Have you ever been stuck on a customer service call, waiting endlessly to get through to an agent? In today’s digital whirlwind, time is gold, and endless hold times simply aren’t an option. This is where AI comes into play to speed up and enhance processes, specifically Conversational AI and Generative AI.

The digital landscape is evolving at breakneck speed. Future implications are already underway: for example, Gartner predicts that by 2027, chatbots will become the main source of communication for customer service channels.

Moreover, AI is at the forefront of the technological revolution, making it difficult to keep up with these rapid changes. This blog aims to explore the nuances between Conversational AI and Generative AI with respect to:

• Technology and functionality
• Real-world applications and use cases
• Implications in a business context

Understanding Conversational AI and Generative AI

Artificial Intelligence continues to revolutionize industries, from self-driving cars to facial recognition technologies. But at the core of daily interactions are two pivotal forms of AI: Conversational AI and Generative AI.

What is Conversational AI?

Conversational AI is a type of artificial intelligence that enables machines to understand and respond to human language. Think of Conversational AI as your go-to virtual assistants—Siri, Alexa, and Google Assistant. These technologies use Natural Language Processing (NLP) to understand human language and reply in a way that is as human-like as possible.

An IBM article underscores the role of Conversational AI in crafting distinctive customer experiences that can set a company apart from its competitors (IBM on Forbes). Increased efficiency and cost savings are also some stand-out benefits of this technology.

Conversational AI in business is mainly used to automate customer interactions and conversations. An example is customer service Chatbots that can provide instant responses to common queries, freeing up human customer service agents to handle more complex issues.

What is Generative AI?

Generative AI is designed to create new and original content—be it text, images, or music. Generative AI works by using deep learning algorithms to analyze patterns in data, and then generating new content based on those patterns. Here’s a detailed explanation on how Generative AI works.

Benefits of Generative AI include increased creativity and productivity, as well as the potential for new forms of art and entertainment. For example, a generative music composition tool can create unique and original pieces of music based on a user’s preferences and inputs. ChatGPT is also a great example of Generative AI.

Exploring the differences between Conversational AI and Generative AI

Both types of AI transform how we interact with digital systems, yet they serve different purposes. Let’s delve into these differences one at a time: purposes, focuses, methods of training, inputs, and outputs.

Purpose

The main purpose of Conversational AI to facilitate communication between humans and machines. Hence, Conversational AI needs to be adept at understanding the context, situation, and underlying emotion behind any conversation, and reply appropriately.

The purpose of Generative AI is to generate new content in different forms, e.g. text, images, or music. Based on the instructions and preferences given by the human user, it creates new and original content in different kinds of media.

Focus

Conversational AI is designed to be as realistic, human-like, and as reliable as possible in its responses. This is because it is mainly used in business or customer-facing scenarios. The inability to engage customers or give incorrect information to clients would negatively impact the business.

Generative AI, on the other hand, is designed to be creative and generate original content. Being truthful and reliable is not as important, and content generated through Generative AI requires human checking. For example, ChatGPT has a disclaimer, “ChatGPT can make mistakes. Check important info.”

Training

Generative AI studies massive datasets from the web, just like a highly trained artist analyzing countless books and paintings. It uses this knowledge to create entirely new things, from composing music to writing stories.

Conversational AI, on the other hand, focuses on the art of conversation. Trained on real interactions within a specific field, it learns to understand the back-and-forth of dialogue and respond accordingly. Think of it as a skilled interpreter, able to navigate the nuances of human conversation within a particular context.

Input/Output

Conversational AI takes in human language as input and generates human-like responses as output, while Generative AI takes in data or inputs and generates new content as output.

 

Real-world applications and use cases of Generative AI and Conversational AI

What better way to understand the differences between the two technologies than how they are used in the real world? Let’s take a quick dive.

Conversational AI applications

• Customer service: Conversational AI is widely used in customer service applications to provide quick and efficient responses to common queries. This can free up human customer service agents to focus on more complex issues and improve overall customer satisfaction.
• Personal assistants: Conversational AI is also used in personal assistant applications such as Siri, Alexa, and Google Assistant. These applications use natural language processing to understand and respond to user requests, such as setting reminders, playing music, or providing information on the weather.
• Language translation: Conversational AI can be used to translate languages in real-time, allowing for seamless communication between people who speak different languages.
• Education: Conversational AI can be used in educational applications to provide personalized learning experiences for students. For example, a conversational AI tutor can adapt to a student’s learning style and pace, providing real-time feedback and assistance.

Generative AI applications

• Art and design: Generative AI is often used in the creation of art and design, such as digital art, fashion design, and architecture. For example, a generative design tool can create unique and original designs based on specific parameters and inputs.
• Music and sound: Generative AI is also used in the creation of music and sound. For example, a generative music composition tool can create original pieces of music based on a user’s preferences and inputs.
• Content creation: Generative AI can be used to create a wide range of content types, including text, images, and videos. For example, a generative text tool can write news articles or product descriptions, while a generative image tool can create unique and original images based on specific inputs.

Comparing Generative AI and Conversational AI in Business Contexts

When evaluating which AI tool best suits their needs, businesses should consider key operational features such as scalability, cost-effectiveness, and user engagement. The following table highlights the strengths and limitations, helping organizations make informed decisions based on their specific requirements.

Comparison Table: Generative AI vs. Conversational AI

Feature	Generative AI	Conversational AI
Scalability	High scalability due to advanced algorithms	High scalability and tailored towards business
Cost-effectiveness	High costs and resources to set up, but potential long-term savings	Generally cost-effective with predictable expenses
User Engagement	Highly engaging with dynamic responses	Consistent and reliable user interactions
Data Privacy	Concerns over data privacy and security	Strong data privacy controls
Traceability	Limited traceability in decision-making processes	Clear traceability and audit trails
Customization	Highly customizable content generation	Customizable yet structured responses
Reliability	Issues with hallucination and factual accuracy	Highly reliable and accurate responses

To learn more on how to calculate the ROI on the tool of your choice, see How to Evaluate ROI on Conversational AI When You Don’t Have a Technical Background? You can apply this knowledge to Generative AI as well.

Can Generative AI be Used in Business?

We know that Conversational AI is specifically designed for businesses to automate interactions with their customers. But what about Generative AI?

Generative AI offers numerous innovative applications in business, from content creation to personalized marketing. However, despite all the amazing use cases, when it comes to providing great customer-facing solutions, Generative AI isn’t the answer. According to a report by BCG of 2,000 global executives, more than 50% still discourage GenAI adoption. Problems of hallucination, limited traceability, and compromised data privacy are just some of the major concerns they have.

Generative AI, by itself, is not fit for business. But it can be used to automate customer interactions, by taking a specific approach that mitigates the risks of using Generative AI.

The Tars Approach: Combining Reliability and Flexibility

Tars offers a unique approach that combines the reliability of structured chatbots with the flexibility of Generative AI. By integrating robust, rule-based responses with the creative and adaptive capabilities of generative models, Tars provides businesses with a balanced solution.

This ensures consistent, accurate, and engaging user interactions while maintaining high standards of data privacy and operational transparency. This hybrid offers an optimized tool for business communication and customer service.

Conclusion

As AI gets more powerful, businesses will be able to use these amazing tools to streamline their work and make customers rave about their experiences— and this is just the beginning.

AI is constantly learning and evolving, and in the future, it will be seamlessly working alongside humans in the corporate landscape. But in today’s dynamic environment, Tars Converse AI stands out as a cutting-edge solution.

Introducing Tars Converse AI Agents

At Tars, we’ve been in the Conversational AI landscape for over 8 years, with the goal to help businesses automate customer interactions.

Tars Converse AI gives you a dedicated AI Agent that:

• Automates Q&A and answers repetitive FAQs
• Personalizes pre-and-post-sales support
• Learns from your unique knowledge base to deliver insightful resolutions, just like a human expert.

By combining a structured approach with Retrieval-Augmented Generation (RAG) architecture and the capabilities of OpenAI, Tars Converse AI optimizes customer journeys from start to end. These AI Agents are custom-built for each business’s specific needs.

To learn more about how Tars Converse AI can transform your business, explore their features here.