Before getting into, what is conversational AI, let’s understand, what a conversation is. 

Oxford Learners says that a conversation is a talk, especially an informal one, between two or more people, in which news and ideas are exchanged. Now, if we combine this and the idea of artificial intelligence, which is “intelligence exhibited by machines or software” (Wikipedia), then we can understand conversational AI. 

What is conversational AI? 

Conversational AI is a field of artificial intelligence that can communicate with people naturally, solve problems and also, become better at communication over time. Shortly, conversational artificial intelligence is using technology to facilitate smooth conversation between humans and computers to achieve a favorable outcome. 

The mode of interaction can be text, video or voice. Imay be present over web interfaces (websites), over phone (IVR calls), through social media (Facebook) or embedded into the product. 

An example of conversational AI could be AI chatbots that you see on websites, or talk to over a customer support call. Another type of conversational AI could be your personal assistants like Siri, Alexa or more. 

How does conversational AI work?

Many research papers have devolved deep into the various components of conversational AI. The image shows a conversational artificial intelligence agent architecture in detail. 

Elements of conversational AI

Image Reference: A Survey on Evaluation Methods for Chatbots Wari Maroengsit et al. 

Let’s discuss the conversational AI architecture in detail. Mostly, the conversational AI architecture contain 3 step operation: 

  • Pre-processing 
  • Processing 
  • Generation 

We’ll cover all three steps in detail. 

Now, let’s consider this is a consumer facing conversational AI. The customer types the query into the “user interface”. At the back of this user interface can be two types of AI chatbots : rule based or AI based. Rule based chatbots are simple chatbots like ELIZA which use pattern matching to give answers or use workflows, whereas AI based chatbots like ones created by Alltius, use AI to generate response to the customer queries. 

After this, the query goes to.. 

Step 1: Pre-processing using NLP

The preprocessing stage is centered around Natural Language Processing (NLP), which is essential for gathering external textual data to build the data corpus or acquiring new data from user-system dialogue interactions. This stage is crucial for preparing and converting data into a suitable text format for further processing by the chatbot. The data obtained through this process serves two primary purposes. Firstly, it acts as the foundational knowledge base or database for the system. Secondly, the data accumulated from interactions with users is utilized as information to enhance the chatbot's understanding of the user in the processing phase. In this phase, NLP techniques such as pattern recognition, parsing, Term Frequency-Inverse Document Frequency (TF-IDF), and Word2Vec are predominantly employed to collect information. 

Step 2: Processing using NLU

The next step involves using the input from the previous step to better understand the intent behind the user’s words. This involves a series of steps like: 

Intent Classification which identifies the intent or purpose behind the user's input. Common intents include greeting, purchase order, complaint, etc. Next is Dialogue Management which involves tracking the context and flow of conversations across multiple turns. Allows the bot to have meaningful dialogues. Key aspects are:

  • Dialogue state tracking - Tracking what's been discussed to provide appropriate context for the conversational AI’s next response.
  • Dialogue policy - Determining next best action during conversations based on state and history.

Next, using Named Entity Recognition (NER) to identify and classify key entities (example: person, location names) within text input. Allows for better understanding. 

This is followed by Vector Similarity Recognition which calculates similarity between vectors to match appropriate responses.Lexicon Based Techniques use a vocabulary dictionary to analyze text by breaking it down into root words and parts of speech. Allows determining meaning and context.Long Short Term Memory (LSTM), a type of advanced recurrent neural network used to understand context based on previous chat turns. Helps classification and prediction for next responses.

The exact set of techniques used depends on the complexity of conversations that the conversational AI chatbot is expected to handle. But NLU forms the core component that allows understanding user inputs.

Step 3: Generation using NLG

The generation stage in conversational AI, derived from Natural Language Generation (NLG), focuses on how the system or agent formulates responses to the user based on the context of each conversation, including the user's intent and the system's knowledge, such as named entities within sentences. This step is crucial for providing information to the user or asking questions to gather more information, necessitating a deep integration of NLG. To generate appropriate responses, the system relies on a robust knowledge base and a thorough understanding of the user, acquired from both the preprocessing and processing stages. Examples of NLG applications in literature include various chatbots and interfaces, such as AliMe and e-commerce website-based chatbots, demonstrating the practical implementations of these concepts.

Retrieval-based and generative-based approaches represent two primary methods in the generation phase. Retrieval systems search through a large database to find and return the best match for a user's query, relying on a data corpus of texts for linguistic and lexicographic reference, as well as dialogue agent intents to simulate conversations across multiple domains. Conversely, generative approaches, often based on recurrent neural networks (RNNs), create new replies but may face challenges in generating meaningful responses. The NLG process involves content determination, where the message's informational content is decided, and sentence planning, which focuses on the organization and realization of the message, showcasing the complexity and depth of generating human-like interactions in conversational AI systems.

Key technologies behind Conversational AI

We’ve already discussed them above, but let’s understand them again. 

Natural Language Understanding (NLU)

NLU is pivotal in enabling conversational AI to grasp the intent behind a user's words, distinguishing between different meanings and contexts. This understanding is critical for generating accurate and relevant responses, ensuring that interactions are smooth and natural.

Natural Language Generation (NLG)

NLG complements NLU by empowering conversational AI to construct responses that mimic human conversation. This capability transforms interactions from transactional exchanges to engaging conversations, enhancing user experience.

Machine Learning and Deep Learning

Machine Learning and its subset, Deep Learning, are the driving forces behind conversational AI's ability to learn from data. By analyzing vast amounts of interactions, these systems learn to predict and generate more accurate responses over time, continually improving their performance.

Speech Recognition

For voice-activated conversational AI, speech recognition is indispensable. This technology converts spoken language into text that the AI can process, enabling users to interact with devices through voice commands.

Large Language Models in Conversational AI

Large Language Models (LLMs) have been a game-changer for conversational AI. Trained on extensive datasets, these models understand and generate human-like text, significantly enhancing the quality of AI-generated responses. LLMs have made conversational AI more versatile and capable of handling a broader range of topics and conversational nuances.

Why do we need conversational AI? 

Conversational AI, even at an infancy stage, has made considerable difference in how we live our day to day lives. Major platforms we interact with use conversational AI either at their workplace or their front-end, eg. conversational AI customer assistants, to help humans complete their tasks efficiently. 

Currently, while strong AI (an AI which has human-like consciousness and can solve tasks better than human) is still a theoretical concept, weak AI is pushing barriers to how human-computer conversations can yield fruitful results. Here are some examples of conversational AI use cases: 

  • Customer Support: Online customer support AI chatbots are used heavily by companies to reduce the workload on human agents and solve common frequent queries of customers without any lag. These AI assistants (depending on the platform used) can solve tasks ranging from solving login issues to answering complex queries requiring multiple steps like claims.  
  • Sales Assistant: Companies are using AI assistants for sales teams to reduce time to information, craft tailored pitches or identify ICP customers on the go. Such AI assistants help sales teams sell more with less effort.  
  • Website Assistant : Almost every website has a chatbot, and the majority of the websites are shifting from rule-based chatbot to conversational AI chatbot. These conversational AI assistants help visitors find information without sifting through multiple pages. 
  • Human Resources: Processes like employee onboarding, training or query resolution can be solved by using smart conversational AI assistants. 
  • Healthcare: Conversational AI assistants can play a major role to make information readily accessible to all stakeholders like patients, hospitals, doctors and insurance companies. 
  • Knowledge base management:  Conversational AI is a perfect match for knowledge base management use case where you can feed into your entire database and then find insights, information or more by just talking to it.  

Benefits of conversational AI 

Conversational AI offers a myriad of benefits for businesses and organizations, leveraging advanced artificial intelligence to transform customer service, sales, and user engagement. Here's an overview of the key advantages:

Enhanced Customer Experience

Conversational AI provides immediate, 24/7 support to customers, answering queries, solving problems, and offering assistance without delays. This constant availability significantly improves the customer experience, as it ensures users can receive help whenever they need it, without being constrained by business hours or limited human resources. 

Personalization at Scale

With its ability to analyze and interpret large volumes of data, conversational AI can deliver highly personalized interactions. It can recommend products, services, or information tailored to individual user preferences and past behavior, making each interaction feel unique and understood.

Increased Operational Efficiency

By automating routine inquiries and tasks, conversational AI allows businesses to handle a higher volume of customer interactions without additional costs. This automation frees up human agents to focus on more complex and nuanced issues, thereby increasing overall operational efficiency.

Cost Reduction

Implementing conversational AI can lead to significant cost savings. It reduces the need for a large customer service team to manage inquiries and support requests, thus lowering labor costs. Additionally, by streamlining operations and minimizing the time spent on each interaction, conversational AI can decrease overhead expenses.


Conversational AI systems can easily scale to accommodate fluctuations in demand without the need to hire and train additional staff. This scalability ensures that businesses can maintain high levels of customer service even during peak periods or as the company grows.

Data Collection and Insights

Conversational AI platforms collect valuable data from interactions with users, providing businesses with insights into customer preferences, behaviors, and feedback. This data can be used to inform decision-making, improve products and services, and tailor marketing strategies.

Enhancing Employee Satisfaction

By handling repetitive and mundane tasks, conversational AI allows employees to focus on more rewarding and challenging aspects of their jobs. This can lead to higher job satisfaction, reduced turnover, and a more motivated workforce.

Challenges for conversational AI 

While conversational AI offers significant benefits, it also faces several challenges that businesses and developers must address to fully leverage its potential. Here are some of the main challenges associated with conversational AI:

Understanding Context and Nuance

One of the most significant challenges for conversational AI is accurately understanding and maintaining the context of a conversation, especially over longer interactions. Human language is nuanced and often relies on context, which can be difficult for AI to interpret correctly. This includes understanding idioms, sarcasm, and subtle cues that humans easily grasp.

Handling Complex Queries

Conversational AI systems can struggle with complex or multi-part queries that require an understanding of several variables or steps. While advancements in natural language processing have improved their capabilities, there's still a gap in handling intricate conversations as seamlessly as a human would.

Continuous Learning and Adaptation

Ensuring that conversational AI systems continue to learn and adapt over time without human intervention is challenging. These systems need to be designed to learn from interactions and feedback to improve their accuracy and effectiveness, which requires sophisticated machine learning algorithms and ongoing monitoring.

User Privacy and Data Security

With conversational AI collecting and processing vast amounts of personal data, ensuring user privacy and data security is paramount. Businesses must navigate the complexities of data protection regulations and ensure their systems are secure against breaches, which can undermine user trust and lead to legal consequences.

Multilingual Support

Developing conversational AI systems that can support multiple languages and dialects is challenging but necessary for global applications. This involves not just translating words but also understanding cultural nuances and colloquialisms in different languages.

Integration with Existing Systems

Integrating conversational AI into existing business systems and workflows can be complex and time-consuming. Ensuring seamless integration while maintaining system performance and data integrity requires careful planning and execution.

User Acceptance and Trust

Building user trust in conversational AI systems is crucial for their success. Users need to feel confident that their queries will be understood and addressed accurately and that their data is handled securely. Overcoming skepticism and building a positive user experience is essential for widespread adoption.


Designing conversational AI systems that can scale efficiently with increasing numbers of users and interactions presents technical challenges. Scalability involves not just handling larger volumes of queries but also maintaining performance and accuracy levels.

Addressing these challenges requires ongoing research, development, and investment in conversational AI technologies. Businesses must also focus on designing user-centric systems that prioritize privacy, security, and seamless integration into users’ lives.

Conversational AI with Alltius 

Are you interested in implementing AI at your organization?

Alltius is pioneering the use of generative AI in customer success and sales to improve buyer journeys across channels. Alltius is made by AI experts from CMU, Google, Amazon and more. With, sales and customer success teams can sell 3X more and reduce average resolution time to 10 seconds within weeks. Access a free trial or book a demo.  

Additional Readings on conversational AI 

If you’re interested, feel free to dive deeper into conversational AI with following resources:

How to build conversational AI in 2024?
March 30, 2024
10 best conversational AI platforms in 2024
March 30, 2024
What is conversational AI?
February 22, 2024
Understanding 7 Types of Artificial Intelligence 
February 22, 2024
What Is a Rational AI Agent?
February 22, 2024
What are Different Types of AI Agents?
February 22, 2024

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