Conversational AI vs. Generative AI: What Is the Difference?

Intro

No technology trend promises more potential for enterprise organizations looking to scale than Artificial Intelligence (AI). As a rapidly growing field that can transform internal and external processes, AI is a revolutionary technology, but only if it is implemented strategically to support enterprise businesses' complex processes and IT landscape sustainably over time. 

Two of the most common ‘types’ of AI are Conversational AI (CAI), which allows computers to interpret human language, and Generative AI (GenAI), which generates content such as text or imagery based on user input. 

Though this article’s title implies competition between the two, the truth is that CAI and GenAI are best utilized together to create a powerful AI Agent. 

To understand why they’re best used together, let’s examine each type of AI, how they can be used most effectively, and what combining them into a complete AI Agent can offer an enterprise organization.

What Is Conversational AI?

Conversational AI is a type of AI that focuses on understanding and interpreting human language. It uses Natural Language Understanding (NLU), Machine Learning (ML), and a series of pre-programmed rules and logic flows to allow computers to communicate with humans in an engaging, lifelike way.

CAI is also an action-oriented AI. It is integrated with backend systems and can perform tasks like updating customer details in a CRM system. This means it can take a frontline role in your customer processes, communicating directly with users, identifying their needs, and then taking the appropriate action (which it can either complete autonomously or, if the task is outside of the AI’s ability, route to a human agent). 

What Is Generative AI?

GenAI is an AI that uses machine learning models to identify patterns in training data. It uses neural networks to analyze enormous swathes of data, recognize patterns within them, and understand the connections, which it can then use to generate content. 

For text-based applications, GenAI leverages Large Language Models (LLMs) – a specific type of model trained on a vast number of books, articles, websites, and other text sources. The LLM doesn’t just read the words – it learns to understand the relationships and rules behind them. This, in turn, allows GenAI to predict the most logical next word in any given sentence. 

Once given a prompt, Generative AI uses its learning to interpret the request and generate the most relevant response—whether answering a specific question, writing an article, or even summarizing existing content. 

LLMs can also be used to boost the performance of Conversational AI, making interactions more natural and dynamic, while still following clearly defined processes. Learn more about this in our guide to LLMs & Conversational AI. 

What Is the Difference Between Conversational AI and Generative AI?

Although both technologies are rarely used in isolation, understanding the differences remains important for anyone considering an AI Agent platform for their organization. 

In basic terms, the main difference between Conversational AI and Generative AI is the purpose. Where CAI is about understanding and communication, GenAI is about prediction and creation. 

Let’s compare the two more closely with a quick rundown of their key elements: 

What Are the Use Cases of Conversational AI?

Conversational AI’s ability to understand language means many of its use cases revolve around customer-facing applications. By giving machines a way to interpret language, CAI can act as a frontline support agent who can communicate directly with customers in an instant, fluid way, while adhering to the rules and flows demanded by your enterprise.

Some compelling use cases include: 

Identification & Verification (ID&V)

Almost every customer service call includes an identification stage, during which a customer must input verifying information before proceeding. Conversational AI can automate this process, improving speed and efficiency for both you and your customer. 

Thanks to its interaction with your backend systems, CAI can ask a customer for the necessary details and then check them in your CRM. Then, it can either perform a handover to the appropriate human agent or ask for further information. 

Automated ID&V reduces call times, lowers user frustration, and can cut down on hold queues – making it one of the most immediately impactful use cases you can implement in your organization. 

Customer Support Agent

Similarly to ID&V, an AI Agent that harnesses Conversational AI can act as a frontline customer support agent. Conversational AI can engage with customers by asking relevant questions to determine their issue, then take action to resolve it or pass it to a human team member. 

The AI Agent can take various actions, such as fetching the status of an order based on a customer’s order number or updating personal details in their CRM entry. It can even use Generative AI to create unique content that answers more complex customer queries. 

Utilized in this way, an AI Customer Support Agent helps alleviate the immediate demand on your human agents and allows you to deal with high volumes of service queries without compromising on quality. 

Whenever the AI detects a customer’s negative sentiment or their query becomes too complex, it can intelligently route the customer to the most appropriate human agent and provide a complete log of the conversation so far. This allows your human agents to focus on complex cases and never be caught off guard. 

Multilingual, Omnichannel 24/7 Chat Agent

For an enterprise business to scale, you need to be able to cater to a wide range of customer needs. Conversational AI allows you to quickly implement multilingual support across multiple channels, meaning customers can speak in their native language and use whichever channel they prefer. It is also not tied to a specific timezone or shift pattern, so it can be made available 24/7. 

AI agents powered by Natural Language Understanding (NLU) can understand and respond in a customer’s preferred language. This makes every customer interaction more fluid and familiar for customers, who will be more comfortable speaking in their own language. The AI Agent will continue to learn as it works, adapting to regional dialects and colloquialisms to better serve users over time. 

But it’s not just language skills that make Conversational AI so versatile – it’s also the omnichannel capabilities it brings to your support team. Customers can talk to your AI Agent via whatever channel they prefer, such as voice chat, SMS messaging, WhatsApp, web chat, and more. 

These things combine to provide a far more customer-friendly service experience. Your users can contact service teams at a time that suits them, in their chosen language, and using whatever device and channel they prefer. 

What Are the Use Cases of Generative AI?

Generative AI is transforming organizations by creating content instantly. This content can be utilized in many different ways, whether to fill out your FAQ section or create live, dynamic content directly in response to a live customer support query. Its ability to mimic human creativity and adapt to complex enterprise needs makes it a critical technology for businesses looking to innovate and scale.

Content Creation and Personalization

Generative AI allows customer service teams to create personalized content that enhances customer interactions. When combined with Conversational AI, Generative AI can produce accurate, engaging information across all of your channels and can even create content in real-time interactions. 

During a call, Generative AI can produce responses that adapt to the customer's tone, preferences, and inquiry history. For enterprises, this level of customization not only increases customer satisfaction but also strengthens brand loyalty. 

AI-generated FAQs, dynamic help guides, and context-aware chatbot conversations ensure that customers feel valued and understood. By automating these processes, businesses can scale their support operations without compromising the quality of service.

Data Analysis and Insight Generation

Generative AI isn’t all about producing new content. It can also play a key role in analysing data and creating actionable insights. AI Agents that harness GenAI can analyze vast amounts of customer data, uncovering patterns and trends such as commonly recurring issues, frequently asked questions, pain points, and more. 

Not only can the AI Agent gather these insights, it can also suggest solutions and help produce them. For example, it could identify a common support query across thousands of call logs and then write a detailed guide that addresses customer needs. 

Additionally, AI can generate detailed summaries and reports from customer interactions, helping managers track performance, sentiment trends, and key metrics. For enterprises managing large volumes of customer inquiries, these insights are invaluable for optimizing processes, predicting demand, and ensuring consistent quality of service.

Agent Support

AI Agents don’t just improve business performance – they also help support your human agents via Agent Copilot. 

For your human agents, creating call summaries at the end of every call is one of the most dull and demotivating activities in a contact center. It is a necessary task, but one that hampers productivity and directly eats into your costs. Any automation that can help streamline call wrap-ups will have a clear positive impact on your ROI. 

An AI Agent automatically transcribes a call as it happens, but it can also take that transcript and create a summary that suits whatever format you require. Generative AI produces these summaries almost instantly, meaning your human agents can simply review the content and make any quick tweaks required before moving on to the next case. 

GenAI can also support mid-call in other intuitive ways, such as generating FAQ answers during a customer’s call and displaying it directly to your human agent so they don’t have to waste time finding the answer themselves and can quickly resolve the query.

Why Conversational AI & Generative AI Work Best Together

Why Invest in Conversational AI or Generative AI?

Enterprise organizations constantly battle scaling and costs. As customer demand increases, how can they offer satisfactory experiences without compromising on quality? 

Only through the adoption of sophisticated AI Agents that utilize both Conversational AI and Generative AI can a business meet demand in a cost-efficient way. With both GenAI and Conversational AI acting in collaboration within your enterprise, you can benefit from:

• Instant customer support: AI Agents can answer calls with no waiting times and begin fielding cases – either to take care of the initial stages of a call before passing it to a human agent, or directly resolving the issue by taking actions such as booking a flight, checking an order, updating details etc. 
• Multi-lingual, omnichannel capabilities: Conversational AI provides live translation for 100’s of languages without you needing to invest in translators or offshored language centers. Better yet, because the AI Agent is not tied to a specific platform, you can provide omnichannel services that users can interact with via text, voice, social media, web chat, and more. 
• Intuitive self-service: One of the best ways to reduce the burden of customer support queries is to provide self-service options for users. AI Agents provide an intuitive means of self-service, and Conversational AI can interpret requests and act on them. 
• Improved human agent productivity: Another clear reason to invest in AI is that it improves human agents' productivity. By automating many of the more laborious tasks associated with customer service and providing live assistance during calls, AI Agents make human workers happier and more efficient. 
• Decreased costs: All of the benefits above combine to result in the most compelling reason for enterprise businesses to invest in AI – the cost savings you can unlock through intelligent automation. With an AI Agent platform, you can save hundreds or thousands of hours in a contact center environment, which translates directly to cost savings that are otherwise impossible with standard service teams.

The Future of Conversational AI and Generative AI

One of the most significant transformations in the new world of AI is the development of Agentic AI. This goal-focused system allows AI Agents to reason dynamically through tasks without the need for strict conversational journey mapping. Agentic AI can navigate the natural variations in conversations, including interruptions and tangents that cannot be pre-mapped, while still achieving the same end goal. 

With Agentic AI, the last barrier between automation and customer service needs has been removed. Where users are willing to interact with automated systems, they only want to do so if the tool can provide accurate answers, human-like conversations, and a sense of trust and privacy. 

Though both Conversational AI provide the language skills needed to help achieve this, it is by incorporating Agentic AI that an AI Agent becomes a truly helpful digital service that can engage in frontline customer support without the need for human supervision or intervention. 

As more and more enterprises unlock the benefits of AI and begin to automate, customers will become more familiar with AI systems and more comfortable utilizing them for self-service tasks. As attitudes change, you can’t afford to be left behind with slow service, long wait times, and expensive staffing costs. 

Cognigy Conversational AI Solutions

Cognigy specializes in AI Agents built for enterprise organizations. We work with some of the world’s biggest brands and can provide expert, tailored support to help you handle millions of customer support queries with efficient automated solutions.

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