Top Differences Between Conversational AI vs Generative AI in '24
- 80% of CEOs have changed or want to change client engagement with conversational AI
- The global generative AI market will reach $111B by 2030
At first glance, these tools seem similar to each other. For instance, both conversational AI and generative AI models can generate answers, but how they do that differs. Therefore, we should carefully study conversational AI and generative AI’s distinct features.
Given that 60%1 of organizations are concurrently implementing four or more hyperautomation initiatives, not fully understanding the differences and similarities of the tools you’re investing in restricts your returns.
In this article, we will:
- Explain the differences between conversational AI vs generative AI
- Discuss how they can be jointly used
- Provide a list of tools that leverage both conversational AI and generative AI
What is conversational AI?
Conversational AI (or conversational artificial intelligence) uses:
- Training data
- Intent understanding
- Speech recognition
- Natural language processing (NLP)
- Natural language understanding (NLU)
To create intelligent systems, such as chatbots, voice bots, and intelligent assistants, capable of engaging in natural language conversations and providing human like responses. This versatility means conversational AI has numerous use cases across industries and business functionalities.
What is generative AI?
Generative AI focuses on creative content generation, as its primary function. It can generate output across 70+ applications in text, image, music, or video format, thanks to:
- Data training on wider and deeper datasets
- Deep learning models and machine learning algorithms
- Generative adversarial networks (GANs)
- Neural networks
What are the differences between conversational AI vs generative AI?
Conversational AI systems are generally trained on smaller datasets of dialogues and conversations to understand user inputs, process them, and generate responses in text/voice. Therefore, output generation is a byproduct of their main purpose, which is facilitating interactive communications between machines and humans.
Generative AI tools, on the other hand, are built for creating original output by learning from data patterns. So unlike conversational AI engines, their primary function is original content generation.
So generative AI is a more flexible tool by creating content in different formats, whereas conversational AI tools can only communicate with users.
How can conversational AI and generative AI be used together?
- Conversational AI capabilities for language understanding
- Generative AI capabilities for original output generation
We get a conversational AI chatbot with generative AI capabilities, trained on trillions of data and topics, understands your questions and generates responses as text, video, music, or picture.
What are some tools using both conversational and generative AI features?
We created an alphabetical list of 5 tools that leverage both conversational AI and generative AI capabilities. Note that not all of these intelligent chatbots serve identical use cases.
1. Bing Chat
Bing Chat can:
- Generate images by using DALL-E
- Answer questions by searching the web
- Give exact sources for its answers, thus being more usable for researchers and academics
ChatGPT is arguably the most popular tool on our list. It can:
- Answer questions, by using its training, in different languages
- Give suggestions and recommendations
- Write essays and songs
- Generate and debug codes
- Integrate with other applications with plugins
Character.ai is a novelty tool in beta mode that allows you to talk with historical and prominent figures, as well as fictional characters. However, Character.ai could still be used in:
- Education: Teaching history or culture through storytelling and dialogue.
- Marketing: Generating catchy slogans, headlines, or descriptions for their products or services through the mouth of a historical or fictional character.
Jasper.ai, with its flagship AI-writing tool, is more tailored towards writers, copywriters, bloggers, and students. But it also has a chat feature, similar to other tools on our list, for back and forth communication.
For more on conversational AI and generative AI
To learn more about conversational AI and generative AI, read:
- 10 GAN Use Cases
- ChatGPT WhatsApp Integration for Businesses
- Top 25 Chatbot Case Studies & Success Stories
And if you are looking to invest in one these solutions, visit our data-driven lists of:
If you have more questions, don’t hesitate to contact us:
This article was originally written by former AIMultiple industry analyst Bardia Eshghi and reviewed by Cem Dilmegani.
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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