Conversational AI vs generative AI - what sets them apart?

In the world of AI (artificial intelligence), there are two main categories: conversational AI and generative AI. While these two types of AI tools often work together, they have distinct purposes and use cases that are imperative for those that work in technology to understand in order to find the ideal tools for their unique needs. 

So let’s explore the similarities and differences between conversational AI and generative AI and understand how they function in different IT tools.

What is conversational AI?

In short, conversational AI is a tool that helps machines communicate with humans in a more natural way. NLP (natural language processing) is an important feature of conversational AI. It is a machine-learning technology that allows computers to interpret, understand, and create human linguistic patterns. Interacting with a conversational AI program should feel like a back-and-forth, reciprocal conversation. 

Conversational AI has plenty of use cases in the world of IT. These programs are trained on data sets with human dialogue, which helps them understand human language patterns. They also draw information from company-specific knowledge bases in order to answer questions. Strong AI tools for IT, like Atera’s AI Copilot, engage in ongoing learning and training by adding solutions to a knowledge base for future reference. 

Common use cases for conversational AI include messaging apps, chatbots, and virtual assistants. You have likely come across popular conversational AI tools in your daily life—think of Alexa, Google Assistant, or Apple’s Siri. 

What is generative AI? 

Generative AI uses more advanced learning tools to identify patterns and structures in its training data. Unlike conversational AI, generative AI can create brand-new content based on its predictions from the patterns it has learned. When you compare conversational and generative AI, the latter is able to generate more accurate content through human response and feedback that allow it to hone its learning and skill set. 

Generative AI plays an important role in the digital transformation process, which refers to the comprehensive integration of digital technology into every aspect of a business. Generative AI tools allow businesses to run more streamlined and efficient operations, enhance customer experiences, and free up IT technicians from humdrum busy work. 

In the world of IT, popular use cases for generative AI tools include code generation, process automation, predictive analytics, ticketing and help desk assistance, and so much more. Take Atera’s Action AI^TM tech, for instance. It’s been shown to reduce ticket response times by 10X and can even handle common issues without the need for human intervention. Atera’she AI Copilot—powered by Action AI^TM— diagnoses issues, offers solutions, and generates and executes scripts. And it can even provide ticket summaries for future learning. 

Other popular generative AI applications that you may have come across in your everyday life include ChatGPT, Google’s Gemini, and Jasper AI. There are countless applications for generative AI tools across many industries, including healthcare IT and education IT solutions. 

Generative AI vs. conversational AI

As we compare generative AI and conversational AI, we can break down the differences between these two types of artificial general intelligence into a few different categories. Take a look at the table that follows to get a better sense of how generative AI and conversational AI stack up. 

Comparing conversational and generative AI

While they do have different goals and applications, generative AI and conversational AI are not mutually exclusive. In fact, you could argue that conversational AI is a specific subset of generative AI applications. 

Generative AI encompasses a broader set of tasks beyond conversational interaction—but often, conversational AI is a necessary component of a truly effective AI tool, as it allows both end-users and IT techs to interact with the product without relying on coding knowledge. 

Conversational AI and generative AI in IT

Conversational AI is typically associated with industries like healthcare, finance, and even e-commerce—industries where personal assistance is needed for customers. On the other hand, generative AI is often associated with more creative domains. Think of content creation, entertainment, and design. 

In the world of IT, both realms are relevan—the creative, productive realm and the customer-facing realm. In that sense, conversational AI and generative AI can work in tandem in the world of IT. So what does that look like? 

Large language models (LLMs)

Large language models, or LLMs, are an excellent example of the combined forces of conversational AI and generative AI. They have played an important role in bringing generative AI to the forefront of AI-powered IT by enhancing natural language understanding (NLU) and natural language processing (NLP) capabilities without forgoing their creative prowess. 

In addition to understanding and responding to text like a human, LLMs can also create content—and that content includes things ranging from music to creative writing to computer code to niche requirements like writing PowerShell scripts. There are numerous applications for LLMs in the world of IT, including: 

• Helpdesk and ticketing, including end-user ticket resolution
• Knowledge base article creation
• Script generation
• Remote session summarization
• Command-line generation
• Real-time troubleshooting
• EDR and XDR security capabilities
• Ticket summarization
• Process automation
• And so much more 

So how can you integrate LLMs, conversational AI, generative AI, or both into your organization’s IT processes? That answer is easy:it all starts with Atera. 

Atera’s Comprehensive AI tools for IT

At Atera, we’re the only comprehensive IT management software that takes AI-powered IT to the next level. Our AI Copilot tool has all of the capabilities listed above,and then some. With advanced reporting, predictive analytics, and next-level process automation, you can save an average of 11-13 hours per week and reallocate the time of your IT technicians to focus on more complex, pressing tasks. 

Our tools include features like RMM (remote monitoring and management), automated patch management, helpdesk and PSA, and so much more. The AI Copilot has revolutionized the way that IT departments deal with support tickets.  Incorporating the AI Copilot enables IT teams to slash resolution times and drastically improve user experience, both within the IT department and outside of it. To explore more of Atera’s innovative IT solutions powered by world-class AI, take our platform for a test drive with our 30-day free trial. Check it out right here.