Chatbots vs. Conversational AI: What's the Difference?

Chatbots vs. Conversational AI: What's the Difference?

The involvement of artificial intelligence in human interactions has experienced massive transformation in recent years. In the past, most systems were rule-based, meaning they relied mainly on predefined rules to respond. Chatbots were then introduced to give users conversational experiences. These chatbots used scripted responses and decision trees to generate responses—but they were only good at handling simple interactions.

The introduction of natural language processing (NLP) and machine learning (ML) algorithms completely changed interactions between humans and computers. With these technologies, computer systems became smarter and more interactive, capable of interpreting human language and generating relevant outputs. Such AI systems provide more intense conversational experiences than chatbots.

Today, chatbots are still used alongside conversational artificial intelligence to facilitate human interactions. While chatbots and conversational AI may seem similar, entirely unique technologies are driving them behind the scenes. Keep reading to discover the key differences between chatbots and conversational AI.

What are chatbots?

Chatbots are software designed to simulate human conversations through voice or text interactions. These computer programs have wide use cases, including providing customer support, answering questions, booking tickets, directing users to the right resources, scheduling calls or appointments, and more.

Many chatbots perform most of these tasks automatically, which frees humans from repetitive and monotonous tasks. Chatbots are available 24/7, enabling organizations to cut costs, especially regarding staffing.

In addition, traditional chatbots can drive customer engagement by providing faster responses, which can translate into more satisfied customers. But this isn’t always the case since chatbots can still misinterpret customer queries and provide irrelevant responses from time to time.

The basics

The two common types of chatbots today are rule-based and AI-powered.

Rule-based chatbots follow a set of predefined instructions or a decision tree to provide fixed responses to users. For instance, when a user asks a question, these chatbots analyze text input for certain keywords and phrases and then retrieve a response from a database that seems to match the query.

While these responses can be very helpful, rule-based chatbots are incapable of learning or adapting to new situations, meaning they may fail when users raise questions that are beyond their scope. They’re also rigid and have limited use of contextual cues, which confines them to handling only simple and predictable interactions.

AI-powered chatbots, on the other hand, use algorithms to process user input at a more detailed level and generate relevant feedback. Such chatbots feature machine learning algorithms, enabling them to learn and adapt. As a result, they can handle more complex tasks.

What is conversational AI?

Conversational AI uses artificial intelligence technology to provide human-like conversational experiences to users. It’s capable of processing and interpreting human language and generating relevant responses. With its algorithms, conversational AI can also process new datasets and adapt to new interactions.

Typically, conversational AI undergoes rigorous training in which it’s exposed to large speech datasets that enable the software to predict or generate meaningful and high-quality responses. Due to its advanced capabilities, conversational AI helps to improve customer engagement, automate routine tasks, and provide 24/7 user support. Conversational AI can also analyze individual behavior and habits and generate more personalized and tailored interactions.

The basics

Conversational AI is generally powered by the following components:

  • Natural language processing. This is a subfield of artificial intelligence that allows computer systems to process human language (both written and spoken). Depending on the training data and supported languages, conversational AI can process what you write or command it to do—including your sentiments, intent, and context—and can generate a meaningful response. But despite their advancements, AI systems aren’t perfect and won’t always get it right.
  • Machine learning. Conversational AI is trained on huge datasets of human conversations, enabling it to learn and identify patterns and generate meaningful responses.
  • Deep learning. This field involves using neural networks to identify complex patterns and relationships from data. Deep learning enables conversational AI systems to interpret human language better and produce relevant outputs.
  • Sentiment analysis. By being exposed to large datasets of human conversations, conversational AI can detect emotional tone from user inputs. It can then adapt to the perceived user’s state and provide appropriate responses.
  • Text to speech. Some conversational AI also features text-to-speech technology that allows it to respond through spoken language.
  • Contextual interpretation. Conversational AI can keep track of user queries, responses, and context in order to provide conversational experiences with a natural flow.

Though conversational AI can’t compare to actual human conversations, it can still enhance customer experience by facilitating near-real-time interactions. It's also quite versatile, which allows it to accommodate multiple interfaces—including voice, messaging, and social media.

Common applications of conversational AI are in virtual and voice assistants, like Siri and Alexa, where it plays a variety of roles—including playing music, setting alarms, and answering different questions.

Chatbots vs. conversational AI: main differences

The main difference between chatbots and conversational AI is:

  • Chatbots serve specific and rule-based purposes.
  • Conversational AI uses its natural language understanding (NLU) capabilities and machine learning algorithms to provide dynamic and versatile conversational experiences across multiple platforms.

Chatbots also provide preset responses based on certain keywords and phrases and excel in limited interactions. Most chatbots are rigid and don’t have the capability of adapting to new interactions.

Conversational AI is trained on large datasets and can learn from new data and make better predictions. As a result, conversational AI can handle more complex tasks and interactions than chatbots. It also offers a higher level of sophistication in human-computer interactions, offering a more natural approach to communication.

Functionality and user experience

Since rule-based chatbots follow predefined patterns to generate responses, they have limited functionality and narrow use cases. These chatbots have trouble providing dynamic conversational experiences or handling complex interactions.

Conversational AI is powered by far more advanced technologies like natural language processing and machine learning algorithms, which allows it to offer numerous functionalities. It can facilitate complex interactions by adapting to user input, analyzing sentiments, and applying context.

In terms of user experience, chatbots have limited functionality, meaning that users’ interactions are somewhat constrained. When individuals ask questions beyond the chatbot’s scope, they will likely not get satisfactory answers to their queries. A chatbot’s inability to process complex interactions can also lead to frustration.

Conversational AI’s ability to adapt to user inputs and provide dynamic conversational experiences facilitates more engaging customer interactions. This AI can also interpret user inputs better than chatbots—enabling it to respond more coherently and add a natural conversational flow.

Powered by NLP technology, conversational AI can interpret user inputs and apply user intent, allowing it to improve customer engagement by providing faster and more meaningful responses. Conversational AI also supports omnichannel communication, which caters to users on different channels.

Applications and use cases

Chatbots and conversational AI have helped transform different sectors, enabling users to get answers to queries, book appointments, access helpful resources, and much more.

But applications and use cases of basic chatbots are generally limited. This is because they have scripted responses and rely on simple algorithms to provide user feedback. Sometimes, individuals can only select specific questions from a given list to ask the chatbot, making the experience similar to searching within FAQs. AI-powered chatbots are far more capable of providing better interactions. Since they have NLP and ML algorithms, they’re better at interpreting user inputs and providing relevant responses.

Conversational AI has more use cases due to its advanced features and technologies. For starters, it can be used as a customer support tool to improve satisfaction and engagement rates. Since conversational AI systems are available 24/7, they can provide valuable help to users as an alternative to 24/7 staffed call centers. This is specifically useful for companies with globally distributed customers.

Additionally, conversational AI can analyze individual preferences and habits to facilitate more personalized and tailored interactions. Leveraging conversational AI in the e-commerce sector can allow firms to gain insight into their customers’ browsing and purchasing behaviors and recommend products that are more likely to appeal to them. Organizations can also use conversational AI to perform a sentimental analysis on social media platforms to better understand what customers think of their brand.

Industry leaders: conversational AI platforms

From Microsoft to Apple, here are some of the industry leaders when it comes to conversational AI platforms.

Microsoft's approach to conversational AI

Microsoft Azure provides a wide range of tools to help organizations build AI-driven applications. For example, the Microsoft Azure AI Bot Service is a low-code platform that lets developers create, test, and deploy virtual agents—even without much programming experience.

This AI bot can be deployed on different platforms, including mobile apps, websites, and social media sites—facilitating a multichannel experience. Microsoft Azure AI Bot Service has a centralized management system, which allows software teams to scale quickly and still maintain compliance with regulations.

Microsoft’s conversational AI can be adopted in sectors like healthcare, retail, and manufacturing. It can help improve operational outcomes, accelerate innovation, support predictive care, optimize product placements, and more.

Amazon Alexa's role in shaping voice assistants

Amazon Alexa has played a fundamental role in the adoption of digital voice assistants. Launched in 2014, Alexa helped popularize natural language processing technology by listening and responding to people’s commands. It can interpret context, intent, and sentiments—which at its launch was revolutionary. Though it can misinterpret some commands, it still manages to help users control various smart devices with their voice.

Alexa’s voice-first approach helped advance technologies like text-to-speech and speech recognition. It also influenced other voice assistants to support multiple languages and cater to users in different geographical regions. Its success encouraged competition from other virtual assistants like Microsoft’s Voice Assistant (Cortana), Apple’s Siri, and Google Assistant.

Apple's Siri and its contributions to conversational interfaces

Apple’s Siri was among the first technologies to help users interact with conversational AI using their voices. Individuals ask Siri questions and receive relevant responses. It also facilitates more natural conversational experiences even when handling complex queries. Siri’s approach influenced future AI technologies to use a conversational tone for user interactions.

Siri also enables users to experience the value of having a digital assistant. It can perform tasks like scheduling appointments, playing songs, performing online research, and making calls. After its launch in iPhones in 2011, it was later integrated into products like HomePod, AirPods, and Apple Watch to provide a multimodal experience through gesture, touch, and voice interactions. This integration helped other stakeholders to identify more areas of AI applications.

The future: conversational AI’s potential

As artificial intelligence systems’ processing capabilities broaden and increase, their use cases are also likely to increase. These tools will become better at handling complex tasks and automation, enabling teams to delegate more complex tasks.

Conversational AI will also facilitate increased human-agent collaboration. Support teams will be able to use conversational AI tools to perform numerous tasks—and increase their creativity and productivity. Contrary to alarmist claims, AI-powered tools will not replace human workers but rather complement them in their roles.

Advances in natural language processing and deep learning will increase the ability of AI-driven tools to interpret user intents, contexts, and human language. As a result, conversational AI platforms could generate more accurate and meaningful responses. Such tools will also get better at sentiment analysis, allowing them to detect emotions appropriately and generate contextually correct responses.

In the future, conversational AI applications will likely evolve to support more modes of interaction. Apart from text and voice, individuals may also use gestures, visuals, or eye movement to interact with AI systems, leading to more immersive experiences. Conversational AI systems will also become compatible with more smart devices and the Internet of Things (IoT).

Conversational AI technology will promote seamless communication by providing real-time interpretation and translation. Though some AI platforms already have this feature, there’s still room for growth to support more languages.

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Chatbots are typically rigid and serve specific roles like answering questions and directing users to various resources. They also feature scripted responses and follow predefined rules, making them suitable only for simple interactions.

Conversational AI, on the other hand, uses natural language processing and machine learning models, allowing it to identify patterns and learn from vast datasets. As a result, it can provide more conversational experiences to users. Conversational AI can also adapt to user input, generating relevant responses and handling complex interactions.

Chatbots and conversational AI can improve customer satisfaction and engagement by providing faster responses—with 24/7 availability. However, rule-based chatbots' functionality and use cases are somewhat limited.

Generally, integrating artificial intelligence into your workflow allows you to cut costs, improve customer experiences, and increase overall productivity. To harness the power of artificial intelligence, consider working with AI developers on Upwork to help you out. Get started today!

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Chatbots vs. Conversational AI: What's the Difference?
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