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Explore Athena AIIn recent years, Artificial Intelligence has demonstrated its potential to transform business operations across various industries and one of the most important applications in this context are AI Chatbots.
With technological advances in Artificial Intelligence taking place at an exponential rate, it is to be expected that the coming years will see AI chatbots reaching new feats and achieving an even greater degree of protagonism.
In recent years, Artificial Intelligence has demonstrated its potential to transform business operations across various industries. The impact of AI innovations has been particularly notable in the customer engagement landscape. Numerous features of Call Centre AI have proven highly beneficial, contributing significantly to advancements in customer engagement, especially within call centre environments, and one of the most important applications in this context is the AI Chatbot. An AI chatbot is a computer program that uses Artificial Intelligence to simulate human-like conversations, providing automated responses and assistance based on user input.
In other articles, we’ve covered what Artificial Intelligence means for the customer service landscape in general, how it fits into the context of Customer Service Automation, and how AI can impact call centres in particular. We have also discussed the significance of chabots exploring their definition as well as the different types of chatbots available and what chatbots mean for contact centres.
In this article, we’ll focus on Artificial Intelligence Chatbots. We’ll get into detail about what makes them different from other types of chatbots, what types of technologies they use, how exactly they can benefit businesses, and how businesses will find an indispensable asset for their CX strategies in the years to come.
Let’s start with the basics: what is an AI Chatbot?
Previously, we have defined a chatbot like this: a chatbot is a computer program designed to simulate and decipher human conversation, encompassing both written and spoken interactions. This technology facilitates seamless communication between individuals and digital devices, creating an interface that mimics human-like interaction.
Naturally, we talk about an AI chatbot when we refer to one of those programs that uses Artificial Intelligence. But what does that mean exactly? Don’t all chatbots use Artificial Intelligence?
The short answer to that question is no, they don’t. The long answer is a little more complex than that.
The roots of chatbots are closely tied to a fundamental challenge in Artificial Intelligence: the Turing test. Proposed in 1950 by Alan Turing, regarded as the “father of AI,” this thought experiment serves as a criterion for determining machine intelligence. Simply put, a machine is considered intelligent if, during a coherent conversation, a human agent cannot discern that they are interacting with a machine.
The question of programming a convincing chatbot isn’t just a contemporary concern for AI researchers or business professionals in customer service—it’s a foundational aspect of AI history and discipline. One of the earliest attempts to pass the Turing test was by MIT professor Joseph Weizenbaum in 1966, who created ELIZA, an early Natural Language Processing (NLP) program simulating conversations between a psychotherapist (the program) and a client (the user).
ELIZA’s operation relied on simple algorithms recognizing specific words or phrases in user input and responding with pre-programmed replies. For instance, if the user referenced ‘MOTHER,’ ELIZA would respond with ‘TELL ME MORE ABOUT YOUR FAMILY.’
Despite its status as one of the first chatbots, conversations with ELIZA revealed repetitive patterns, indicating it couldn’t convincingly pass the Turing test. However, its impact was significant, as some believed it possessed human-like feelings and could aid individuals, particularly those with psychological concerns. Despite these perceptions, ELIZA’s limitations were apparent to those with knowledge of computer science and psychology, preventing it from passing the Turing test. But that isn’t all.
ELIZA would be what we would now call a keyword recognition-based chatbot; it’s one of the simplest types of chatbots around and today, ELIZA wouldn’t even be considered to involve any AI at all. Chatbots like ELIZA are tailored for specific purposes, concentrating on executing a defined function.
Proficient in handling uncomplicated inquiries like questions about business hours or straightforward transactions with limited variables, their conversational capabilities, despite integrating NLP, remain fundamentally restricted. They don’t fully understand context or nuance and, most importantly, they don’t learn from past interactions.
On the other hand, an AI chatbot is characterised by their use of Conversational AI: this serves as a comprehensive term encompassing various technologies and systems intended to facilitate natural language interactions between humans and computers. Let’s go through some of them.
Natural Language Processing (NLP) is a pivotal component in the realm of AI chatbots, essential in their ability to comprehend and respond to human language. NLP equips chatbots with the capability to understand the intricacies of language, including syntax, semantics, and context.
Through advanced algorithms, an AI chatbot employing NLP can interpret user input, discern the underlying intent, and extract relevant information from the conversation. This technology goes beyond simple keyword recognition, enabling chatbots to engage in more sophisticated and contextually aware conversations.
NLP is instrumental in bridging the gap between machine and human communication, allowing chatbots to not only recognize explicit commands but also grasp the subtleties and nuances inherent in natural language. The integration of NLP into AI chatbots contributes significantly to their effectiveness in delivering personalised and meaningful interactions, ultimately enhancing the overall user experience.
Some technologies, applications or functionalities that fall under the broader category of NLP are:
Automatic Speech Recognition, also known as ASR, is a fundamental process that entails the conversion of spoken language into text, playing a pivotal role in speech analytics. This technology involves accurately transcribing spoken words into written form, facilitating further computational understanding.
A key subprocess of ASR is known as speech tagging. This specialised step enables computers to dissect spoken language, incorporating essential contextual elements such as accents or other attributes inherent in speech. Speech tagging adds a layer of depth to the analysis, enhancing the system’s ability to interpret and process spoken input with greater accuracy and nuance.
Within human speech, a single word often carries multiple meanings. Word sense disambiguation is a semantic analysis process designed to choose the most fitting interpretation for a word depending on its context. This technique proves particularly useful in discerning whether a word operates as a verb or a pronoun, enhancing precision in language understanding.
Named Entity Recognition (NER) distinguishes words and phrases by categorising them as specific entities, such as identifying “James” as a person’s name or “United Kingdom” as the name of a country.
Sentiment analysis involves analysing text data to determine the emotional tone expressed, typically classifying it as positive, negative, or neutral. Using natural language processing and machine learning, sentiment analysis gauges the subjective sentiment within written content, providing insights into opinions, attitudes, or emotions conveyed in the text.
Machine Learning empowers an AI chatbot by enabling it to learn from user interactions. This process involves the chatbot analysing patterns in data, identifying trends, and adjusting its responses accordingly. ML allows chatbots to predict user needs, leading to more accurate and contextually relevant answers.
This adaptability without explicit programming is crucial for engaging in dynamic conversations and accommodating a wide range of user inputs. Essentially, ML equips chatbots with the ability to continuously improve and evolve based on user interactions, enhancing their overall conversational capabilities.
Deep Learning takes the AI chatbot to a more advanced level by allowing them to understand intricate patterns within user interactions. Through neural networks, DL enables chatbots to recognize complex relationships and dependencies, resulting in a higher level of accuracy.
This advanced understanding is particularly beneficial in capturing subtle nuances in user queries and responses. By leveraging deep learning techniques, chatbots become more sophisticated in their ability to comprehend and generate contextually appropriate answers. This contributes to a more refined and natural conversational experience for users.
Natural Language Understanding is a crucial aspect that empowers chatbots to interpret and derive meaning from human language in a contextually aware manner. NLU involves the chatbot understanding not only the literal meaning of words but also considering the context in which they are used.
By integrating NLU into an AI chatbot, these systems can discern user intents, grasp the implications of specific phrases or expressions, and consider the broader context of the conversation. This depth of understanding enables chatbots to provide more relevant, context-aware, and human-like responses. Ultimately, NLU enhances the overall conversational experience, making interactions with AI chatbots more meaningful and effective.
Hopefully, by now you’ll have a more complete understanding of how an AI Chatbot works and what technology sets them apart from other types of chatbots. Now, you might be wondering something? What are the benefits of AI Chatbots for businesses? Do all businesses need AI chatbots, or can some do with just non-intelligent chatbots? We’ll address this topic in the next section.
Can all businesses benefit from Artificial Intelligence chatbots? That’s a question asked by many contact centre owners or customer service department managers and decision makers. As we have explained in other articles, the use of chatbots for customer facing operations, from customer service to sales, presents an array of undeniable benefits, some of which are:
But do you need Artificial Intelligence to reap those benefits? Some business owners, managers, and decision-makers might think you don’t. After all, as long as it’s available 24/7, addresses simple customer queries freeing live agents from them, and does a good enough job at it, that’s already quite a feat isn’t it? Unfortunately, it’s not as simple as that.
In a Userlike study, 60% of respondents expressed a preference for engaging with a live agent over using a chatbot, citing concerns about the chatbot’s potential inability to accurately comprehend their queries. However, other studies reveal that only 10% of customers are dissatisfied after interacting with chatbots, with almost 70% feeling satisfied and about 20% feeling neutral. What does this tell us?
Naturally, customers want their questions to be understood and adequately answered; but they also want that to happen quickly. Most customers, when faced with the choice of whether to wait 15 minutes to talk to an agent or have it solved by a chatbot straight away, would choose the latter. And as you know, immediacy and availability is one of the strongest advantages of implementing chatbots.
So, what should we take away from this? As we have mentioned first, the main reservation customers have about chatbots is the doubt about whether they’ll be able to understand their queries correctly. However, if they do, they’re more than happy to talk to a chatbot, and they might even prefer it over speaking with a live agent if that means their problem will be solved immediately.
In conclusion: the more accurate and fine-tuned a chatbot is in its answers and its capacity to learn from past interactions, anticipating how future conversations might go before they even happen, the most positive impact it will have on a business’ results, both in terms of customer satisfaction, of productivity, of reduced costs, and ultimately, in its bottom line. This all depends on the level of sophistication of the chatbot in question: and that’s why Artificial Intelligence chatbots are occupying their position at the forefront of modern business.
With technological advances in Artificial Intelligence taking place at an exponential rate, it is to be expected that the coming years will see AI chatbots reaching new feats and achieving an even greater degree of protagonism. Modern AI customer service chatbots like Athena AI Agent have transformed from simple scripted tools into sophisticated, generative AI-powered agents. They manage complex conversations, integrate with backend systems, and adapt over time, autonomously resolving over 80% of queries while forwarding complex cases to human agents when necessary.
NLP (Natural Language Processing)
Shaping the Future of Customer Experience.
