Conversational AI overview

Conversational AI allows machines to understand, process, and respond to human language in natural and meaningful ways. The first conversational bot, ELIZA, was created in 1966. It used a method called pattern matching to provide pre-programmed answers in response to specific words in users’ inputs. Over half a century later, many bots still use pattern matching. However, with the advent of powerful AI technologies like natural language processing (NLP), machine learning (ML), large language models (LLM), and deep learning, it’s now possible to create conversational bots—including AI copilots—capable of more human-like conversation, learning, and logic.

What are the benefits of conversational AI?

Conversational AI bots offer convenient, seamless service delivery. They can be embedded in applications, allowing users to perform various tasks without needing separate apps to access them.

But as with any technology, conversational bots work best when they’re created with a clear understanding of users’ needs and preferences. When there are shortcomings in the design or supporting IT infrastructure, users may find the experience more frustrating than helpful. But when done well, bots offer consumers and businesses an impressive array of benefits:

Improved customer service: Chatbots provide 24x7 customer support, providing instant responses to inquiries, reducing wait times, and improving customer satisfaction.

Streamlined operations: Approving workflows, requesting vacation time, booking travel, and finding information across multiple sources are just a few use cases for business.

Cost efficiency: By automating routine inquiries and tasks, conversational AI allows employees to focus on higher-value tasks. This leads to savings in labor costs and increased worker satisfaction.

Scalability: Bots easily scale to handle a high volume of simultaneous interactions, ensuring consistent service quality during peak times and reducing the need to increase staff.

Personalized experiences: AI chatbots and voice assistants can analyze user data to deliver personalized recommendations, support, and services.

Data insights: Businesses can collect data from interactions, providing insights into customer behavior, preferences, and feedback, which can inform strategies and decision-making.

Multilingual support: The ability to support multiple languages makes it easier and more cost-effective to cater to a global audience.

Accessibility: For those who have difficulties using traditional web or app interfaces, conversational bots offer an alternative means of interaction.

Efficient problem solving and decision-making: AI-powered systems can quickly process and analyze vast amounts of data to assist in decision-making and problem-solving.

Integration and automation: A single chatbot can integrate with multiple systems for seamless automation of tasks, such as booking appointments and conducting transactions—as well as integrating with consumer and industrial Internet of Things (IoT) systems.

What are the challenges of conversational AI?

Using conversational AI technology, businesses have made significant strides in improving how they interact with customers and streamline operations. However, these solutions can be complex and implementing any AI solution requires special considerations:

Understanding nuances and context

One of the key challenges for conversational bots is accurately interpreting the nuances and context of human language. Subtleties like sarcasm, idioms, and cultural references can lead to misunderstandings and inappropriate responses.

Maintaining conversational flow

Keeping a conversation flowing naturally is crucial for a positive user experience. Conversational bots can struggle to handle complex interactions or manage transitions between topics smoothly, which can disrupt the conversational flow.

Data privacy and security

Handling personal data securely is a major concern with all AI applications, especially when dealing with sensitive information. Complying with data protection regulations and safeguarding user privacy are critical challenges.

Scalability

As businesses grow, conversational AI systems need to scale accordingly, which can be technically challenging. Handling increased volumes of interactions without a drop in performance or speed requires robust infrastructure and continuous optimization.

Continuous learning and adaptation

Conversational AI systems must continuously learn from interactions to improve their accuracy and relevance. This ongoing training requires substantial resources and advanced machine learning capabilities.

Examples of conversational AI by industry

Conversational AI is transforming customer interaction and operational processes across various industries. From automating healthcare appointments to automating supply chain processes, these technologies make it possible to create custom business AI solutions that improve efficiency, enhance user engagement, and drive innovation. Here’s a look at some notable conversational AI examples:

Automotive

Empowering customers to search inventory, book test drives, discover recall information, and schedule maintenance appointments.

Education

Personalizing tutoring, transcribing lecture notes, and enhancing language learning through real-time conversations and coaching.

Energy and natural resources

Providing workers with quick access to safety protocols and streamlining incident reporting.

Financial services

Augmenting customer service and operational efficiency by offering personalized financial or insurance advice, assisting with transactions, and handling claims processing.

Healthcare

Improving patient outcomes and operational efficiencies through automated appointment scheduling and providing easier access to personal health data—while also maintaining privacy.

High tech

Providing technical support and engaging users in feedback loops to improve products.

Manufacturing

Enabling quick responses to operational issues, automating supply chain processes, and interfacing with industrial IoT devices.

Media and telecommunications

Routing customer support requests, creating subtitles and audiobooks, and helping customers find the movies, TV shows, and music they’re interested in.

Public sector

Improving citizen engagement by streamlining service requests and providing automated responses to common inquiries.

Retail

Enhancing online and in-store shopping by expediting customer inquiries, recommending products, processing orders, and providing after-sales support.

How does conversational AI work?

AI-based chatbots use ML, NLP, and natural language understanding (NLU) to understand users’ inputs and provide human-sounding conversational flows. Deep learning , a subset of machine learning involving many-layered neural networks, is a critical conversational AI technology for enabling bots to learn and make intelligent decisions instantly.

Key processes in how conversational AI technology works

Input interpretation

The process begins with the AI interpreting the user's input, which can be in text or speech form. Speech inputs are first converted into text using speech recognition technologies.

NLP, NLU, and deep learning

NLP allows the AI to break down and analyze the text. NLU, a subset of NLP, goes deeper by understanding the context and intent behind the user’s input. It uses deep learning to grasp nuances, ambiguities, and the specific meanings of words in various contexts, enabling a more accurate interpretation of the user’s needs.

Dialogue management

This orchestrates the conversation with the user, guiding the interaction based on the intent, context, and system capabilities. It may involve querying databases or executing specific actions to provide precise and relevant responses.

Response generation

The AI constructs a response that aligns with the user's request and the context of the conversation. This could involve choosing an appropriate answer from a set of predefined options or generating a new response through machine learning.

Continuous learning and adaptation

Through machine learning, the AI system continually improves, learning from each interaction. This enhances its language models and improves its ability to predict and respond to diverse requests.

Feedback loop

Incorporating user feedback allows the system to refine its performance, adjust its conversational models, and deliver more accurate responses in future interactions.

It’s important to note that even chatbots that use deep learning may incorporate less-advanced technologies like simple algorithms and pattern-matching. These older technologies are still useful when the bot developer or designer need to guide users through a specific series of actions, or to guide them to predetermined resources.

Types of conversational AI

Conversational bots can be categorized into three types based on their underlying technology: pattern-matching, algorithmic, and NLP/ML.

Pattern-matching chatbots are often quicker and less expensive to develop and are effective for narrow or well-defined applications where the range of user queries is limited and predictable. They’re particularly useful for tasks that require straightforward, canned responses, but they can’t understand context, intent, or variations in inputs that don't match their programmed patterns.

Algorithmic chatbots follow a set of logical operations or algorithms, and work well for applications where responses can be determined through a clear set of steps or calculations. While they may sound conversational, they don’t actually understand human language. However, they’re effective in scenarios where responses depend more on logic than language comprehension or learning from past interactions.

NLP and ML-based chatbots offer advanced and fluid conversational experiences, capable of interpreting a wide range of human inputs. They understand context, learn iteratively from interactions, and can respond with nuanced responses. They’re ideal for applications requiring a high degree of interaction variability and personalization, such as dynamic customer service environments and AI copilots.

The choice between the three types depends on the specific needs, budget, and desired user experience with the bot. While the initial investment in NLP and ML chatbots is higher, their ability to learn and adapt can provide a more engaging user experience—and potentially lower long-term costs by reducing the need for constant updates of algorithms and pattern databases.

How to build conversational AI

Creating conversational bots involves a systematic process to ensure they’re effective, engaging, and capable of understanding and responding to human inputs. Bots are typically designed and built on a conversational AI platform, which we’ll cover in the next section. Here’s a brief overview of each stage in the process:

Design

This phase focuses on defining the bot’s purpose, functionality, and the scope of conversations it can handle. This includes identifying the target users, the types of questions the bot will answer, its personality, and the conversational flows. The designers also decide on the platforms (web, mobile, social media) where the bot will be deployed.

Train

Training involves feeding the bot a large dataset of dialogues, questions, and answers to help it learn and understand the nuances of human language. This phase uses NLP and ML algorithms, including deep learning models, to enable the bot to recognize intents, extract relevant information, and respond appropriately.

Build

In the build phase, developers code the bot, integrating the trained models and implementing the designed conversational flows. This stage also includes setting up integrations with external systems or APIs for actions the bot will perform, like booking appointments or fetching data.

Test

Testing is crucial to identify and rectify issues in understanding, response accuracy, and user experience. It involves simulating conversations to ensure the bot behaves as expected across a variety of scenarios and inputs. Feedback from these tests is used to refine the bot’s responses and functionality.

Connect

Once tested, the bot is connected to the chosen platforms or interfaces where it will interact with users. This includes deploying the bot on websites, social media, messaging apps, or other digital channels. Ensuring seamless integration and accessibility for the intended audience is key.

Monitor

After deployment, continuous monitoring is essential to evaluate the bot’s performance, user satisfaction, and to identify areas for improvement. Monitoring tools can track conversations in real-time, allowing developers to update the bot’s training data, refine its algorithms, and add new features based on user feedback and changing needs.

Throughout these stages, collaboration among cross-functional teams—including UX designers, developers, data scientists, and content creators—is vital to build a conversational AI bot that’s user-friendly, intelligent, and scalable.

Should I use a platform to build conversational AI?

Good conversational AI platforms provide the tools, training, and infrastructure needed to create, deploy, maintain, and optimize chatbots and voice assistants. If your project is small or you’re just looking to experiment, consider a platform that offers no-code and low-code options, plus solid training resources. On the other hand, if you’re looking to create an enterprise-level solution, it might be best to choose a platform that provides comprehensive support for security, governance, testing, and scalable infrastructure.

Key things to consider in choosing a conversational AI platform

No-code and low-code: These capabilities empower users without deep technical expertise to build and deploy conversational apps. No-code and low-code platforms often feature:

• Drag-and-drop interfaces to simplify the design and flow of user interactions.
• Pre-built templates to accelerate bot development for common industry use cases.
• Customizable components to allow the bot to integrate with existing business systems.

NLP and NLU capabilities: For understanding user intent and context.

Multichannel integration: Allowing deployment across web, mobile, and social media platforms.

Scalability: The ability to handle varying volumes of conversations without degradation in performance.

Customization and personalization: Tools to tailor conversations to individual users or specific business needs.

Analytics and reporting: For insights into user interactions and bot performance, facilitating continuous improvement.

Security, compliance, and responsible AI: Ensuring data protection and adherence to regulatory standards, as well as guidance for ensuing that you’re implementing AI responsibly and ethically.

Proprietary vs. open source: Proprietary platforms typically provide comprehensive support and seamless integration for specific applications. Open source platforms offer greater customization and community-driven innovation but may require more technical expertise to implement and maintain.

Comparison of proprietary vs. open source platforms

Conclusion: from ELIZA to truly conversational AI

Many of us have been using conversational bots for years in the form of voice assistants like Alexa or Siri to shop, search the web, and access digital media. The technology has also become a common—if sometimes underwhelming—way to interact with businesses through automated phone directory systems, product selection wizards, and website chatbots. However, underwhelming experiences may soon become a thing of the past now that NLP and NLU technologies are making conversational AI bots more truly conversational.

FAQ