What is Conversational AI? Comparing Conversational AI vs GenAI

Conversational AI not only reshapes the way we interact with machines but also unlocks new possibilities in connecting with customers. Meanwhile, the emergence of Generative AI has marked groundbreaking advancements in content creation, spanning text, images, and audio. So, what is Conversational AI, and how can it be distinguished from Generative AI? Let’s explore with FPT.AI in this article!

What is Conversational AI?

Conversational Artificial Intelligence (Conversational AI) is a branch of artificial intelligence (AI) that enables computers to understand, process, and generate conversations that mimic human language. Conversational AI models are trained on vast amounts of data, leveraging machine learning (ML), natural language processing (NLP), automatic speech recognition (ASR), and other technologies.

Moreover, two subfields of NLP play a crucial role in conversational AI:

• Natural Language Understanding (NLU): Allows machines or applications to process linguistic data and determine user intent based on context, purpose, syntax, and semantics.
• Natural Language Generation (NLG): A process by which machines generate text in natural language based on the input data they receive. NLG systems aim to interpret AI’s structured data for humans.

Conversational AI is utilized in other fields where understanding human language is essential, such as sentiment analysis, text-to-speech, and speech-to-text conversion. Among these, AI chatbots are the most common form of conversational AI. They are often integrated into customer communication channels such as websites, fan pages, Twitter, and more, supporting businesses in answering frequently asked questions (FAQs), troubleshooting, and even engaging in casual conversations.

However, it is important to distinguish between the terms “Chatbot” and “Conversational AI.” Conversational AI encompasses all technologies related to bots, including voice assistants, virtual agents, and text-based assistants using speech. In contrast, chatbots typically focus solely on text-based interactions with a more limited scope of application.

How does conversational AI work?

Conversational AI uses NLP to process large amounts of unstructured natural language data and transform it into structured data formats through computational linguistics and machine learning. This allows machines to understand information, make decisions, and generate responses. A machine learning algorithm must fully comprehend a sentence and the role of each word within it. Techniques like part-of-speech tagging are employed to ensure that the input text is accurately understood and processed.

Regardless of the scope and purpose of the conversational AI tool, the interaction process typically involves four steps:

1. Input Reception: The user initiates a query (either text or a voice prompt), which the tool receives. Using speech recognition technology, the voice input is converted into machine-readable text.
2. Input Analysis and Interpretation: Conversational AI leverages natural language understanding (NLU) to process, analyze, and interpret the meaning behind the user’s request.
3. Output Generation: By employing dialogue design, training data, and machine learning algorithms, conversational AI generates a response based on the user’s input.
4. Output Delivery: The generated output is sent back to the user.

Examples and Applications of Conversational AI

Organizations can develop and deploy various conversational AI tools for diverse practical purposes, such as:

1. All-encompassing, subscription-based chatbots: Advanced chatbots like OpenAI’s ChatGPT generate text responses to user queries on a wide range of topics.
2. AI-powered search engine assistants: Search engines integrated with AI functionality, such as Google Gemini and Microsoft Copilot on Bing, quickly generate the most relevant search results based on user queries.
3. Conversational business intelligence (BI) apps: These applications integrate with databases or data warehouses to retrieve necessary data for analysis, enabling users to interact with the app and receive outputs as data visualizations.
4. Customer support chatbots: Chatbots and virtual assistants with pre-programmed functionalities are integrated into company websites, helping businesses provide limited responses to customer inquiries.
5. Sentiment analysis tools: Conversational AI is used in sentiment analysis tools, enabling organizations to analyze customer reviews or comments to gather insights about satisfaction levels, neutrality, or dissatisfaction.
6. Text-to-speech and speech-to-text conversion: Conversational AI is also utilized in tools that convert text to speech and speech to text, facilitating voice-based interactions.

Key Benefits of Conversational AI

Timeliness and 24/7 Availability

One of the greatest advantages of conversational AI is its ability to provide instant responses, enabling businesses to enhance conversion rates for potential customers. Additionally, addressing inquiries promptly from the start helps reduce the risk of negative feedback and improves brand reputation.

Improved Customer Interaction

Customer behavior is increasingly shaped by technology. According to Gartner, 30% of customers expect live chat options on websites, and one-third are willing to replace phone calls with messaging. Conversational AI facilitates seamless communication between customers and businesses. More than just a “hotline,” this technology creates a natural interaction environment, boosting engagement, improving customer retention, and driving business growth.

Unlimited Scalability

Conversational AI can handle a large volume of user queries without the need to increase staffing. This is particularly beneficial when businesses face a sudden surge in customer requests. With its large-scale handling capabilities, businesses can easily maintain operations even during peak periods.

Challenges in Implementing Conversational AI

However, developers and organizations must consider common challenges associated with conversational AI systems, including:

• Language Translation: Many conversational AI models are primarily trained in English. Global companies aiming for multilingual chatbot support need to invest additional resources in this feature.
• Security: Companies interacting with customers via AI chatbots must implement robust measures to handle and store transmitted data securely.
• Interpreting Language and Context: Conversational AI may struggle with diverse human language variations, such as slang, technical terms, and local dialects. Developers are working to train models to address these challenges effectively.
• Biased Outputs: A conversational AI system may fail to understand certain accents or deliver biased responses if trained on insufficiently diverse datasets.

How Can Businesses Leverage Conversational AI?

Before developers begin building a conversational AI application, it is crucial to choose the right platforms and tools that align with their needs. Factors to consider include:

• Ease of deployment.
• Integration with existing processes, systems, and technologies.
• Affordability relative to budget and value provided.
• Scalability to meet future growth demands.
• Ensuring data security and compliance with regulations.
• Availability of reliable technical support from the provider.

Next, it is essential to plan and evaluate the scope and conversational structure that the Conversational AI tool must follow during user interactions. The process involves the following steps:

• List potential user inputs that may arise during interactions, based on frequently asked questions (FAQs) or data from previous customer interactions.
• Identify user objectives when interacting with the Conversational AI tool, using collected user data. It is crucial to consider the methods or expressions users may use to make requests, in order to define the scope and purpose of the tool, ensuring it can handle diverse inputs and accommodate various expressions.
• Determine specific requirements and goals for the tool to design a comprehensive conversational flow and future user experience.
• Create a prototype to test whether the concept functions as expected and identify potential issues.
• Guide and train the prototype through possible scenarios and user interactions to ensure the Conversational AI tool communicates smoothly and provides appropriate responses.
• Implement reinforcement learning to improve model accuracy over time.

Additionally, the platform on which the business is present should be decided based on customer preferences and the goals of the message being conveyed. For example:

• Handling complaints: Prioritize using Twitter and Facebook for their convenience and fast interaction.
• Providing updates and engaging customers: Integrate customized chatbots on websites or platforms like WhatsApp.
• Managing large volumes of time-sensitive queries: Deploy voicebots or in-app bots to address immediate needs efficiently.

Choosing the communication platform is similar to deciding between using Facebook or LinkedIn, depending on the target customer group of the business. Focus on where your customers are most active to optimize effectiveness.

Conversational AI vs Generative AI

Conversational AI and Generative AI are distinct concepts designed for different purposes, but they share certain similarities. For instance, ChatGPT is a Generative AI tool capable of creating articles, images, songs, and poems. At the same time, it is also an advanced chatbot that organizations can use for customer service.

Conversational AI

Conversational AI focuses on conversations powered by natural language processing (NLP) and machine learning (ML) with end-users. It is often used to obtain information or answers from an organization without the need to wait for a service agent at a support center. Such requests typically require open-ended conversations, making a conversational AI tool necessary for users.

The training data provided to conversational AI models differs from that used in generative AI models. Conversational AI training data may include human conversations to help the model better understand how typical dialogues unfold. This ensures the model recognizes various types of input, whether it is in text or speech format.

Generative AI

Generative AI focuses on creating content, including text, images, videos, and audio. For example, if a marketing team wants to produce a striking image for an advertisement, they can use a generative AI tool to perform a one-way interaction and receive the generated image.

The training process for generative AI models utilizes neural networks to identify patterns in their training data. This analysis, along with human guidance, enables generative models to improve the quality of the content they produce. The ultimate goal of generative AI is to deliver accurate and realistic outputs.

FPT.AI Honored as Global No.1 in “Conversational AI Software 2024” for the Second Consecutive Year

In 2024, FPT.AI, a product of FPT Smart Cloud, once again solidified its leadership position by being honored as the Best Conversational AI Software globally in the “Conversational AI Software” category by Software Reviews (a member of Info-Tech Research Group). Achieving an overall score of 9.0, with a perfect 100 points for product experience, this recognition underscores the exceptional quality of FPT.AI.

This global award was determined through surveys and votes from reputable organizations and businesses utilizing AI solutions in 2024. It serves as well-deserved recognition of FPT.AI’s efforts to accompany businesses in creating outstanding value.

Since 2013, FPT has invested heavily in AI research and development, integrating this technology into various aspects of life and achieving significant milestones. FPT.AI currently provides five main solution groups:

1. Conversational AI: Includes FPT AI Chat, FPT AI Engage, and FPT AI Enhance, supporting automated conversation building and management, leveraging NLP, and enhancing call center quality.
2. Operational Optimization: Includes FPT AI Read (data extraction and document digitization) and FPT AI eKYC (electronic customer identity verification) – applying advanced OCR technology, facial recognition, and liveness detection.

To date, FPT.AI has deployed over 3,125 chatbots and 3,200 virtual assistants, supporting 200 million interactions monthly. These solutions have helped businesses boost productivity by 67% and cut costs by 40%.

Mr. Le Hong Viet, CEO of FPT Smart Cloud, shared:

“Every product and service of FPT.AI stands out with its exceptional application of AI and Generative AI. We are committed to continuous innovation to deliver value to the community, helping businesses enhance efficiency and positioning Vietnam as a leading technology nation.”

The award from Software Reviews not only acknowledges the superior quality of FPT.AI but also highlights the potential of Make-in-Vietnam technology products on the global stage. This accolade serves as motivation for the FPT.AI team to continue pioneering the application of cutting-edge technology, developing the Next GenAI platform, creating breakthrough value for businesses, and positioning Vietnam as a central hub for AI in the region and the world.

In summary, Conversational AI and Generative AI are steadily affirming their crucial roles in advancing technology and modern businesses. By optimizing communication, automating processes, and enhancing user experiences, these technologies not only bring practical benefits but also pave the way for innovation in the future.

We hope this article from FPT.AI has provided you with a clear understanding of the essence, benefits, and effective applications of Conversational AI. Contact us today to experience exceptional integrated Conversational AI solutions and create a unique competitive edge for your business.

Reference: TechTarget. (n.d.). What is Conversational AI?. Retrieved January 20, 2025, from https://www.techtarget.com/searchenterpriseai/definition/conversational-AI