Conversational AI vs Chatbots: Which one fits you?

Over the past few years, chatbots have been actively used in various fields. They automate processes and replace employees. Therefore, bots increase productivity and deftly interact with people. Like many technical tools, every year chatbots are getting better and smarter than their predecessors. This is the reason why more and more companies and industries want to implement chatbots in their solutions. Let’s see the core gap between chatbots vs conversational AI.

• What is Conversational AI?

Conversational artificial intelligence is a way to simulate human communication. The main components of conversational AI are natural language processing (NLP) and machine learning (ML). Based on these two components, you can build a virtual interlocutor. It can answer and ask questions, have a conversation, joke, and anything else that was programmed into the conversational AI.

Machine learning development is responsible for algorithms and datasets. Like other areas of ML, the increase in incoming data leads to the improvement of AI models. The conversational AI database is enriched with knowledge, and the quality of a bot is increased.

NLP is responsible for automatic natural language manipulation and analysis. In conversational AI, it consists of 4 stages:

Input generation –  getting a voice or text message that is sent to the input at the first step;

Input analysis – recognition of the information entered in the previous step. Natural Language Understanding (NLU) as an NLP component helps to analyze the language, its context, and recognize the meaning of speech or text;

Output generation –  formulating a response after understanding the meaning of the request. Natural Language Generation (NLG) is also an NLP component that is used to generate a response to keep the conversation going.

Reinforcement learning –  entered inputs analysis to improve the accuracy of conversational AI responses in the future.

NLP components make it possible to understand voice or text messages and answer in the same natural way. But don’t forget that conversational AI is just a tool. To implement interaction with people in the real world, an environment is needed for integration. For example, it can be Facebook Messenger, Viber, Telegram, or other messaging Apps.

•  What are AI Chatbots?

Chatbot solutions can be an excellent shell for conversational AI. But before moving on to AI bots, let’s understand what a chatbot is.

A chatbot or bot is a computer program that allows the user to converse with a virtual agent. Simulation of a conversation with a real person is carried out using voice generation or messages. These are, respectively, voice bots or text bots.

Chatbot conversation can be primitive or varied, like a human. It can be formal, funny, strictly informative, friendly, and so on. It all depends on how the chatbot is programmed. This tool is very widespread in the world: according to the average estimates, about 1.4 billion people use chatbots.

Rule-based chatbots are the simplest bots. The logic of building a conversation with a user depends on each pressed button and the response provided. Such programs are created with scripts for every user step.

Intellectually independent chatbots are more complex software solutions that use ML. Such bots extract keywords from the user’s message. These words and phrases are triggers for the program to provide a predetermined answer. Over time, such a machine learning chatbot gets smarter and better at recognizing intents and identifying the right answers. For example, for a client’s message: “I have a problem with my bank card.”, the bot can highlight the words “problem” and “bank card” as intents and then look for a solution in the knowledge base.

Finally, AI chatbots are much smarter and can understand fluent human language. These are the chatbots with conversational AI discussed earlier. Conversational AI chatbots capture the essence of the dialogue, remember the context, and strive to solve the user’s problem. According to the survey, approximately 40% of buyers do not see the difference between a chatbot vs conversational agent if they receive answers to their questions.

In general, bots have become widely used for different purposes. For example, Facebook Messenger, by itself,  has over 300,000 bots! And this number continues to grow. Over the past 5 years, the interest in chatbots has grown by about 5 times.

•   Conversational AI vs. Chatbot

Chatbot vs conversational AI are two concepts that can complement each other in a technical solution. As stated in the previous paragraphs, conversational AI is a natural language simulation tool. In turn, a bot is a way to interact with a human. Bots may not include AI. But conversational AI powers chatbots. Therefore, here the ML part remembers what you said to the bot, and the natural language processing services help companies understand the purpose of the messages and generate the necessary answer.

Chatbot vs conversational AI software can be incredibly smart. According to statistics, about 27% of users who contacted customer support cannot say whether they spoke to a human agent or an NLP chatbot. The most famous advanced voice bots are Apple’s Siri, Google Assistant, and Amazon Alexa. Yes, it took a lot of time and resources to build the best AI chatbots. But as the practice has shown, such bots help attract consumers and save companies’ expenses. 

•    How Do You Create an AI Chatbot?

Conversational AI can be created in many different ways, and its functionality can be varied. AI bots can send text messages to solve issues, analyze images, provide videos, or work with documentation. A machine learning chatbot can also provide answers to simple queries and automatically transmit complex questions to human agents. As you can see, conversational AI has a large-scale development field. So, how do you create a chatbot?

1. Understand the goals 

First of all, you need to decide what you need a conversational chatbot for. Well-defined project objectives are the most important thing to get started.  Conversational AI is not always required, especially on a small scale:  sometimes it is better to hire a support agent for these tasks. But when you understand that the AI bot will become an excellent virtual employee and assistant, you need to think about the company’s needs. The fields of activity of an NLP chatbot should be described with specific tasks that it must solve. This way, the end goals and metrics for analyzing the technical solution will be clear. In the future, it will help to evaluate chatbot conversations with consumers. For example, it can be message metrics or bot metrics.

2. Sketch out different flows

So, the scope for creating conversational AI has been agreed upon. At this stage, it is necessary to think over all possible scenarios for the bot. This clarifies all the nuances of the future bot for both business and development teams. Conversational AI must have a well-thought-out logic described in a document. It is good to come up with potential questions and conversations on behalf of the user.  Based on this analysis, the flow components can be structured and used for developing the chatbot.

3. Build a chatbot

Now we need to move on to creating a machine learning chatbot. There are several options for how to do this. An easier way is to use non-coding platforms. For example, the popular Chatfuel platform, FlowXO, Botsify, Pipe.bot, etc. Each platform has its advantages: easy integration, focus on the simplicity of use, ready-made templates, automatic mailings, and much more. Also, you can use SaaS products to simplify your work. For those teams that have good development skills, there are code-based frameworks. They allow the creation of more flexible and complex AI bots. NLP processing tools and libraries are already built into them. Examples of such frameworks are Microsoft Bot Framework from Azure Bot Service, Wit.AI from Facebook Bot Engine, Watson Assistant from IBM, and API.AI from Google Dialogflow.

4. Testing

The last step before launching is bot testing. It is important to cover several strategic points here. The first is whether the conversational AI chatbot meets the business objectives. The check is carried out by conducting various dialogues with the bot. If possible, you need to cover all topics and types of questions/tasks addressed to the bot. The second point is to identify all those issues that conversational AI cannot resolve. This is an important part of testing. The scope of these topics can be incorporated into improving the solution. And the third thing to check is attempts to “break” AI bots. Pain points are discovered by asking challenging and “wrong” questions and engaging in strange dialogues. Users can use your technical solution not only for true purposes but also for fun. It would be best if you gave at least minimal thought to the behavior of AI chatbots in such cases. Anyway, this will make the bot better and more versatile because the model learns during the process.

• Conversational AI Use Cases

Smart text and voice bots can be excellent virtual employees in different areas of business. Let’s take a look at some practical examples.

Customer Support is one of the most common areas for a conversational chatbot. Users ask for help with all sorts of problems and questions. The bot increases the effectiveness of the support provided. It also helps to reduce the number of human agents significantly. But if having people on customer support teams is important to the company, it can still use a chatbot. AI can handle simple requests while sending complex ones to a hotline and connect users to employees. Additionally, a company can make a simple bot. It can be based on frequently asked questions to cover most of the cases. Some of the best customer support AI bots are ChatBot, ManyChat, and Acquire.

More and more companies want to have personal assistants as AI bots in their arsenal of tools. Approximately 75% of shoppers choose to shop using self-service. Users are less willing to contact salespeople and are more and more focused on a DIY process.  People are thus deprived of individual recommendations without involving real employees. But a personal assistant chatbot makes up for this lapse and simplifies the shopping process simultaneously. AI virtual collaborators can deftly process orders and advise on shopping lists, thereby increasing sales. Personal assistants can understand the conversation with the user and be applied in a variety of ways. Another example of the successful use of conversational AI is in healthcare. Chatbots can be used as an intermediary between healthcare professionals and patients. In this case, health professionals will spend less time on routine processes. In turn, patients can independently organize their visits to doctors, receive individual treatment and test plans, and have answers to their questions. All this improves service efficiency and saves time. Examples of famous personal assistants are Cortana, Bixby, and Youper.

Many consumers go through a flow via text or voice bots. This means that their dialogues or key phrases with AI can be saved in the company’s database. Bots are good helpers in data aggregation. This allows the business to analyze customer behavior and requests. It is essential for improving business strategies. Information is rarely recorded from direct conversations with human employees, such as at the bank or the reception. Most often, these are telephone conversations in audio recordings. Such a repository is challenging to analyze and make conclusions. AI chatbots work with natural language, understand the meaning, and important words. All of this is a broad field of activities on the way to a better understanding of users.

Chatbots are gaining momentum. One of the reasons for that is the population’s digitalization. Millennials are focused on user-friendly tech solutions. About 67% of them are more likely to buy something using chatbots. Therefore, companies are trying to apply AI in more and more areas.

• Benefits of AI Chatbots 

24/7 availability. Chatbots do not get tired. More than half of consumers want to have a business available 24/7. Conversational AI easily meets these needs.

Instant answers. The virtual assistant spends a fraction of a second preparing a response. One AI chatbot can respond simultaneously to a huge number of users, unlike a human employee. Approximately 69% of customers do highlight quick responses from bots as an important business advantage.

Endless patience. Many companies strive for a high level of service quality. But employees may not always be patient enough for some consumers. The chatbot is not subject to its emotions since it doesn’t have them 

 Therefore, virtual assistants can answer questions for an infinitely long time, repeat the same thing, and still remain calm.

Multi-language. You can have text or voice bots that can communicate in many languages. It’s just a matter of programming and setup. This helps the business to attract more customers and not look for polyglot employees.

Cost savings. According to various estimates, on average, a company can save $0.70 on each individual client request thanks to virtual assistants. And if you count how many requests there are per day/month/year, then the amounts will be impressive.

Increased sales. According to statistics from companies that have successfully implemented conversational AI, their sales increased by 67%. It should be added that consumers trust in the dialogue with the AI bot. About 22% of customers follow its recommendations for purchasing products.

Increased customer interaction. People like to interact with simple or most advanced chatbots. This means that customers are more familiar with the brand. Nearly 80% of consumers say their experience with an AI bot has been positive. 

Reduce human error. Humans aren’t robots, and that they tend to make mistakes. Well-trained virtual assistants do an excellent job with a minimum number of errors. Organizational statistics show that real customer communication with human agents last year decreased by 70% due to the introduction of AI chatbots. You can imagine how much more accurate the processes of companies have become.

Challenges of AI Technologies

Unclear user communication. Even a well-programmed conversational AI product has interoperability problems. Sometimes a chatbot cannot understand its users for various reasons. Such problems include  slang, fuzzy speech, strong accent, loud background noise, strange messages, emoji, etc. Also, languages are evolving,  and new words appear. In addition, simpler chatbots have a problem with unprogrammed scripts. Consumers can access virtual assistants for requests they cannot fulfill. All such cases need to be monitored and used as scenarios for improvement.

Conversational AI communication challenges (source)

Data Security and Privacy. This is an essential point, especially when it comes to personal data. Development teams need to build conversational AI reliably. The possibility of obtaining private information should be excluded. The guarantee of the confidentiality of personal data must be respected.

User apprehension. Consumers may be wary of conversations with chatbots. They may be reluctant to share personal data with a machine rather than a person. In addition, many people prefer face-to-face communication with human workers. According to the survey, about 22% of participants say that bots cannot recreate live communication like with real people.

People have become so active in using chatbots that their number is growing rapidly. This proves user-friendliness and process improvement for companies. The trends towards the development of conversational AI continue. And if chatbots are now used by almost one and a half billion people, I wonder what number will be in a few years? This is a good incentive for many businesses to have such a tool as an AI chatbot soon. 

Which solution is right for your business?

Conversational AI is a language processing system. So, what’s the difference between conversational AI vs chatbot? The difference between them is like that between an answering machine and an employee who can conduct a dialogue.

The conditional chatbot works according to a pre-prescribed scenario. If the question is out of bounds, it “breaks”: asks to reformulate, offers to return to the main menu, or simply gets lost. Such systems can be useful – especially if the calls are typical, for example: changing the password, checking the balance, order status. Simple problems are simple solutions.

Conversational AI works differently. This system does not rely on a list of predefined commands, but on algorithms for understanding the language. It “listens” (in the sense – processes input), determines the user’s intention, and selects the appropriate answer. At the same time, it can rely on the context of the dialogue, consider previous remarks, recognize emotions, mistakes, and non-standard formulations. It is more flexible, more accurate, and more versatile. But it requires a different approach to implementation: it needs to be trained based on real data, integrated with existing business processes, tested, and fine-tuned.

For business, the key question is not in technology, but in the task. Do you just need an interface to help unload operators at the first level of support? Or do you want the system to be able to accompany the client in the decision-making process, give personal recommendations, sell, advise, and fix the problem?

If the client path consists of the same type of steps, the chatbot is suitable. But if the client needs to explain something, adapt to his situation, or continue from where he left off last time, then full-fledged dialogue intelligence will already be required.

Future trends in conversational AI

AI solutions that work with text and speech are developing faster than most other areas. Already, several sustainable trends directly affect the strategy of companies investing in communication automation.

1. Bias towards voice.

Interfaces become voice-activated – not because it’s fashionable, but because it’s faster. Users get used to talking to technology. And if earlier voice assistants coped only with “turn on the music”, now it is a full-fledged channel of communication with banks, insurance, and logistics services. Order, clarification, cancellation, complaint – all these can be processed by voice. The text is more about the archive, the voice is about real interaction.

2. Support for multiple languages ​ ​ without separate configuration.

Modern language models cope with the recognition and generation of text in dozens of languages. This allows companies to work with an international audience without hiring separate teams for each region. This is especially true for e-commerce and service businesses entering new markets. Translation, adaptation, and localization are now solved at the level of the model architecture, and not due to an increase in the budget for operational support.

3. Deep integration with back-end systems.

Current Conversational AI implementations are increasingly not limited to chatting. Systems are connected with CRM, ERP, analytics systems, logistics, and billing. This turns them into participants in the business process. The client asks – AI does not just answer, but pulls data, generates a report, and starts an operation. The dialogue model becomes an intermediary between the user and the entire IT infrastructure of the company.

4. On-the-fly self-learning and adjustment.

One of the most serious barriers to working with AI is the constant need to “learn” the model. Today, this problem is gradually being solved: solutions appear that independently analyze their mistakes, collect data, and correct behavior without the participation of a data scientist. This lowers the cost of ownership and speeds up scaling.

5. AI as an employee with a functional workload.

If earlier the AI ​ ​ interface was an additional option, today it is considered a “digital team member”. He can make appointments, transfer tasks, form documents, remind, and initiate actions on other platforms. This is no longer just an imitation of communication – it is participation in the operating system.

So, what to choose?

The market is moving from “fashion introductions” to pragmatics. Decisions are chosen not by hype, but by results. Conversational AI is not a universal answer to all tasks. But where there is scale, complexity of processes, and a high cost of error, such systems become not just useful, but necessary.

Companies that successfully implement dialog systems adhere to a simple principle: start with one targeted scenario, test, fix metrics, and scale. For example: first – automation of the first support line, then – connection to CRM, then – access to the voice channel.

In an environment where the number of operators is decreasing and customer expectations are growing, Conversational AI gives businesses a tool that can maintain quality without proportional cost increases. For many companies, this is no longer a “need” question. This is a question – where to start, how quickly it will pay off, and who will take responsibility for the result.

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