The Difference Between Generative AI and Conversational AI

What’s the difference between conversational AI and generative AI and which solution is right for your business? There has been a meteoric rise of LLMs (large language models) and generative AI in the past couple of years.

In the meantime, machine learning algorithms, natural language processing solutions, and neural networks are becoming increasingly sophisticated. Business leaders, contact centres, and modern teams should pay attention to two flavors of artificial intelligence in this vast landscape: generative AI and conversational AI.

Understanding the key differences between these two solutions will ensure you’re investing in cutting-edge technology that’s right for your business, even though they might work together.

Let’s examine conversational AI and generative AI in more detail.

The conversational AI subset of artificial intelligence allows bots and computers to mimic human conversation and understand user input. This is a tool that allows people to interact with machines as though they were speaking to another person (without writing code).

You’ve probably encountered conversational AI in a chatbot on a website, a voice bot in an IVR system, or a self-help tool like Slackbot.

Natural language processing is used to interpret human input. Databases are also used to determine how to respond to a user via natural language generation. However, some bots are more advanced than others.

In addition, some solutions can draw insights from customer profiles and CRM systems to personalize the user experience based on the tone of voice or the words used by the user.

AI tools that simulate human interactions cannot create unique responses, as they are trained on massive datasets and insights into human dialogue, and they draw their responses from a predefined pool.

Conversational AI has a significant impact on customer experience. It can enhance virtually every customer-facing operation, from answering customer questions to troubleshooting product problems.

Several contact centers integrate conversational AI tools into their platforms, which can help:It can help sales professionals gather and qualify leads, analyze market trends, and even recommend products to customers, as well as empower them to complete transactions on their own.

Marketing teams: Conversational AI tools provide insights into customer trends, preferences, and journeys. They can also dynamically share marketing content with customers.

Conversational AI solutions can also provide valuable insights into customer needs, preferences, and trends. In the customer service landscape, these solutions allow teams to offer 24/7 service to customers in the language of their choice.

Various industries have benefited from conversational AI, including retail, banking, and healthcare. In retail, for example, it can improve 24/7 order processing and customer engagement, in banking, it can simplify transactional tasks, and in healthcare, it can streamline patient care.

Conversational AI can also be found in smart speakers and personal assistants. Apps like Siri, Alexa, and Google Assistant all use conversational AI algorithms.

The pros are:

• Enhances and delivers omnichannel customer service well.
• Provides 24/7 customer support.
• Enhances productivity and efficiency of teams to save money.
• Guides business decisions with valuable insights.
• Provides continuous support and guidance to enhance team performance.

The cons are:

• The ability to process complex queries (without regular training) is limited.
• Specific linguistic nuances are difficult to understand.
• Data compliance risks.

t’s important to note that both generative AI and conversational AI have pros and cons. Conversational AI can empower teams to deliver exceptional customer service 24/7 across any channel.

Besides improving operational efficiency, it can improve agent productivity by automating routine and recurrent tasks (like summarizing and transcribing text). Additionally, conversational AI tools can help businesses make intelligent decisions and optimise workplace processes by giving them insights they need.

It is also true that there can be challenges. For instance, most conversational AI solutions are capable of handling routine requests, but struggle with complex queries. In order to deal with more complex inquiries, conversational AI tools need constant training and fine-tuning.

Customer experiences can be hampered by solutions that don’t understand finer linguistic nuances, such as satire, humour, or accents. Moreover, conversational tools interact with customer data, so compliance with data privacy regulations is always a risk, just like most forms of artificial intelligence

AI that uses deep learning and neural networks to create new content, such as text, images, and sounds, is known as generative AI. Some of these tools can create content based on the prompts you give, and some respond to text, video, audio, and images in a multimodal manner.

There is a confusion here between generative AI and conversational AI. In fact, generative AI is often referred to as an updated version of conversational AI. In fact, natural language processing and generation tools are still used by apps like ChatGPT and Microsoft Copilot to facilitate human-bot interactions.

Generated AI, unlike conversational AI, can create original content rather than just responding to questions based on what it finds in its database.

Neural networks are used by generative AI tools to identify patterns and structures in their training data, then generate new content from those patterns. As an example, Microsoft Copilot will generate a list of dates for your next team meeting based on your meeting habits, schedules, and shared calendars.

The pros are:

• Enhances employee creativity and productivity.
• Strengthens team connections and enhances collaboration.
• Analyzes growth opportunities and provides actionable insights.
• Provides personalized 24/7 service to transform the customer experience.
• Produces original, unique content in a variety of formats.

The cons are:

• Ethics and transparency issues (collection and use of data).
• Copyright and IP infringement risks.
• Incorrect or biased responses are caused by AI hallucinations.

Just like conversational AI, generative AI has pros and cons. For most professionals, the best advantage of this type of intelligence is that it enhances creativity and productivity. Using these tools, teams can generate original ideas and content that inspires and motivates them.

Additionally, it can enhance collaboration by summarizing meetings in seconds, providing action items for each team member, creating agendas, and translating content in real time. In addition to automating the follow-up process, Microsoft Copilot in Outlook can suggest the best time to call colleagues after an event or conversation.

Aside from boosting customer experiences, generative AI can pinpoint trends, deliver personalized and unique responses to questions, and boost customer satisfaction. It can increase your company’s revenue by enabling proactive product recommendations, identifying opportunities for product optimization, and centralizing market research.

Besides improving operational efficiency, it can improve agent productivity by automating routine and recurrent tasks (like summarizing and transcribing text). Additionally, conversational AI tools can help businesses make intelligent decisions and optimise workplace processes by giving them insights they need.

It is also true that there can be challenges. For instance, most conversational AI solutions are capable of handling routine requests, but struggle with complex queries. In order to deal with more complex inquiries, conversational AI tools need constant training and fine-tuning.

Customer experiences can be hampered by solutions that don’t understand finer linguistic nuances, such as satire, humour, or accents. Moreover, conversational tools interact with customer data, so compliance with data privacy regulations is always a risk, just like most forms of artificial intelligence