Chatbots popping up on web pages or available in messengers to answer basic questions and take orders have long ago become customary. Some of them are AI-powered, some aren't, but we all know they aren't almighty. However, with the advent of generative AI, businesses and customers can expect a huge boost in chatbot effectiveness.
In a 2023 survey of 350+ executives across the globe by the IBM Institute for Business Value, believe generative AI will be interacting directly with customers in the next two years. And, as Gartner predicted in 2022, by 2027, chatbots will become the primary customer service channel for approximately .
Here, we'll discuss the current state and future of chatbot technology and the differences between rule-based/conversational AI/gen AI chatbots/virtual assistants. Also, we'll talk you through use cases across industries and show some decent live examples of AI-powered chatbots.
Table of Content
1. Four Major Types of Chatbots
In a broad sense, a chatbot is a computer program mimicking human conversation when a person interacts with it: asks questions or gives tasks. Chatbots can be integrated into websites, social media, business apps, and devices.
Not all chatbots are artificial intelligence chatbots; some are based on simpler algorithms. Others, like decision tree-based chatbots, already utilize machine learning but still aren't deemed advanced. Finally, more sophisticated chatbots actively employ different subsets of AI, such as natural language processing (NLP), natural language understanding (NLU), machine learning (ML), deep learning (DL), reinforcement learning (RL), sentiment analysis, speech recognition, computer vision, robotic process automation (RPA), and more.
For convenience, we'd single out four groups of chatbots: rule-based chatbots, conversational AI chatbots, generative AI chatbots, and virtual agents/assistants.
1.1 Rule-based Chatbots
These are chatbots fully preprogrammed by developers, with no AI usage. A rule-based chatbot follows a ready-made script, a simple if-then logic; hence, it's quite limited in what inputs it can "understand" and process. There's no element of learning, too.
Rule-based chatbots are easy to build, but because they're so rigid, there are few use cases, all having to do with the automation of the most routine tasks. A simple FAQ chatbot, a bot to book appointments, track orders, or register for events, especially if it has a menu/options to choose from, is most likely rule-based. However, some of the more modern rule-based chatbots incorporate NLP and ML techniques to make conversations more natural and answers pertinent.
1.2 Conversational AI Chatbots
These are the most popular chatbots as of now. An AI chatbot uses NLP and NLU to correctly identify and interpret the input, user's intent, and context to then answer in a similar humane, conversational manner. ML and DL help these chatbots improve their response relevance over time as they continuously learn from interactions with users.
As a result, a conversational AI chatbot has a far better understanding of open-ended questions/free-form text inputs, user sentiment, and context (even over multiple conversations). So, when it comes to use cases, these modern chatbots can handle all the stuff rule-based chatbots do, plus more complex tasks: for instance, give personalized recommendations based on user behavior (past interactions, purchases, and browsing history). At the same time, many traditional AI chatbots still rely on pre-defined rules, scripts, and datasets to some extent.
1.3 Generative AI Chatbots
Being an emerging trend now, they're the next generation of AI chatbots. A generative AI chatbot is a level-up in comparison to a conventional one, as it's powered by an advanced neural network (e.g., a large language model (LLM) or text-to-image model), which utilizes a deep learning architecture type called transformer (invented relatively recently, in 2017). Read our latest article to learn more about AI model types and for your business purposes.
Gen AI doesn't rely on predefined responses or patterns at all. LLMs like ChatGPT (Generative pre-trained transformer) are trained on ginormous amounts of data, which allows them to create unique outputs in a snap. Plus, all the capabilities of "regular" AI chatbots are enhanced in gen AI ones, including translating languages, maintaining context awareness, keeping natural, empathetic communication, giving answers or issue solutions, and self-learning.
1.4 Virtual Assistants
Alexa, Siri, and a couple more digital products fall into this category. Relying primarily on NLP, NLU, and ML/DL to recognize speech and give meaningful audio responses, they also utilize robotic process automation (RPA) to act on user commands: open apps, browse, set alarm clocks, and so on.
Reinforcing virtual agents with gen AI is also already a reality: for instance, Apple announced the in 2024.
2. AI Chatbot Major Use Cases, Benefits, & Challenges
AI tools (from AI bots to AI platforms) now permeate almost all business processes, automating and optimizing both internal and customer-facing operations. When it comes to typical AI chatbot use cases, their number keeps growing, especially since the invention of generative AI. A comprehensive list of AI chatbot applications would take well beyond several dozen points, but let's concentrate on the most common ones.
- Customer service: conversational AI chatbots provide round-the-clock support in cases like site navigation, order status checks, issue resolution, and so forth.
- Content creation: gen AI chatbots help different teams to compose customer service messages, reclamation feedback emails, product descriptions, and whatnot.
- Personalized recommendations and up/down/cross-selling: an advanced AI chatbot platform with robust predictive intelligence can offer prospects relevant items during customer interactions.
- Product/service promotion: commerce chatbots can show a user hot deals or special offers based on the ongoing conversation.
- Appointment reservation: AI chatbots can ask all the needed info and book a visit to a doctor, yoga class, or massage session.
- Lead generation: bots can collect user data (especially emails) making it possible to reach back to prospects later.
You might need several AI products to facilitate different workflows. When a company follows a smart AI strategy, not relying solely on conventional approaches, it can seriously benefit from technological advances. Here are some advantages of the best AI chatbots.
- Enhanced customer support. Having an AI chatbot alongside human agents ensures 24/7/365 coverage of customer care cases.
- Accelerated workflows. Quality AI chatbot software expedites numerous processes, like answering FAQs, tracking orders, creating customer support/marketing content, and more.
- Boosted customer engagement. AI chatbots often aid in gathering user contact info, which can then be tapped into by sales or service teams.
- Cut/saved costs. Partial delegation of tasks to artificial intelligence allows for handling the rising volume of interactions or other tasks without expanding a team.
- Less staff burnout. Automating a great deal of tedious, repetitive, mechanical operations allows teams to focus on more complex, creative, and, hence, fascinating tasks.
- Better personalization. Based on site or app visitors' behavior and conversation, an AI chatbot can recommend suitable goods/services.
There's no doubt about the usefulness of artificial intelligence to businesses, employees, and customers. Yet, AI technologies still have limitations functioning-wise and carry some risks worth bearing in mind. Let's list some of them.
- AI chatbots can't replace humans (at least yet). AI is actively evolving now, but even the most potent models, let alone less advanced conversational AI chatbots, can't resolve complex tasks without human intervention. That's why the feature to escalate cases to human agents in customer service chatbots is so critical. AI is a helper for people, not their substitute.
- AI requires tons of quality data. Chatbots, especially generative AI ones, need as much data as possible in order to perform well. Getting enough good data, in turn, can be challenging and pose security risks.
- AI may jeopardize company and customer data. To reduce risks like data leakages as well as privacy policy violations or intellectual property/local/international law breaches, brands should choose and train AI models carefully, following well-established rules.
- AI chatbots can be misleading or hallucinate. Even with enough data, AI bots still often generate irrelevant, incorrect, or made-up responses and might not fully abide by the brand's tone of voice. So, human supervision remains vital to prevent ruined customer experiences and mistakes in internal operations.
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3. How to Build Business AI Chatbots?
So, what does it take to deploy your own chatbot for customer service interactions, messaging apps, or/and workflow automation? Let's consider two major ways to build an AI chatbot: with out-of-the-box software or custom development.
3.1 Ready-made AI Chatbots
There are many AI chatbot vendors (Google Dialogflow, IBM Watson Assistant, Zendesk, Drift, Tidio, and so on) offering pre-built products that are fairly simple implementation-wise. Some chatbot platforms state it takes just a few clicks to get a chatbot up and running (no-code options). Others require minimal to moderate coding to integrate and customize the solution (low-code options).
And what about training? Specialized platforms pre-train their models on domain-specific data (conversations and knowledge bases) so that an AI chatbot works as expected. Plus, vendors provide topic libraries to improve future AI-user interactions. More and more companies are shifting towards offering more gen AI capabilities, either via GPT integrations or by training their own LLMs.
When it comes to the advantages of this option, it's relatively inexpensive and super quick to deploy and support. These are cloud-based SaaS platforms available by subscription. It eliminates the need to manage the entire infrastructure: training, updates, maintenance, and scaling are on the provider.
Speaking of shortcomings, off-the-shelf solutions can't be as tailored to specific business processes and tasks as custom-made chatbots. Plus, dependency on vendor support can backfire: a client has no control over the software, so in case of an emergency on the vendor's side, they just have to wait for the issue to be fixed.
3.2 Custom AI Chatbot Development
Basically, it's about hiring a dedicated team or agency for end-to-end development: choosing an AI model and tech stack, development and training, integrations with the org's current tech environment and business applications, UI/UX design, testing, and, finally, deployment.
AI giants offer APIs to create advanced gen AI chatbots based on powerful LLMs like OpenAI's GPTs, Google's PaLM, Anthropic's Claude, IBM Watson, and more. Besides, there are some open-source options: in libraries like Hugging Face Transformers, developers can find pre-trained models (GPT-2, BERT, and others) and fine-tune them for concrete tasks.
Fine-tuning is a go-to option for many businesses as it implies customizing a pre-trained model using smaller, industry-specific datasets to adapt a chatbot to its future application, be it customer service, content generation, text/audio translation, smart recommendations, or something else.
Another way is to create an AI chatbot from scratch (go through the building, training, and deploying stages) with open-source deep learning frameworks like TensorFlow or PyTorch. This path allows the client to get a solution packed with unique custom features, trained on domain-specific data, fully geared for its use cases, and with full control over the model architecture and sensitive data. However, it's a way more labor- and computationally intensive process than fine-tuning existing LLMs or multimodal gen AI models.
4. Business Chatbots Powered by Artificial Intelligence: Four Examples
Now, when we've explored how chatbots work for different businesses in theory, let's see some real-life cases. We've chosen four modern AI chatbot examples across industries, from eCommerce to customer relationship management to healthcare.
4.1 Klarna’s AI Assistant
Klarna, a Swedish fintech company, has recently in partnership with OpenAI to handle a larger amount of customer queries from all over the world faster. The AI assistant keeps a natural conversation in multiple languages about the service itself, payments, refunds, and returns, demonstrating a big leap from the older generation of conversational chatbots and gen AI-powered ones.
As the company states, the chatbot processes about 2/3 of incoming communications. The effectiveness of the AI assistant is also reflected in customer satisfaction scores, which are on par with live agents.
4.2 Salesforce's Einstein
Salesforce collaborated with OpenAI to , SF's suite of AI technologies and features, with the capabilities of ChatGPT. Now, it's embedded in all Clouds (Sales Cloud, Service Cloud, Marketing Cloud, Commerce Cloud, and others) to help generate various types of content (e.g., chat replies, emails, case summaries) and code, increasing teams' productivity and elevating customer experiences.
To delve deeper into the capabilities of Salesforce CRM analytics and understand how it can transform your business strategies, check out our detailed .
4.3 Buoy's AI Health Assistant
Buoy, a healthcare consultancy based in the US, offers its built in collaboration with medics and based on medical research data. It's akin to a telemedicine session: the tool allows you to fill out a questionnaire, but the questions there change depending on each previous user's input to show the most relevant options. At the end, it displays matching conditions with brief explanations and recommendations on further steps. Plus, it prompts suitable medical centers for treatment.
4.4 H&M's Chatbot
It's an example of traditional chatbots helping to check an order status, answer questions (basic ones), and having an option to hand the dialog over to a consultant if there's a need for human interaction. The overall chatbot experience is quite nice, yet this customer service chatbot isn't multilingual (for instance, if shopping from a Polish version of the app, the chatbot won't understand inputs in English).
Make AI Chatbots Work for Your Business
AI technology is constantly evolving, so are the products employing it. After simple rule-based bots in Facebook Messenger to take pizza orders came chatbots understanding human language and giving helpful conversational responses. Processing simple user queries was mastered by chatbots a long ago, and now AI tools leverage large language models to respond appropriately to more complex inputs. Gen-AI-powered chatbots and voice assistants are getting better by the month.
That's why it's high time to create the best AI chatbot for your business. We at Onilab offer to create AI chatbots based on cutting-edge ChatGPT models. Whatever industry you represent (eCommerce, healthcare, insurance, trading, real estate, etc.) and whatever use case you have (automate tasks, ensure quick customer service, reinforce marketing or improve risk management), we're in.