Chatbots in banking, insurance area, sales... These tools are incredibly popular. They can support people with knowledge and sometimes replace them in communicating with customers. But what can be done to prevent a chatbot from surprising us with an ignorant or obnoxious message – an action that could potentially damage the image of the company?
From this article you will learn, among other things:
- What types of chatbots companies are using today
- How to choose the best chatbot for your company
- How to test chatbots for different levels of refinement
A short history of chatbots
Chatbots began gaining significant traction in the mid-2010s. Initially, they were simple tools designed to serve as interfaces for direct communication between customers and company representatives.
As AI technology advanced, these chatbots evolved to recognize simple intents, making them particularly useful in industries like travel, banking and insurance, telecommunications, where customer queries are often very repetitive. With the advent of models like ChatGPT, the potential and capabilities of chatbots have expanded significantly.
Implementing an AI chatbot that utilizes a large language model is tricky, because these models are not inherently designed to be accurate and precise all the time. In other words, they can “hallucinate”, producing facts or statements that aren’t grounded in reality. This makes AI chatbots relatively easy to implement as demos, and they often perform impressively at first glance.
However, with deeper testing or during full-scale production, it quickly becomes apparent that these chatbots may fail to provide accurate or reliable answers in certain scenarios. This is why it’s crucial to have a solid procedure for chatbot selection and testing.
Types of AI chatbots
There are four main types of AI chatbots that businesses can implement, each with distinct features and use cases.
Rule-based chatbots
It's a chatbot that has been around for a long time. This type of chatbot operates on predefined rules and decision trees, guiding the user through a series of clickable options without requiring them to write anything. Essentially, the entire conversation is designed in advance, allowing the chatbot to direct the customer or user to the correct place or resource.
These chatbots are straightforward and effective for simple, linear interactions where the path to resolution is predictable and consistent.
AI-powered search (semantic search)
The second type, while not a chatbot in the traditional sense, is AI-powered search, often referred to as semantic search. This technology allows users to find specific passages or parts of documents stored in repositories that likely contain the answers to their questions.
For example, if you ask, “Where can I find and see white sharks?” the AI search doesn’t rely on mere keyword matching. Instead, it understands the meaning of your query and identifies documents that mention white sharks or locations where sharks can be found. While this approach may not be highly precise and lacks deep intelligence, it is a valuable tool for quickly surfacing relevant information. This type of AI is particularly useful for empowering employees to locate information faster.
This approach is useful if you have hundreds or thousands of documents that overlap and don't want to build a proper knowledge base for a chatbot.
AI knowledge assistant chatbot
The third type is the AI knowledge assistant chatbot. This is a more advanced tool that not only searches internal company documents but also determines which passage contains the best answer and formulates a response. This is a step ahead of semantic search, because the chatbot itself produces a refined answer. It can summarize, add relevant details, or present the answer in a way that can be directly forwarded to a customer. Although still primarily an internally-facing tool used by employees, this type of chatbot offers more than just locating information.
This chatbot can be also plugged into corporate systems like ERP, CRM or other.
Customer-facing AI chatbot
It's a tool that interacts directly with customers and handles a wide range of tasks, such as service, product selection, sales and order-related inquiries. This type of chatbot can automate about 20% - 50% of conversations traditionally performed by a human. The chatbot not only finds relevant information, but also suggests next steps and makes simple decisions. This makes it the most difficult type to implement, as it requires the chatbot to be highly accurate, context-aware and able to seamlessly guide the customer through various processes.
The ability to handle complex, dynamic interactions makes this type of chatbot a powerful tool for improving customer service, but it also requires careful planning and testing.
Which chat will be the best and most useful?
Selecting the right AI chatbot for your business is a critical decision that hinges on several key factors. Here are the primary criteria you should consider when choosing a vendor or a solution.
1. Determine the type of chatbot needed
The first step is to decide which of the four types of chatbots meets your needs best. For instance, a semantic search chatbot is the simplest in terms of AI complexity, while an automated customer assistance chatbot is the most challenging to implement. The latter will likely be the most expensive and require significant changes within your organization. It’s crucial to assess whether your potential return on investment justifies venturing into more advanced AI chatbot solutions. Consider the specific use cases within your organization and the value each type of chatbot could bring.
2. Language requirements
The second consideration is the range of languages the chatbot needs to support. Depending on your customer base or internal needs, you might require a chatbot that operates in multiple languages. Not all AI chatbot solutions are equally adept at handling multiple languages, so it’s important to choose a solution that can meet your linguistic needs effectively.
3. Knowledge integration
Next, consider the type and extent of knowledge the chatbot must access to perform its tasks. This could range from a small set of documents to more complex integrations with systems like ERP (Enterprise Resource Planning). The scope of the knowledge base will influence the type of chatbot you choose. For example, a simple rule-based chatbot might only need access to a FAQ document, while an AI knowledge assistant or automated customer assistance chatbot might require deep integration with multiple internal systems to pull accurate, real-time information.
4. Tolerance for mistakes
Finally, it’s essential to evaluate your organization’s tolerance for errors. Chatbots, particularly those powered by large language models, may occasionally provide inaccurate information. Consider the impact of these mistakes: Are they easily recoverable, with minimal risk, or could they lead to significant losses or damage to your reputation? Your tolerance for such errors will guide you in choosing the right level of sophistication and the corresponding safeguards needed in the chatbot solution.
This is the time for testing
When it comes to testing AI knowledge assistant chatbots and automated customer assistance chatbots, the process is crucial for ensuring they perform as expected.
Questions levels
Below is a sample set of questions categorized by level of difficulty.
Easy questions
These questions have clear and straightforward answers directly found in the knowledge base, like “Is there a customer service point in London Stratford?” and there is a mention of “London Stratford customer point” in the documentation.
Medium questions
These questions require paraphrasing or synonym recognition, with answers that are in the knowledge base but not in the exact wording used by the question. Examples:
Is there a help desk in East London?(Answer found as “Customer service point in London Stratford”)
Does product X come with a guarantee?(Answer found as “Warranty information for product X”)
Where can I find the latest data on company earnings?(Answer found in document “2023 Annual financial report”)
Hard questions
These questions require some logical inference or understanding of context, and the answers may not be directly stated in the knowledge base. Example:
Is there a discount available for the premium plan?(Requires inference that premium plans do not offer discounts, as in standard plans description there is a mention about a discount and for premium plans there is no discount information.)
Tricky questions (super-hard)
These questions are designed to have no answers in the knowledge base or there are multiple possible answers in the knowledge base. The chatbot should recognize this and inform the user about no answer or being not sure how to formulate an answer. Examples:
Can I get a refund on product IIEN#34?(There is no such product in knowledge base)
How can I terminate my subscription?
(Question does not mention whether this is B2C or B2B customer and what product he has → multiple correct answers, more information from user is needed)
What do you do if I steal your product?
(No answer in knowledge base, chatbot should answer “I don’t have information about our conduct in such situation”)
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Everyone has a chatbot. Do you want one, too?
Before you decide if you want to use a chatbot in your business, think carefully about what function it will perform. Adjust its operation to your expectations. Ensure good implementation . And then, test.
The results of the tests will give you clear information about what you should change in your chatbot and how to improve the operation of this tool. Remember that every chat requires human support - it is through such cooperation that it will fully realize the needs of your business.
If you want to consult your needs and confront them with AI solutions, contact us, we have great AI experts on our team.
