While most companies use the terms bots and conversational AI interchangeably, there are key differences between the two technologies. In recent years, bots have provided a new way for organizations to adopt NLP technology to drive traffic and engagement. Understanding what a bot is and what conversational AI is can go a long way in choosing the right solution for your business.
Bots are text-based interfaces built using rule-based logic to complete predetermined actions. While bots are rule-based and linear, they follow a predetermined conversational flow, conversational AI is the opposite. Instead of relying on a rigid structure, conversational AI uses NLP, machine learning, and contextualization to deliver a more dynamic and scalable user experience.
What is Chatbot in simple words?
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| Chatbots vs Conversational AI: What the Difference? |
A chatbot is a human-designed computer program that is intended to interact using human language. This conversation can be done via text messages or voice commands. With a machine learning approach, chatbots can increase their knowledge based on their interactions with users.
The function of a chatbot is usually to provide customer support, provide information, do what the user orders, and answer questions. In some companies, chatbots are deliberately integrated into social media, company websites, and chat applications.
What is Artificial Intelligence?
As the name implies, Artificial Intelligence (AI) is a program that operates using artificial intelligence that is able to imitate human intelligence. AI relies on complex algorithms and mathematical models to learn, plan, recognize patterns, and help make decisions. In addition, this system is also equipped with machine learning, deep learning, natural language processing (NLP), and computer vision techniques.
Where can conversational AI be used?
Conversational AI has several use cases in business processes and customer interactions. We have grouped these use cases into four broad categories.
1. Informational
In the informational context, conversational AI primarily answers customer questions or offers guidance on a specific topic. For example, your users can ask a customer service chatbot about the weather, product details, or step-by-step recipe instructions. Another example is an AI-powered virtual assistant, which answers user queries with real-time information ranging from world facts to breaking news.
2. Data capture
You can use conversational AI tools to collect important user details or feedback. For example, you can create more human-like interactions during the onboarding process. Another scenario is post-purchase or post-service chats where the conversational interface collects feedback on the customer journey, i.e., their experience, preferences, or areas of dissatisfaction.
3. Transactional
In transactional scenarios, conversational AI facilitates tasks that involve any transaction. For example, a customer can use an AI chatbot to place an order on an e-commerce platform, book a ticket, or make a reservation. Some financial institutions use AI-powered chatbots to allow users to check account balances, transfer money, or pay bills. This makes things easier for your customers and improves their experience.
5. Proactive
When you use conversational AI proactively, the system initiates a conversation or action based on certain triggers or predictive analytics. For example, a conversational AI application can send users an alert about an upcoming appointment, remind them about an unfinished task, or suggest products based on browsing behavior.
Conversational AI agents can proactively reach out to website visitors and offer assistance. Additionally, they can update your customers on delivery or service disruptions, without having to wait for a human agent.
How does conversational AI work?
Conversational AI works using three main technologies.
1. Natural language processing
Natural language processing (NLP) is a set of techniques and algorithms that enable machines to process, analyze, and understand human language. Human language has several features, such as sarcasm, metaphors, variations in sentence structure, and grammar and usage exceptions. Machine learning (ML) algorithms for NLP enable conversational AI models to continuously learn from vast amounts of textual data and recognize diverse linguistic patterns and nuances.
2. Natural language understanding
Natural language understanding (NLU) deals with the understanding aspect of a system. NLU ensures that conversational AI models process language and understand the user’s intent and context. For example, the same sentence may have different meanings based on the context in which it is used.
NLU uses machine learning to distinguish context, distinguish meaning, and understand human conversation. This is especially important when virtual agents have to escalate complex questions to human agents. NLU makes the transition smooth and is based on a precise understanding of the user’s needs.
3. Natural language generation
After understanding the user’s input, the system formulates a coherent and contextually appropriate response. Natural language generation (NLG) enables virtual agents to produce human-like sentences in a way that is clear, relevant, and linguistically natural. NLG uses powerful deep learning algorithms to formulate responses in context. Additionally, as AI chatbots interact more with users and human agents, their responses become more refined and flexible over time.
What is the difference between chatbots and conversational AI?
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| Chatbots vs Conversational AI |
Conversational AI platforms take inputs from sources such as websites, databases, and APIs. Bots, on the other hand, require ongoing effort and maintenance with text-only commands and inputs to stay up-to-date and effective. Conversational AI platforms benefit from their malleable design nature, allowing for seamless interactions with users.
For example, if a user changes their mind mid-conversation and has a seemingly random question from what they started, a conversational AI platform can compensate for human randomness and automatically break out of the conversation flow to provide a timely, sensible response. In comparison, bots do not have the ability to switch topics and are more limited to pre-defined scripts, and the static nature of their rules means they cannot produce any output that is not manually placed in their flow.
1. Functions and Capabilities
With its limited capabilities, chatbots can only be used to answer questions asked by its users. This system is widely used to respond to and carry out simple commands. Even so, chatbot capabilities are limited to the scope of tasks that can be handled. In addition, chatbots still rely on human roles to learn new things or simply make decisions.
Meanwhile, artificial intelligence can utilize data and experience gained, analyze complex information, and make complex decisions as well. In addition to being able to adapt to the environment, AI can predict something based on data patterns.
2. Its Independence in Learning New Things
As previously mentioned, chatbots work to adjust to user requests. The responses it gives will adjust to the rules programmed before that. However, some chatbots are now also equipped with a machine learning approach. This technology makes it easier for them to provide more diverse responses.
Artificial Intelligence has been equipped with machine learning to learn independently since the beginning. AI can process data, recognize patterns, improve its performance, and provide decisions and understanding of the commands submitted.
3. Complexity of Creation and Implementation into Everyday Environment
Chatbots only require specific tasks and are usually used on a smaller scale. This system tends to have limitations in understanding other more complex contexts. To provide the right response, chatbots require further clarification from their users. The responses given are also highly dependent on certain keywords or patterns.
On the other hand, most industries and more complex scenarios tend to use Artificial Intelligence as a support. Although it produces adaptive and higher quality results, AI requires more supporting technology, for example, more sophisticated resources. This system is able to understand the nuances of customer sentences, understand the natural language of its users.
4. In terms of advantages and disadvantages
a. Bots
- Focus on Navigation
- Rule-based linear interactions
- Pre-defined conversation flows
- Single channel; can only be used as a chat interface
- Can only perform text commands, input and output
- Manual maintenance, updates and revisions = difficult and time consuming to scale
- No capacity to learn
b. Conversational AI
- Focuses on Dialogue
- Non-linear and dynamic interactions
- Natural language processing, understanding and contextualization
- Omnichannel (can be used on websites, voice assistants, smart speakers and call centers
- Can perform voice and text commands, input and output
- Highly scalable. As the company’s database and pages are updated, so is the conversational AI interface
- Deep learning capabilities
How to provide AI chatbots for businesses?
Companies are investing in conversational AI because of its potential to have more personal and fluid conversations with customers. Conversational AI mimics human interaction to the point where it’s hard to tell if the person is talking to a human or an AI. But first, think about this. Does your company really want that level of personalization? Can you reach all your targets with a bot instead?
Bots can also personalize conversations to some extent. They can speak to the person calling and display a persona as well. For small businesses that are plagued with repetitive inquiries, bots can be invaluable in filtering leads and offering relevant notes to users.
In recent years, businesses have found bots to be a short-term, stopgap solution rather than an effective solution to their engagement challenges, leaving bots disconnected and not feeding into each other across their sites.
With Conversational AI, the ability to build effective Digital Assistants becomes feasible and efficient. Customer interactions with these platforms are consistent and high-quality across brands, whether customers are engaging with in-depth sales inquiries or solving support issues.
Conclusion
Conversational AI can also leverage past interactions with each customer across channels—online, over the phone, or via SMS. It can easily pull in a customer’s personal information, services used, order history, and other data to create a personalized and contextualized conversation. On the other hand, most bots only know what the customer explicitly tells them, and most likely require the customer to manually enter information that the company or service should already have.
You can certainly use bots to alleviate some of your challenges, but if your goal is to create a competitive advantage and build truly great customer interactions, then you’ll need to use a more sophisticated set of tools to build conversational AI.
