Generative AI in Customer Service - The Ultimate Guide

In the age of artificial intelligence, if we say that customer support is not evolving at a pace that has not been seen in recent years, we would be lying. Customer support as we know it is over. ChatGPT, Bard, LLama and other Generative AI tools powered by Large Language Models are going to make a significant impact on most of the spheres of the modern business, and Customer support is no different.

Generative AI uses the power of advanced algorithms, which are based on neural networks, to generate new content in the form of text, images, videos or entire experiences. Generative AI deviates from traditional AI since it draws its knowledge from a vast dataset that has been fed to it, which gives it the ability to give contextually relevant answers.

In a recent interview, it was the visionary Bill Gates who said that, “ In the near future, anyone who's online will be able to have a personal assistant powered by artificial intelligence that's far beyond today's technology."

With this context, we are about to explore the role that Generative AI is going to play in customer service and support in the coming years. From extreme customer personalization to content creation to real-time issue resolution and to voice based support, Generative AI is set to revolutionize how businesses interact with their customers - we will cover all of this and more in the upcoming sections.

Let's start by understanding how Generative AI actually works -

Artificial intelligence is a field of science that has slowly evolved from being a part of science fiction to becoming part of our everyday lives in a matter of a couple of decades. From the early 50s where scientists like Alan Turing and Marvin Minsky laid the framework for modern Artificial Intelligence, with the term officially coming into existence during the Dartmouth conference of 1956.

Decades of research by various computer scientists later, AI today is smarter than ever, with the ability to create entire coherent sentences and solve complex problems. Generative AI, which is a subset of artificial intelligence, is also making all the right noise.

The global Generative AI market is currently worth over $13 billion. It has been just over a year since most of these tools debuted (ChatGPT on 30 November 2022, Google Bard on March 21, 2023, Anthropic Claude in March 2023, and Midjourney in July 2022). Already, Gartner predicts that by 2025, Generative AI augmented tools will be used by at least 50% of the workforce of fortune 100 companies.

Content Creation

With Generative AI tools such as Midjourney, companies can produce high-quality, original content at scale. Not just written content, Generative AI can also be used to generate images, music and videos, where they can cater to the diverse marketing needs.

Personalization

Every individual prefers an experience that is tailored to their needs. Generative AI algorithms can tailor make recommendations according to each individual needs, along with customized marketing campaigns.

Neural Networks

The human brain consists of billions of neurons, which form the basis of most of our cognitive functions, including memory and creativity. Inspired by this architecture, computer scientists devised the neural networks, which consist of layers of artificial neurons. These networks became the backbone of Generative AI models allowing machines to learn from data, recognize patterns, and generate new content in a way that mimics human intelligence.

Latent space

Latent space can be considered as an abstract representation of data that captures the underlying structure and variations in the original, high-dimensional data space. It is essentially a compressed representation of the data, where similar data points are grouped together, making it easier to analyze and manipulate data while retaining essential features and patterns.

Variational auto encoders use a combination of neural networks to produce the desired result. The two interconnected neural networks are called an encoder and a decoder. The encoder takes input data points and converts them into compressed data called latent space or latent variables. This serves as the compact blueprint for the data.

VAEs work under the principle of dimensionality reduction. From the latent code, VAEs can generate new data points that share the characteristics of the original training data.

The decoder network takes samples from the sample space and reconstructs it back to the original input space. Through VAEs, machines can thus generate novel and original content that captures the essence of the data that they were trained upon.

The more commonly used type of Generative AI, GANs consist of two neural networks. One is called a generator and the other is called a discriminator. These neural networks are pitted against each other in a perpetual battle.

The first neural network, called the generator, produces new data that closely resembles the training data. The discriminator, which is the second neural network, is tasked with discerning the difference between genuine data and the data created by the generator

As these two neural networks compete against each other, the generator gets better and better and deceives the discriminator. The discriminator also gets better at identifying data that is artificially generated. This interplay of the neural networks leads both of them to excel, leading to the generation of increasingly original data.

GANs have also been known to produce high quality images, videos and text, which shows how versatile they are.

The customer service landscape is undergoing a dramatic shift. While traditional AI offered automation and basic chatbots, Generative AI (GenAI) is now leading the way in a new era of personalized communication. It has changed the way businesses interact with customers, providing round the clock customer support and driving in more business efficiencies.

Let's look at some of the key areas where GenAI has transformed customer service -

24/7 Support:

Generative AI powered chatbots and virtual assistants are on the forefront of providing assistance to customers 24 hours a day, 7 days a week. Generative AI powered chatbots don’t take breaks, are usually instantaneous in their response, and get the context almost as much as a human agent. They can answer customer queries, provide product information, and fetch all the requisite details that a customer asks in the least possible period of time. Make your business run around the clock with Gen AI.

Provide personalization at scale

If businesses have learnt one thing about the customers in the digital age, it is that personalization can go a long way in ensuring a customer stays with a business. According to a McKinsey report, 76% of the consumers will make a repeat purchase from a brand that personalizes. A customer's history, past purchases and preferences can make a generative AI engine anticipate the needs and offer personalized recommendations

Automate repetitive tasks

One of the tasks that customer support teams dread, but is essential for the functioning of everyday business, is answering questions that are repetitive in nature. Customer support agents cite answering these repetitive queries as one of the most dreadful parts of their jobs, and it is ripe for automation. This is where Gen AI can step in. Using sophisticated algorithms, chatbots and voice assistants powered by Generative AI can answer “n” number of questions, without breaking a sweat. This leaves the customer support agents to concentrate on the more complex queries, which they need more time and attention on.

Proactive support

One thing that a customer appreciates, especially with an ecommerce store, is a business that anticipates their problems and provides proactive support. For instance, if a customer says they are facing an issue with an item they have purchased, businesses can be proactive and ship them a replacement even before they have returned the item. This way, the customer knows that the business cares about them, and Generative AI can go a long way to provide this kind of proactive support.

Aside this, Genrative AI is also now playing a key role in driving customer engagement through product. Let's look at some of these areas as well -

For example, in the first week of November 2023, Google launched a set of Generative AI product imagery tools for those who are willing to advertise in the U.S. Users will simply have to type the prompt of the image they want to use, and the images will be generated via the text-to-image capabilities of AI.

The company showcased the Product Studio by typing something like “a skincare product that was surrounded by peaches with tropical plants in the background.” Now, the same company can request the product to be placed in a winter inspired setting, with “snow surrounded by pine branches or pine cones.”

Data visualization is the art of converting raw or unprocessed data into pictorial format, which will enable businesses to make sense of a large pool of data. This visual representation can be in the form of graphs, infographics, maps or even charts.

With Generative AI, businesses can create personalized visualizations of customer data, turning complex, multidimensional data into visually appealing, interactive and insightful representations.

Data is no longer just pretty. Generative AI makes data accessible, understandable and eventually, valuable. In the future, we may see Generative AI create dynamic stories out of data that may evolve in realtime as the data does

Knowledge base in the form of written content has always been difficult to create for the modern business, simply because of the time and resources required. All this changes with generative AI.

Generative AI can help write new articles from bullet points or powerpoint decks that the employees have created to explain a certain process. Generative AI can even listen in to the recording of a customer call, identifying the major pain points of the customer and the solution that the representative offered them. Using this, it can create a complex knowledge base, which can be used to resolve queries quickly in the future.

Voice support is one of the oldest modes of customer support there is and still, majority of the users prefer talking to an agent directly than interacting with a text based chatbot. Here are some facts -

• A study by the CFI group has shown that 76% of the respondents feel more comfortable with traditional phone calls to customer support representatives to resolve their issues.
• A Zendesk report says 70% of the customers are irritated when their call is transferred from one department to another.
• 33% of the customers are most frustrated when they have to stay on hold, according to Hubspot research

All this points to one thing - voice is still a key part of the customer support puzzle.

Personalized Interactions

GenAI-powered voice technology can deliver highly personalized customer interactions. By leveraging extensive customer data and contextual understanding, these systems can tailor responses to individual preferences, past interactions, and specific needs, creating a more engaging and satisfying experience.

Enhanced Natural Language Understanding

Generative AI enhances natural language processing capabilities, allowing voice assistants to understand and respond to a broader range of accents, dialects, and colloquial expressions. This improved comprehension ensures that customers from diverse backgrounds receive accurate and relevant support, fostering inclusivity and reducing frustration.

We have seen how voice, text and multimodal interactions are set to redefine the way customers engage with businesses. But there is more, a lot more.

There are nuanced and often overlooked trends, which will reshape the future of how customer support will be delivered using Generative AI.

Some of these include:

a. Support agents will get cognitive augmentation

Support agents will no longer be relegated to getting automated responses, but rather their entire human capabilities will be enhanced. During customer interactions, support agents with Generative AI backing them will get sentiment analysis, dynamic suggestions and real-time insights. They will act as intelligent companions, making even novice support team members seem experts.

b. Hybrid learning architecture

Currently, what the AI models are good at is identifying the patterns behind customer queries. But what if they were also able to identify the logic behind these breakdowns? We see an emergence of hybrid learning architectures, which combine symbolic reasoning with neural network capabilities, which will accomplish exactly this. Gen AI can grasp the intricacies in context, which will lead to much more intelligent resoonses.

c. Explainable AI

The exact nature of how AI reaches a particular decision has been a bone of contention among many, especially in the customer support circle. This will change with the emergence of explainable AI. Explainable AI will bring in transparency in the way AI models arrive at specific responses. When businesses make AI decisions more interpretable, businesses will get trust with customers, which showcases accountability in the customer support process.

Lack of empathy

While Generative AI technology may give us all the answers we are looking for, it still lacks the empathy and emotional intelligence of a human agent. Generative AI chatbots may not be adept at dealing with sensitive situations and will fail at building a rapport with the customers.

Relying too much on automation

Building on the previous point, while Generative AI technologies can make the customer support process faster, it should not be seen as a complete replacement to traditional support. This is because gen AI technologies still lack the empathy and the emotional intelligence required to deal with sensitive customer queries.

Bias and ethical considerations

Fairness and inclusivity must be the cornerstones on which these LLMs must be trained upon, and care must be taken to ensure that they strictly adhere to ethical principles and address potential biases.

Limited understanding of context and nuances

While Generative AI tools may generate human-like conversations, they may not be able to grasp the subtleties of the language, or understand sarcasm. Failing to identify these emotional cues and overlooking critical information can lead to miscalculations and eventually frustration.