platform will also depend on your customization needs.

Establishing success metrics

Once implemented, you'll need to establish success metrics based on your objectives. These success metrics should measure user satisfaction, response accuracy, response speed, and interaction completion rates.

Ensuring data privacy and security

Ensuring data privacy and security is crucial to developing effective conversational AI. By choosing a platform that integrates data privacy and security measures and establishing data privacy and security guidelines in the implementation process, companies can meet compliance and regulation standards. This is essential to maintain trust and avoid legal issues.

Developments in NLP and ML have produced much more sophisticated conversational AI applications, however, the technology still struggles with a set of challenges:

• Understanding unclear or ambiguous user input is a significant challenge for conversational AI. Tone, sarcasm, typos, syntactical errors, and more, can confuse the AI and yield incorrect, inaccurate, or unsatisfactory responses.
• Handling accents and different languages can challenge voice- and speech-recognition software. The technology must be trained on a breadth of data which is a resource-intensive undertaking.
• Ensuring reliability and accuracy is another conversational AI challenge. Organizations must deploy resources to review and assess the output quality of their conversational AI tools to help avoid hallucinations. This can be time- and resource-intensive.

Additionally, ethical concerns in the use of conversational AI are an important challenge. Ensuring unbiased responses and maintaining user privacy are critical in conversational AI development. Developers must address these issues to create fair and trustworthy systems.

As NLP, ML, and search technologies develop, conversational AI will become increasingly sophisticated — from improved understanding of inputs to better emotion and sentiment detection. The personalization capabilities of conversational AI will improve, enhancing user experiences.

We're likely to see continued integration of conversational AI tools in IoT and smart devices to continue improving customer interactions with machines.

Elastic leverages generative AI to power its natural language AI Assistant to guide SREs and security analysts from detection to resolution. Using conversational AI, the AI Assistant acts as a copilot to automate and streamline processes — freeing up engineers and analysts from mundane tasks so they can focus on more complex problems.

Take a deeper dive into conversational AI with Elastic

• IBM partners with Elasticsearch to deliver conversational search with watsonx Assistant
• NLP vs. LLMs: Understanding the differences
• How to make a chatbot: Dos and don'ts for developers in an AI-driven world
• Elastic Generative AI Tools and Capabilities
• Optimizing chatbots with NLP & vector search in Elasticsearch

¹ ELIZA chatbot