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Until a decade ago, the only option people had to reach out to a company was to call or email their customer service team. Now, companies offer a chat team to provide better round-the-clock customer service. According to aFacebook-commissioned study by Nielsen, 56% of people would prefer to message rather than call customer service, and thats where bots come into play.

Bots are revolutionizing the way companies interact with their customers. A decade ago, bots were considered a passing tech fad. However, that debate has been put to rest now as major companies like Amazon, Microsoft, Facebook and others have started deploying bots in almost every area of their business. The new debate brewing in the bot community is about the choice between rule-based bots or AI bots. Which one to choose? Which one is better? These are the questions on the minds of business leaders intending to utilize bots in their organizations. There are many factors that contribute to the efficiency of bots for different applications, and understanding these factors can help businesses make informed decisions as to choosing between rule-based versus AI bots.

Building and deploying bots is now on most companies to-do lists, if theyre not already deployed. Nevertheless, most are confused about whether they should go with rule-based bots or AI bots. Lets evaluate the pros and cons of each.

Rule-based bots can answer questions based on a predefined set of rules that are embedded into them. The set of rules can greatly vary in complexity. Building such rule-based bots is much simpler than building AI bots. Rule-based bots are generally faster to train. The bots are built on a conditional if/then basis, which makes them simpler to train. The rule-based bots can take action based on the outcome of the conditional statements. Easy training of rule-based bots simultaneously reduces implementation cost. The rule-based bots are highly accountable and secure. These bots cannot learn on their own and will provide the answers that the companies want them to provide. Since rule-based bots cannot self-learn, this ensures that they will provide consistent customer service. Rule-based bots can professionally hand over the conversation to a human agent if the customer asks something that is absent from the database. The practice to handover the conversation to the human agent ensures that no unnecessary information is conveyed to the customer.

A rule-based approach also enables faster implementation of bots. Unlike AI bots, rule-based bots do not need to wait for years to gather data that can be analyzed by algorithms to understand customer problems and provide solutions. Rule-based bots can be easily implemented by embedding known scenarios and their outputs into them. These bots can then be embedded with more data according to new conversational patterns from new customer interactions. Although rule-based bots have many advantages, their limitations cannot be overlooked.

The problem with predefined rule-based bots is that they need to be embedded with rules for performing every small to complex task. If anything that is out of the database comes their way, then the rule-based bots hand over the conversation to humans. It means that rule-based bots cannot operate on a standalone basis; they need human intervention at some point.

Another limitation of rule-based bots is that of personalized communication. Chatbots can service different people speaking different languages. In addition, not only the languages, but the way of communication also varies from person to person. For instance, to book a flight to Paris one person may say, I want to book a flight to Paris, and another may say, I need a ticket to Paris. Both statements mean the same thing, yet if the rule-based bot is unable to understand that, it will pass the conversation to a human which may frustrate the customer.

Rule-based bots can be embedded with information from conversational patterns as time passes. Nevertheless, it becomes a challenge for developers to embed every possible scenario into rule-based bots. Although rule-based bots can be quickly implemented, they are hard to maintain after a certain length of time.

AI bots are self-learning bots that are programmed with Natural Language Processing (NLP) and Machine Learning. It takes a long time to train and build an AI bot initially. However, AI bots can save a lot of time and money in the long run. The use of AI bots works well with companies that have a lot of data as they can self-learn from the data. The self-learn ability of AI bots saves money, as unlike rule-based bots, they do not need to be updated after a certain interval of time. AI bots can be programmed to understand different languages and can address personalized communication challenges faced by rule-based bots.

With the use of deep learning, AI bots can learn to read the emotions of a customer. These bots can interact with the customers based on their mood. For instance, a China-based startup, Emotibot, is helping to develop chatbots that can detect the current mood of the customer and respond accordingly. With constant learning, AI bots can help provide personalized customer service to enhance customer engagement. Since AI bots can handle customer queries from end-to-end without human interaction required, they can be deployed for round-the-clock customer service.

AI can make chatbots smart, but it cannot make them understand the context of human interactions. For example, humans can change their way of communication depending on with whom they are communicating. If they are communicating with small children they use simpler words and shorter sentences. In addition, when human employees communicate with clients they use a more formal tone. Since bots cannot understand the human context, they communicate with everyone in the same way, irrespective of age or gender. The self-learning ability of AI bots might seem helpful to businesses but it can cause trouble sometimes. AI bots do not possess an accurate decision-making quality, and thus can learn something that they are not supposed to. For instance, a chatbot named Tay started posting offensive tweets. The chatbot Tay got manipulated through social engineering tweets and started posting undesirable phrases like Hitler was right, in a canned Repeat after me. series of tweets.

These advantages and disadvantages can help companies decide whether to use rule-based bots or AI bots, but only up to a certain extent. There are many other factors that enterprises should consider before implementing chatbots in their companies. Whether the bots will serve B2B or B2C, in what areas the bots will be deployed, and how the bots will be maintained are some factors to be considered. Rule-based bots and AI bots both have their own benefits and disadvantages, and both can be useful in their own ways. Enhanced customer service is king when it comes to the growth of a business. Understanding how different bots will improve their customer service ultimately helps them choose the best-suited bot for their business.

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Choosing Between Rule-Based Bots And AI Bots - Forbes