Conversational AI vs generative AI are two terms that are frequently used in discussions about artificial intelligence (AI). Although they both fall under the umbrella of artificial intelligence technologies, their functions and modes of operation are distinct. Let’s examine each idea in more detail so you can see how it differs and can be used.
Conversational AI vs Generative AI | Definition
What is conversational AI?
Conversational AI is a technology that facilitates more natural interactions and interactions between machines and humans. The conversations are similar to back-and-forth dialogue during a debate. This technology is used by chatbots, messaging apps, and virtual assistants. Three widely used conversational AI apps are Alexa, Google Assistant, and Siri.
What is generative AI?
Generative AI uses machine learning algorithms and the data it has been trained on to allow users to create new content, including animation, text, images, and sounds. Neural networks and deep learning are used by generative AI to produce results. Popular applications using generative AI are ChatGPT, Google Bard, and Jasper AI.
Real-life Applications of Conversational AI
- Customer support chatbots: These AI-powered chatbots are made to answer a variety of consumer questions, from straightforward FAQs to trickier problems. Their ability to function around the clock guarantees that client concerns are promptly resolved.
- Voice assistants: Conversational AI is used by apps like Siri, Google Assistant, and Alexa to comprehend and react to user commands. In addition to playing music and updating the weather, they can set reminders.
- Social media interactions: As social media platforms such as Facebook Messenger, WhatsApp, and Instagram grow in popularity, businesses are turning to conversational AI to engage with customers. These artificial intelligence tools can help with purchases, make recommendations, and answer product-related questions.
- Virtual agents for contact centers: By managing routine questions, conversational AI can simplify contact center operations and free up human agents to work on more difficult problems.
- Personalized marketing: Conversational AI can improve users’ shopping experiences by offering tailored product recommendations and promotional offers based on analysis of user interactions. For instance, Yellow.ai’s Engage assists retailers in creating digital campaigns driven by Generative AI that enhance lead generation by 20% and raise revenue per campaign by 15%.
Real-Life Applications of Generative AI
- Content creation: Generative AI is capable of producing a wide range of content, including music (Musenet), videos (Runway), text (ChatGPT, Google Bard), and images (DALL-E, Midjourney). This is especially helpful for creative industries, as AI can help with idea generation and first draft generation.
- Enhanced customer interactions: Through improved chat and search experiences, generative AI can enhance customer interactions. In addition to producing content according to user preferences, it can respond in a more human-like way.
- Data exploration: Large volumes of unstructured data can be explored by generative AI, which can then provide insights through conversational interfaces and summaries. Businesses hoping to extract useful insights from their data will particularly benefit from this.
- Repetitive tasks automation: Generative AI can be used to automate tasks like responding to requests for proposals (RFPs), translating marketing content into multiple languages, and verifying that customer contracts are compliant.
- Innovations in various fields: Applications for generative AI can be found in material science research, drug and chip design, and other fields. It’s a useful tool for research and development because of its capacity to produce original solutions based on available data.
How does Conversational AI work?
To better understand linguistic patterns, conversational AI models are trained on datasets containing human dialogue. To generate relevant answers to questions, they convert human conversations into languages that computers can understand using natural language processing and machine learning technologies.
Every business has its knowledge bases from which conversational AI applications derive their responses. With every interaction, business AI software consistently trains, gaining new knowledge from the interactions and adding it to the knowledge database. These knowledge bases are also updated by humans.
Predefined responses, or rule-based systems, are another option for conversational AI’s initial replies.
How does Generative AI work?
Neural networks are used by generative AI to find patterns and other structures in training data. After that, it creates new content by making predictions using the patterns it has learned. Generative AI can be trained using a variety of methods, including supervised learning, which makes use of feedback and human response to produce more accurate content.
Companies can build foundation models to serve as the AI systems’ starting point for a variety of tasks. AI neural networks or machine learning models that have been trained on vast amounts of data are known as foundation models. Because of their versatility and generality, they are capable of handling a wide range of tasks, including text translation, content production, and image analysis. Foundation models comprise, for instance, GPT-4 and PaLM 2.
Conversational AI vs Generative AI: Why Not Both?
While conversational and generative AI offer distinct advantages, they can complement each other to enhance the customer experience. Although they are categorized as generative AI models, tools like Microsoft Copilot for Sales also employ conversational AI.
Contact centers can integrate conversational and generative AI in several ways. For example, by utilizing the insights of back-end generative models, conversational AI bots can produce more insightful responses to customer inquiries.
To assist with customer support, intelligent conversational assistants can evaluate incoming ticket data and allocate problems to specialized generative models. Even during conversations, conversational bots can extract useful information from generative AI-generated knowledge bases and FAQs.
In the end, combining generative and conversational AI enhances the benefits of each approach. While conversational AI bots are capable of managing routine, high-volume interactions in contact centers, generative algorithm-powered solutions are better suited to handle more complicated inquiries and provide agents with extra assistance.
It’s likely that as these technologies develop further, CCaaS and contact center industry leaders will release more tools—like Five9’s generative AI studio—that let users create custom systems that combine the best features of both approaches.
Conclusion
Two types of artificial intelligence capabilities are available: conversational AI vs generative AI. Generative AI produces original content, whereas Conversational AI concentrates on natural interactions. Optimizing both improves operational effectiveness and customer experiences. More integration is probably in store for future developments, which will lead to more complex and adaptable AI solutions.
