Generative AI for customer support: History, benefits and use cases

Have you heard of Generative AI? This revolutionary technology based on deep learning is reshaping the customer support landscape by understanding natural language, identifying context, and interpreting emotions in any conversation.

This AI-driven system provides smart responses akin to human intelligence, enabling businesses to engage in dynamic and personalized conversations with their customers. It’s also capable of acquiring knowledge and enhancing its abilities over time, which can help companies more efficiently address future queries and concerns based on historical data.

It's no wonder that many businesses are implementing AI-powered customer support solutions. In fact, Intercom's 2023 report, The State of AI in Customer Service, reveals that 69% of support leaders plan to invest more in AI in the year ahead—and 38% have already done so.

In this article, we’ll go into significant depth explaining how Generative AI for customer support is propelling businesses into new frontiers. You’ll find out how generative AI can be incorporated into existing support departments to benefit both customers and agents, and you’ll see successful cases of companies that have implemented Gen AI solutions.

What is generative AI?

Generative AI is an advanced form of artificial intelligence capable of creating a wide range of content, including text, images, video, and computer code. It achieves this by analyzing extensive sets of training data and generating unique outputs that closely resemble the original data. Unlike rule-based AI systems, Gen AI relies on deep learning models to produce original outputs without explicit programming or predefined instructions.

One of the remarkable features of generative AI is its ability to create highly realistic, intricate, and utterly novel content, akin to human creativity. This makes it an invaluable tool in various applications, including image and video generation, natural language processing (NLP), and music composition. Examples include ChatGPT for text and DALL-E and Midjourney for images.

Generative AI technology background

The history of Generative AI can be traced back to the early development of artificial intelligence and machine learning. Some of the highlights of this trajectory actually began as far back as the 1940s!

The McCulloch-Pitts Neuron Model (1943), devised by Warren McCulloch and Walter Pitts, marked the genesis of neural networks. Their premise was that networks could be comprehensively elucidated using propositional logic, leveraging statements and logical operators such as “and”, “or”, and “not”.

In 1950, Alan Turing introduced the Turing Test, a pivotal concept for assessing machine intelligence. Although not intrinsically linked to Generative AI, this notion profoundly shaped the perception of AI's potential in emulating human-like proficiencies.

The Dartmouth Workshop (1956) stands as a cornerstone, formally birthing the discipline of Artificial Intelligence. This pivotal gathering catalyzed the exploration of "thinking machines," an effort that laid the groundwork for machine learning studies and the subsequent emergence of generative models.

Frank Rosenblatt's creation of the Perceptron (1958) introduced a single-layer neural network with the ability to learn and make decisions based on input patterns. This innovation hinted at the expansive array of potential applications, including image recognition, but it wasn't without limitations.

In 1968, Marvin Minsky and Seymour Papert's critical assessment of single-layer networks spurred advancements in the field. Their exploration underscored the complexity of training and solving intricate problems, which ultimately steered the trajectory of Generative AI.

The Backpropagation Algorithm (1986) emerged as a transformative breakthrough, resuscitating neural networks as multi-layered entities with efficient training mechanisms. This ingenious approach entailed networks learning from their own errors and self-correcting – a paradigm shift that significantly enhanced network capabilities.

Venturing into the 1990s, Recurrent Neural Networks (RNNs) surfaced as a milestone, imbuing networks with memory and temporal continuity. RNNs enabled sequential data utilization, propelling applications such as language translation, Siri's functionality, and automated YouTube captions.

Despite RNNs' innovation, the need for more autonomous operation persisted. This need culminated in the emergence of Restricted Boltzmann Machines (Late 1990s), a genre of generative models founded on probabilistic modeling and unsupervised learning. Notably, these machines powered collaborative filtering, a technique that leveraged past interactions to tailor solutions for contemporary users.

Fast-forward to 2011, and the Proposal of Generative Adversarial Networks (GANs) by Ian Goodfellow and his collaborators took center stage. This ingenious architecture featured a data-generating generator and a distinguishing discriminator. GANs not only learned from historical data but also simulated realistic customer inquiries, effectively sharpening support teams' skills and response quality.

What does ChatGPT have to do with generative AI?

All of that progress brings us to ChatGPT, the breakthrough LLM chatbot released by OpenAI in November 2022. 

The GPT in ChatGPT stands for Generative Pre-trained Transformer architecture, which is a language model capable of understanding natural language and performing related tasks. These tasks include creating text based on a prompt and engaging in a conversation with users.

ChatGPT builds upon Variational Autoencoders (VAEs) for image and text generation, and Generative Adversarial Networks (GANs) for training processes. Put more simply, it can identify language patterns, provide contextually relevant responses to users’ input, learn from their feedback, and improve its performance over time.

In the realm of generative AI for customer support, ChatGPT has found several applications, including:

• Chatbots and virtual assistants: ChatGPT can be trained to resolve low complexity tickets and answer frequently asked questions.

• Content generation: The technology can help your agents create educational content for your company's help center.

• Language translation: It can assist customers worldwide without language barriers.

Use cases for generative AI in customer support

Now that you know what generative AI is, it's time to see how the technology can make your customers’ lives easier and your agents’ work more efficient.

Customer assistance

1. Instant answers

In the previously mentioned 2023 report, The State of AI in Customer Service, 45% of the surveyed support leaders said they expect a change in resolution times as a result of implementing AI.

In an era in which efficiency is more critical than ever, tools powered by generative AI for customer support allow you to offer 24/7 assistance without burning out your team. This inexhaustible technology means that your customers get accurate, personalized answers at any time, day or night.

2. Auto-generated article suggestions

Sometimes all a customer needs is an article that tells them how to do something step by step. If this is a scenario your company is familiar with, Gen AI can help you generate automatic recommendations based on keywords, history of interactions, and similar requests from other users.

3. Question triage

One of the great strengths of generative AI for customer support is its ability to identify which questions can or cannot be answered by the AI itself, filtering out the most complex ones and sending them directly to humans.

Support team assistance

1. Recap conversations for other agents

Whether you're transferring tickets, covering for an absent colleague, or reporting issues and feature requests to product teams, AI-driven summarization ensures time efficiency by transforming long conversation threads into short and easy to read paragraphs.

In fact, this automation feature of generative AI for customer support can reduce manual tasks. According to Intercom’s State of AI 2023 report, 28% of the respondents say that artificial intelligence helped them recap conversations, for example. 

2. Expand answers

With AI generated chat answers, for example, the support representatives can write shorthand customer responses and let the artificial intelligence generate a complete suggested or rephrased message. 

3. Create new help articles

Support reps can build on past interactions with customers to create articles that better respond to their needs. Reps can also use artificial intelligence to expand on a topic, identify gaps in tutorials, and make the information as complete as possible.

Benefits of generative AI for customer support

1. Ease the burden on support teams

Support teams facing both high-stress situations and an endless procession of repetitive tasks are often left with burnout. By offloading routine inquiries to AI, support agents can focus on the more engaging and intellectually stimulating aspects of their work.

In fact, many companies are already taking concrete steps to reduce the burden on their employees. According to our Customer Service Trends Report 2023, 71% of support leaders plan to invest more in automation to increase the efficiency of their support team.

Tools like AI-powered chatbots will allow your support team to do more by:

• automating answers to frequently asked questions

• collecting information to help triage complex problems

• routing urgent or VIP queries to the agent best equipped to resolve them

This doesn’t mean that humans will be taken out of the customer service picture. Rather, they’ll gradually evolve and begin developing the skills necessary to work collaboratively with this rapidly advancing technology.

2. No training required

As businesses grow, so does the volume of support inquiries they receive. But hiring and training more support agents may not always be the most practical or cost-effective response.

According to 41% of the customer care leaders surveyed by McKinsey in 2022, it can take up to six months to train a new employee to achieve optimal performance. An additional 20%, meanwhile, reported that such comprehensive training takes more than six months.

Implementing generative AI for customer support can help your team achieve scalability. It allows you to offer 24/7 assistance to your customers, as well as more consistent responses, no matter how high the volume of inquiries becomes. 

In short, you overcome two hurdles at once: you expand your level of support without overburdening your current human agents or increasing the costs associated with training new employees.

For Samuel Miller, Customer Support Operations Manager at Dental Intelligence, the biggest value add for AI is precisely the reduction of training costs:

“For us, it’s really about saving money on training because we don’t have to train them on every single thing. We can just train them on the major issues they have to do, and not so much on the day-to-day things that customers can find, the knowledge articles, and stuff like that. It allows us to go deeper in the training quicker.”

If you have further questions about the potential to transform the customer service experience through technology, take the advice of our VP of Customer Support : To AI or not to AI? The support leader’s dilemma.

3. Improved team productivity

Another benefit of generative AI for customer support is its ability to increase team productivity by 40-45%, according to recent McKinsey research. 

This can take different forms for each business. For example, a healthcare enterprise may use sentiment analysis to detect a frustrated customer and escalate the issue to a human agent for personalized attention.

If you’re unsure about how to deploy Gen AI in your company, take Kavita Ganesan’s advice and look out for existing processes that are inefficient. Founder of the consulting business Opinosis Analytics and Ph.D. in Natural Language Processing (NLP), she believes that:

“Finding those manual processes that are repetitive and require human-level thinking – that’s a key point – is where AI solutions can really make an impact in the short term because those problems are well-understood and likely have metrics you can use as a way to measure how it’s performing against the manual approach.”

Generative AI for customer support success cases

If you want to use generative AI for customer support and accurately answer questions with zero training required, you need to meet Fin, our AI-powered bot. It never generates misleading answers or initiates off-topic conversations, and is able to triage complex problems and seamlessly pass them to your human support teams.

On top of all that, Fin becomes smarter over time, enabling it to keep up with the forever changing support needs of your customers. As a result, it dramatically reduces your support volume, simultaneously improving both customer and agent satisfaction.