Artificial Intelligence is old news. Generative AI is all the rage now. Since the advent of Generative AI-based platforms such as ChatGPT and Dall.E, our collective observation about Artificial Intelligence has transitioned from “this doesn’t concern us” to “interesting”.
There has been much said, written, and projected about Artificial Intelligence. The idea remains pretty much the same i.e, will AI overtake any of the millions of tasks that human beings tend to do on a day to day basis. And so far, the answer is yes.
Nevertheless, now it is not so much about whether AI will overtake us but about what can AI do for us (and at what level).
It goes without saying that it will take decades before Artificial Intelligence is smart enough to formulate overtaking human beings in any field. In the meantime, our primary concern should be (and has been) to make the most out of artificial intelligence.
To elucidate this point better, let’s take the example of Generative AI. By all means, Generative AI seems like the next big development in this route.
Generative AI enables us to quickly generate brand new content based on a variety of inputs such as text, image, voice, animation, and other sorts of data.
But how does it work?
Generative AI is the nascent stage in the development of Artificial Intelligence where natural language processing has enabled tools like ChatGPT and other generative-AI based platforms to create original content.
Now there are layers to the level of details that a generative-AI platform could render and it all depends on the kind of training it has been subjected to.
The generative aspect in the generative AI has been possible because of GAN; Generative Adversarial Network.
What is GAN? It’s a Machine Learning model where two neural networks compete with each other through deep learning methods to become more accurate in their predictions.
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Take a look at the below screenshot for a simpler understanding of GAN:
Alright, so this is what Generative AI is. Now, we can finally come to the topic at hand i.e, Generative-AI chatbots.
What is Generative-AI Chatbot?
Generative-AI chatbots are basically smarter versions of traditional chatbots that not only understand and interpret the inputs but also create cohesive and relevant output for the queries.
But chatbots haven’t always been the same. When the first chatbot was invented back in 1966, it used pattern matching and substitution methods to simulate conversations.
Ofcourse, there have been huge developments in the chatbot industry since then, especially in regards to CX. The true development came to the chatbot industry when Natural Language Processing (NLP) and Machine Learning (ML) were factored in.
Chatbots like Cortana, Siri, Alexa ushered in a new revolution in how we interact with chatbots. These chatbots were developed on extensive training modules, fed on large datasets, and made to analyze patterns so they could deliver contextually relevant responses.
Now is the time for Generative-AI chatbots. These are trained on generative pre-trained transformers. Unlike the traditional bots, the new generative-AI chatbots employ intelligent algorithms and large language processing capabilities to deliver more accurate and operationally efficient responses.
Rise in Popularity: Script to Spontaneity
It is no wonder that the idea of Generative-AI chatbots has caught up and is now one of the most sought after approaches to the chatbot industry.
In the last decade, an increasing number of industries and organizations have adopted or are looking to adapt the new Generative AI-based tools. Chatbots are at the forefront of this revolution.
Compared to the older models, the Gen-AI chatbots offer a number of advantages and thus they are in popular demand. Here are some of the advantages Gen-AI chatbots offer over their traditional counterparts:
- Boosted creativity through the generation of unique and contextually fitting replies, thereby cultivating more captivating and inventive dialogues with users.
- Elevated contextual comprehension, as generative AI models excel in analyzing conversation contexts comprehensively, resulting in more precise and contextually fitting responses.
- Enhanced conversational fluidity, as generative AI chatbots adeptly sustain a natural conversational flow, creating interactions with users that feel remarkably human-like and effortlessly seamless.
According to Gatner, AI-powered chatbots can become the primary customer support channel for a quarter for all companies by 2027. This stat only goes to show that we are right in the middle of a chatbot revolution.
Questioning its Longevity: Fab or Fad?
But is all of this worth it? Or is it just another fleeting trend? Are Generative-AI chatbots a fad or are they here to stay?
Let’s find out:
The Fabulous Aspects
Firstly, let’s go through the fabulous aspects of Generative-AI chatbots.
Natural Language Understanding
Generative-AI chatbots boast natural language understanding, meaning they can understand the intent behind a query. NLU allows the chatbots to understand and interpret an input as a human would and respond as closely to a human being as possible.
Here are a few actionable implementations of NLU:
- Paraphrasing and Natural Language Inference: NLI is a process in natural language processing that determines the logical connection between two bodies of text.
- Dialogue Agent: It is used to keep a track of the current state of conversation.
- Semantic Parsing: Semantic parsing is the process of helping understand the intent behind a given query.
- Answering Queries: Answers questions in a natural, human-like way.
- Analyzing Sentiment: Diagnoses texts to understand the sentiment behind it and respond accordingly.
- Summarizing: Shortening the message and highlighting the main points.
Improved Conversational Skills
It goes without saying that Generative-AI chatbots offer much more refined and relatable conversational capabilities compared to the traditional chatbots. You can hold conversations with a Generative-AI chatbot on pretty much any topic as long as it has been fed and trained on the right dataset to answer your questions within context.
Contextual Understanding
Contextual understanding is another important aspect of a Generative-AI chatbot. In order to keep the context in the equation, Generative-AI chatbots keep track of the conversation history, including both the queries raised by the user and its own responses.
Personalization
Personalization is at the crux of customer experience. Whether you are encouraging your customers to make a purchase or trying to console them after an order went awry, you need to be able to modify your message to cater to that particular customer only.
Personalization is probably the most significant driving factor behind the rise of Generative AI chatbots. At the end of the day, the goal of any chatbot or other interactive tool is to make the person on the other side feel understood.
The Faddish Concerns
Despite the number of advantages mentioned above, there are a few concerns that the chatbots are yet to address.
Ethical Considerations
First and foremost are the ethical considerations and compliance that must be met before a chatbot is deployed. Chatbots, in themselves, raise a number of ethical concerns that could range from invading privacy to nonconsensual collection of personal data. Let alone an AI-powered chatbot that understands the context is smart enough to analyze sentiment through words.
⦿ Personal Prejudices
Firstly, Generative-AI chatbots are fed on a certain data and they continue to learn through the queries and comments from everyone who interacts with them. Not to mention, the responses are also pre-programmed and by individuals with their own beliefs and tendencies, which could itself lead to a biased approach.
An article from TechCrunch mentions that the first question every user should ask about a chatbot is “Does this bot serve me or the service provider?”, pertaining to whose interests lie at the core of the bot’s actions.
“Will the food-ordering bot recommend the pricey/low-quality items or the best-priced and quality food? Will the HR bot serve me or the company? Will the insurance bot try to facilitate me claiming the money or try to prevent it?”
⦿ Transparency
Another important factor, which could also be counterintuitive to the application of a chatbot, is transparency. Users on the other side should always be made wary when they are talking to chatbot. This could turn around and defeat the entire purpose of deploying an AI-chatbot that could imitate human-like interactions. Because at the end of the day, no customer wants to talk to a bot about their concerns, no matter how smart or empathetic they might come across.
⦿ Privacy Concerns
Consider this scenario. You are talking to a very friendly customer representative over text. They seem to know and understand you and you are feeling free to divulge some of your personal information with them. However, at the end of the chat you realize that you were conversing with a bot and all the information you have provided shall be used to target you with unsolicited ads.
This is a major privacy concern and one to be wary of when conversing with an AI-powered chatbot. Chatbots should only collect the data that the user allows and that too with their consent. Moreover, developers must also ensure that chatbots don’t reveal a user’s personal data.
Technical Limitations
There are a number of technical limitations that come with chatbots, no matter how advanced. Firstly, chatbots can only imitate human-like conversations. They lack the emotional depth and intelligence to answer sensitive queries with relevant responses without hurting or affecting the human being on the other side.
Moreover, chatbots are also quite limited in terms of creativity. Chatbots lack out-of-the-box thinking and can therefore come out as blunt or even rude at times.
Lastly, even the most advanced chatbots at the end of the day are just that, bots. This means they are liable to make mistakes that could be harmful for both the customer and the company as a whole. Consider the ChatGPT debacle that came up a month ago. Researchers unveiled that ChatGPT was revealing personal information of the user that it was fed on including names, email addresses, and phone numbers.
Handling Complex Queries
According to a recent Forester survey for Cyrara, chatbots have garnered a rating of 6.4/10, with certain aspects of the technology being really helpful while others igniting frustration and disappointment.
A 6.4 rating means there is a lot of room for improvement and most of these “need to be made” improvements come from chatbots inability to handle complex queries. Respondents of the survey report that they have received responses that make no sense.
Nearly 40 percent of the respondents described their interaction with a chatbot as negative. Such experiences are not only keeping the customers from trusting chatbots but also hurting brands’ retention and overall reputation.
Case Studies
Organizations across the globe are increasingly adopting Generative-AI. And rightfully so. Chatbots are the starting point of contact for customers and they use it for a number of reasons including looking for products, needing to resolve a query outside of office hours, and just to get a faster response.
Let’s take a look at some of the best use cases of Generative-AI chatbots in the current age of skepticism against such technology.
Expedia X ChatGPT : A Traveling Solution Platform is Utilizing Generative AI to Provide Ultimate Customer Service
Expedia is one of the first e-commerce platforms to incorporate ChatGPT into its app. The reason? In order to help customers not feel overwhelmed with the number of choices and create personalized travel itineraries for every customer.
Customers can now log into the Expedia app and converse with the app about travel arrangements. The chatbot will help travelers find the right accommodations, make proper arrangements, and create itineraries as per their particular needs and preferences.
With Generative-AI chatbot, Expedia is able to:
- Deliver handy lists of the places that have been discussed in the conversation
- Give a summary of the conversation that it has with customers
- Talk about rewards and discounts as well as provide personalized recommendations
- Predict price surges and suggest the best times to make the booking
Gen-AI ServiceNow : ServiceNow is Employing Generative AI to Encourage Self Service
ServiceNow introduced its “Now Assist” Generative-AI solution across its services in 2023 and has since been the pioneer of AI in customer service. The move comes as an expansion for the company’s Now Platform which includes ITSM, CRM, HR service delivery, and more.
Now Assist helps employees, agents, and developers make use of Generative AI in their day to day tasks to build more powerful workflows and bring about efficiency that would otherwise be impossible.
Here’s how ServiceNow is bringing the ease of AI across its operational dependencies:
- Streamlining ITSM operations by providing comprehensive summaries of incidents and real-time interactions with virtual agents
- Facilitating the implementation of incident management best practices and quickening resolutions
- Enhancing self-service by providing access to quick resources and expediting query resolution
Future Outlook
Generative AI is only beginning to gain traction. By all means, this is the nascent stage of an AI revolution that will soon take over the entire world and be incorporated in our daily tasks. Let’s see how.
Evolving AI Capabilities
AI is not new. It is a decade old concept that has taken various forms and shapes over the years. The AI we are able to use now has come a long way and still has a long way to go before it could come closer to the competence of a five year old human being. You see, with Artificial Intelligence, the best thing that we can hope to do is mimic human intelligence.
Recently, if you have been catching up on Artificial Intelligence news, you must have come across the term AGI; Artificial General Intelligence. AGI is a field of research that aims to bring Artificial Intelligence on par with human intelligence. With AGI, machines will be able to take and perform tasks that they have not been trained on.
The current state of AI is that of dependability. All the Artificial Intelligence we have today has a set parameter. An AI-based machine or tool that has been trained on certain tasks will not be able to perform any task other than what it has been fed and trained on. AGI aims to make Artificial Intelligence self-sustaining.
AGI, or Artificial General Intelligence, is a conceptual endeavor aimed at creating AI systems with autonomous self-control, a substantial level of self-awareness, and the capability to acquire new skills through learning.
So, all in all, the next move for Artificial Intelligence is to become self-aware and self-driven. And when it does, we hope it is able to run a contact center with little to no margins of failure.
Conclusion
There you have it, a brief guide into Generative AI and Generative AI chatbots and an analysis on their real-world applications and impact. We hope you got a few insights out of the piece of content about adapting Gen-AI chatbots for your own requirements. If you have any questions that you would like to be answered, please write to us at marketing@novelvox.com
Cheers!
