Everything You Need to Know About AI Chatbots in 2023
Table of contents
What is a Chatbot?
A chatbot refers to a program designed to comprehend and respond to user inputs, delivering information, assistance, or accomplishing tasks through predefined rules or AI algorithms. They find applications in diverse areas, serving as customer support chatbots and messaging platform bots.
The creation of a chatbot involves two distinct facets:
The creative aspect, encompassing chatbot design, involves tasks such as identifying the target audience, aligning the bot’s tone of voice, crafting responses, structuring conversation flows, and ensuring a positive user experience. It also involves designing the visual elements of the interface.
Conversely, the technical aspect, known as bot building, encompasses the programming of the designer’s flows to bring the bot to life. This includes ensuring an accurate understanding of user inputs, effective information processing, and providing suitable responses. Additionally, it involves establishing connections with external sources like databases and APIs.
In simple terms, bot designers play a crucial role in ensuring that a chatbot operates in line with its intended purpose, supporting customer service agents and aiding clients in achieving their objectives.
Rule-based vs AI Chatbots
Chatbots can be categorized into two types: rule-based and AI chatbots.
Rule-based chatbots operate without artificial intelligence. When interacting with such chatbots, they strictly follow a predefined path, adhering to human-made rules or a decision tree.
These chatbots prove effective in handling closed-ended questions, such as surveys, product feedback, or conference talk ratings. Additionally, they are cost-effective and simpler to implement compared to conversational AI chatbots. Although they utilize Natural Language Processing (NLP) to interpret user text input, their reliance on a rule engine makes them susceptible to challenges in navigating the complexities of human language. Implementing them as virtual assistants can also lead to user frustration when encountering limitations in the conversation flow.
AI chatbots leverage artificial intelligence, Machine Learning (ML), and Natural Language Processing (NLP) to replicate human-like conversation. These AI-driven chatbots improve their capabilities with each interaction, delivering more accurate and personalized responses. They demonstrate increased sophistication in extracting pertinent information from external sources, as facilitated by bot designers.
How Chatbots Work
There are two distinctions among key natural language processes:
- Natural Language Processing (NLP) serves as a comprehensive term for machines engaging with human language.
- Natural Language Understanding (NLU), a subset of NLP, entails a more advanced comprehension of language, involving the extraction of context and intent from inputs.
In essence, an interaction with an AI bot follows a four-step multi-turn loop:
- The user inputs information or poses questions in the chatbot.
- NLU analyzes the input, identifying intent and additional details such as data, location, or products.
- Dialog Management associates the intent with the appropriate segment of the conversation flow and accesses external systems or knowledge bases to retrieve relevant data.
- Natural Language Generation crafts a response based on the information gathered in Dialog Management and transmits it back to the user.
Why Businesses Need Chatbots
The presence of chatbots dates back to the 1960s, with continuous technological enhancements over decades enabling their expansion into new markets and engagement with diverse audiences.
The primary business use cases for most bots can be categorized into information-oriented and task-oriented chatbots.
As implied by the name, information-oriented chatbots are crafted to furnish users with answers to their inquiries. These chatbots excel in retrieving precise and pertinent FAQ responses sourced from databases, websites, or APIs. They can serve various purposes, such as:
- Interactive interfaces facilitating navigation through extensive user-facing knowledge bases.
- Virtual tour guides providing information about exhibits, offering historical facts, and responding to visitor questions.
- Health information providers addressing health-related queries, delivering guidance on symptoms, suggesting first aid procedures, and recommending home remedies.
Task-oriented chatbots exhibit a proactive nature, capable of executing specific tasks or actions, rendering them more transactional. Their primary objective is to guide users through predefined workflows or processes by understanding and responding to specific commands or intents associated with their assigned tasks.
This entails engaging in multi-turn conversations with users, utilizing Dialog Management, as discussed in the previous section. Moreover, this step-by-step guidance may involve accessing connected APIs to perform necessary actions, such as verifying users before divulging personal information or incorporating new data into the system.
Task-oriented chatbots excel in:
- Appointment scheduling, allowing users to check availability, schedule appointments, receive reminders, and manage rescheduling or cancellations.
- Order tracking, enabling buyers to monitor real-time status updates, obtain estimated delivery times, and seek details about their orders.
- Financial services, where conversational AI chatbots provide users with account information, conduct transactions, and address financial queries, particularly beneficial in banking and insurance institutions.
Challenges in Constructing Chatbots
The effectiveness of chatbots hinges on the quality of the dataset upon which they are constructed. If the data is biased, corrupted, or inaccurate in any way, the chatbot may mislead or discriminate against users, resulting in frustration and an elevated churn rate.
To enhance your chatbot’s comprehension capabilities, it is essential to utilize datasets encompassing a variety of sentence structures, synonyms, and real-world examples that capture the subtleties of language. This underscores the significance of generative AI in expediting the bot-building process, as elaborated below. Fortunately, our conversational AI platform at Born Digital incorporates NLU models and generative AI integration.
Even with meticulously curated datasets, errors may still arise. As a bot designer, it is crucial to continually scrutinize and pinpoint areas where chatbots make mistakes, identifying common errors to enhance the precision of intent recognition.
Navigating the intricacies of conversation flows poses another formidable challenge in bot design. Employing a low-code visual flow builder proves invaluable in simplifying this process and making it more intuitive.
Lastly, gaining client trust and encouraging the adoption of chatbots present additional hurdles. Historical reservations are understandable, given that, until recently, companies had access to only robotic-sounding bots with low intent detection accuracy. In the current landscape, where advanced conversational AI chatbots are readily available, presenting users with an uninspiring interface would be a missed opportunity and detrimental to business. This sentiment aligns with the findings of the Uberall report, which highlighted the desire for chatbots to exhibit a more “human”-sounding, natural conversation.
The Evolution of Chatbots through Generative AI
The advancement of chatbots now includes the incorporation of generative AI, leveraging Large Language Models (LLMs) with ChatGPT being a prominent example. This widely used tool has the capacity to access all available internet information, enabling it to generate responses to user queries, much like traditional chatbots.
While generative AI tools are beneficial for process automation and expediting NLU model training, they may not be optimal for business applications. Despite the impressive scope of using the entire internet as a knowledge base, there are drawbacks, as evidenced by instances of ChatGPT providing inaccurate or contextually inappropriate answers. Integrating such tools into business chatbots relinquishes control over answers, data storage, and security.
Therefore, it’s important that AI bots undergo training on specific and carefully curated datasets. These datasets must be self-contained, ensuring the security of sensitive information, a critical consideration in sectors such as healthcare and finance. However, it’s worth noting that, as mentioned earlier, ChatGPT remains an excellent tool for expediting the bot design process.
Generative AI showcases its capabilities through various applications:
- Similar Phrases: ChatGPT can instantly generate ten alternative versions of a sentence, such as “Tell me how to access my bank account number.” These examples aid in training the NLU model.
- Synonyms: Ensuring the model comprehends different user expressions, for instance, in discussions about credit cards.
- Conversation Flows: ChatGPT can be tasked with creating a flow initiated by a user asking, “Where is your closest branch?”
- Bot Responses: Particularly useful for quick ideation on non-information-specific answers, assisting in defining a brand-matching tone of voice.
Research conducted indicates that incorporating generative AI in training NLU models can accelerate the bot-building process by up to 60%.
What's the future of AI Chatbots?
The chatbot industry is rapidly growing, projected to exceed $994 million with a remarkable annual increase of around $200 million and a 22% compound annual growth rate. Businesses, particularly smaller ones, are integrating chatbots for efficient customer connections. Future trends indicate that chatbots will become more human-like, driven by advancements in NLP and ML, offering natural interactions.
Deep customer insights will guide chatbot behavior, and they will play a central role in reshaping contact centers, potentially running them autonomously. Voice bots equipped with voice capabilities will become mainstream. Businesses will prioritize chatbots for exceptional customer experiences, leveraging messaging platforms, facilitating automated payments, and utilizing social media engagement.
The evolving landscape of chatbots extends beyond increased adoption of generative AI and AI-assisted tools. A notable trend is the transition of conversational AI bots towards becoming team-agnostic. This shift implies that companies will move away from creating distinct chatbots for marketing, sales, and customer service. Instead, the focus will be on developing a singular virtual assistant that serves as an extension of their brand.
As 2023 concludes, the era of chatbots is just beginning, promising further innovations and integration into daily life.
Selecting the Right Conversational AI Platform
Now that you have gained insights into what a chatbot is, its types, and use cases, you can confidently take your initial steps with Born Digital!