Author of this article：Miya, Search engine optimization expert

With the power of generative AI, customer service is rapidly reaching a higher level of corporate development and has become the focus area of executive teams. Data show that 85% of executives predict that generative AI will interact directly with customers.

If you are engaged in customer service, you may have the following questions: What exactly is AI? How does it apply to customer service? Will AI replace my job? Don't worry, we will help you clarify these questions and uncover the truth about generative AI.

If you want to learn moreHow AI is revolutionizing customer service, And bring the high-level expected return on investment (ROI) to the company, then you have found the right place. Before the formal discussion, let's start with the basics.

What is generative AI?

Generative AI is a branch of artificial intelligence, and its core function is to “generate” brand new content, data, or output. Such systems learn and analyze patterns in existing data, and generate content similar to human creation based on these patterns, covering a variety of application scenarios such as image generation, text creation, data enhancement, and music composition.

Despite the past yearGenerative AIIt has received widespread attention and seems to have become a dazzling new technology, but its history can actually be traced back to the 1950s and 1960s. However, it was not until 2014 that generative AI really became available with the emergence of a specific machine learning technology called generative adversarial network (GAN).Generate brand new content outputThe ability has opened a new chapter in practical application.

The difference between generative artificial intelligence and artificial intelligence

Analysis of commonly used but confusing terms

The definition of ”generative AI" varies from website to website and dictionary to dictionary. Some regard it as a category of AI algorithms, while others describe generative AI as a deep learning model and classify it within the larger framework of machine learning. and DictionaryGenerative AI is defined as: an AI technology that processes user prompts and generates response output based on a training data set.

AI is usually used as a general term to refer to various advanced computing systems. Although AI and generative AI are related, they differ in the scope and specific implementation of the field of artificial intelligence. Some people even equate AI, generative AI, machine learning, and large language model (LLM), so it is essential to understand these differences. First, we need to distinguish between AI and generative AI.

Artificial intelligenceIs a broad branch of computer science, the goal of which is to create intelligent machines capable of performing tasks that usually require humans to complete. These tasks include speech recognition, problem solving, perceptual analysis, and language understanding. AI can also focus on tasks in specific areas, such as diagnosing medical conditions or playing chess. AI technology includes a wide range of methods and technologies, including but not limited to machine learning、Natural Language processing (NLP)And expert system.

Although AI canAnalyze data intelligentlyAnd respond to its results, but its functions are usually limited to these application areas. For example, traditional AI programs can complete complex calculations or precise analysis, but they do not “generate” brand-new and innovative content.

Generative AI It goes one step further, using existing data to create brand new content and support multiple forms. Based on a machine learning model, generative AI generates original content, such as text, pictures, audio, or video, by identifying patterns from existing data.

The two fields of AI and generative AI are both continuously evolving and developing, opening up more possibilities for the wide application of technology.

Next, we introduce some terms related to generative AI to help deepen our understanding：

• Machine Learning (Machine Learning)： This is a subfield or method of AI research that aims to develop algorithms and models that enable computer systems to learn from data and make predictions or decisions. Machine learning systems rely on a large amount of training data, and adjust their performance on this basis, continuously improving as the amount of data exposed increases.
• Large Language Model (LLMs)： Large-scale language model is one of the latest and not clearly defined concepts in machine learning model. For example, OpenAI's GPT-3 is a language model trained on a large amount of text data. It is good at text generation and understanding, and can generate natural dialogue-like replies.
• Deep Learning (Deep Learning)： Deep learning is a subfield of machine learning, focusing on training neural networks to process more complex patterns than traditional machine learning.
• Neural Networks (Neural Networks)： Neural networks draw on the connection structure of neurons in the human brain, can learn through a large number of examples, and are used for tasks such as pattern recognition, classification, and regression. They generate meaningful output by processing input data, and continuously improve their learning ability as information accumulates.

How generative AI works？

Generative AI uses machine learning technology to analyze common patterns and structures in massive amounts of data, and uses this information to generate brand-new content. As data samples increase, the output of generative AI will become more refined and intelligent.

The following is an overview of the operation process of generative AI：

Data collection and preprocessing：Collect task-related data samples, such as text, images, or other types of content, and preprocess the data. This step ensures the quality and consistency of the data, and lays a solid foundation for the training of the model.

Model training：According to the characteristics of the task and data, select the appropriate generation model for training. Common models include：

• Generate confrontation network (GANs)： Through the confrontation training of two neural networks, one is responsible for generating content and the other is responsible for evaluating its authenticity, and the two sides improve the accuracy of prediction in the competition.
• Variational autoencoders (VAEs)： By compressing large-scale data into a small representation in the “latent space” (latent space), and then decoding from it, complex information such as images is reconstructed. This encoding and decoding process makes the model better at generating similar high-quality content.
• Auto-regression Models (Auto-Regression Models)： By predicting future behavior based on historical data, it is used to generate serialized data, such as language models or time series predictions.

Model sampling：After the model training is completed, new content can be generated by providing random input (also known as seed data). Based on the learned patterns, the model generates content similar to the characteristics of the training data.

Model fine-tuning and exploration：Fine-tune the model parameters according to actual needs to improve the quality or style of the generated content. At the same time, you can deeply explore the performance and potential of the model in different situations by adjusting the inputs and parameters.

Content evaluation：Conduct a multi-dimensional evaluation of the generated content, including authenticity, coherence, relevance and aesthetics. Optimize the model based on feedback, so as to achieve continuous iteration and improvement.

Ethics and prejudice considerations：The use of generative AI needs to pay attention to ethics, such as avoiding bias in training data, the dissemination of misinformation, and content abuse. Through the formulation of strict regulations and technical means, reduce potential risks and ensure the responsible use of generative AI.

Why is generative AI important?

Although many people still maintain a certain degree of suspicion about generative AI, there is no doubt that it has the potential to make extraordinary achievements.

“It is expected that in the next year, investment in AI will grow by more than 300%. ”

• Forrester

Its ability to quickly create novel and impressive content has further promoted the development of creativity and innovation-and promoted the boundaries of human imagination in the fields of art, music, literature, etc. It is precisely because it can generate a large amount of content so quickly and has personalized characteristics that it can be used in many industries such as marketing, e-commerce and entertainment.

Generative AIThe ability to simulate real-world scenarios makes it a very valuable tool in scientific research, engineering, and risk assessment. It can also suggest new molecular structures, simulate protein folding and other complex biological processes, thereby promoting medical and scientific progress.

“By 2024, more than 100 million people in the United States will use it.Generative AI。 By 2025, this number is expected to reach 116.9 million. ”

• Hootsuite

Generative AIinData enhancement, AI research, data interpolation and denoising also play an important role.

How does generative AI affect customer service?

People speculate that AI is about to fundamentally change the way we do business. In the field of customer service, the impact of AI has already emerged. By 2024, the global chatbot market will reach US9994 million.

As leaders and employees of customer service, you may still be confused about how generative AI affects your customer service organization or your role in the team, which is completely understandable. We are here to help you sort out these questions and break the misunderstanding that AI will replace your work.

Although generative AI will take over some customer support tasks like previous AI versions, it also paves the way for new opportunities. Instead of weakening the role of customer service professionals, generative AI will increase the importance of labor input.

"68% of employees said that generative AI will help them better serve their customers. ”

• Salesforce

When generative AI works harmoniously with the customer service team, wonderful things happen: you can do it with minimal labor costs.Automatically resolve customer service issues。

This not only provides opportunities for customer support teams, but actually promotes their career development, from traditional customer service representatives to robot administrators, or from customer support advocates to conversational AI experts.

Click "Read More" below to learn how to transition to an AI-first way of working and ensure that AI can continue to develop with investment and structural adjustments in customer service teams.：

Customer service generative AIIndustry examples

Considering the many advantages of generative AI in customer service, you may be curious which companies are actually applying these technologies and how they are implemented. Here are some key industries that have successfully integrated generative AI chatbots into their customer service strategies：

Tourism and Hospitality industry

Airlines such as Delta Air Lines (Delta) useGenerative AI chatbot, Can quickly provide detailed answers about travel, reservations and in-flight services, while also enabling customers to quickly complete operations such as check-in, baggage tracking and flight inquiries. Airports like Heathrow International are also using generative AI to reply to service inquiries and automatically summarize cases, saving agents' time and energy, thereby increasing productivity. Travel websites such as Expedia integrate generative AI (such as ChatGPT) into mobile applications to provide a more conversational travel planning experience, and generative AI robots can provide travel advice and help.

e-commerce

Generative AI is making it easier for shoppers to find the goods they want. Global retail giant H&M passedGenerative AI chatbots reduce response time by 70%%, And shoppers can now use voice assistants driven by generative AI in mobile applications. This not only improves the customer experience, but also significantly reduces the burden on the customer service team.

Google's shopping service is usually the starting point for finding the perfect product, but now they have launched a brand new generative AI-powered “try-on” function that allows shoppers to view the effect of clothing on models of similar body types, skin tones, and sizes. Through a technology called “diffusion” (diffusion), Google's new generative AI engine can obtain a single image of a piece of clothing and adjust it to various body types to show how the clothes hang, fold or unfold naturally.

Medical, financial services, etc.

Used by medical companies like SmileDirectClubGenerative AITo listen to and summarize customer calls, save agency time and improve the customer experience. The productivity tool ClickUp uses its generative AI chatbot to provide users with instant product information and smoothly hand over questions to relevant experts when necessary.

Other generative AI application cases in customer service include the rapid drafting of detailed email replies, which increased Octopus Energy's customer satisfaction score by 18%.

In fact, we have only touched the surface of the changes that generative AI can bring to customer service organizations, and this technology is progressing rapidly. The more we use it, the smarter and more efficient it becomes. Let's take a closer look at the current scope and limitations of generative AI in customer service.

Generative AI in customer serviceWhat can and cannot be done?

Intelligent customer service leaders recognize that generative AI is not a one-size-fits-all solution-it requires the right approach and a certain amount of prudence.

First, let's explore what generative AI can do for customer service today.

Accelerate content creation

Companies with the right AI platform can use AI as a writing and building assistant. The AI assistant can extract information from existing content and develop the first draft of the chat stream to accelerate the realization of value. Speeding up the speed of building bots enables CX leaders to quickly start chatbots and start to have an impact on customers.

It should be remembered that although generative AI can help speed up the process, it still requires manual intervention-there is a person to review the content generated by the AI to ensure that it is safe, accurate, and helpful.

Reshaping the customer service organization

As generative AI reduces the pressure on some automation construction, the CX team has more opportunities to reorganize their team and put customer support staff in more strategic positions.

AI may be a sailing ship, but you still need someone at the helm. Now, more attention is paid to analytical work-dig deeper into which interactions are effective and which ones are not, and then identify different processes or interaction frameworks to solve these problems and help customers find the answers they need.

This provides a clearer direction for the structure of the customer service organization and opens up a new career development path, enabling robot builders to advance to real robot managers, and these managers can also use this to use other skills to enable more people.

Provide built-in dialogue best practices

Advances in generative AI have made it easy for inexperienced people to automate, and dialogue best practices are built into the generated responses and content.

The robot administrator can basically list some key points, and the AI assistant can use these built-in best practices to reformat them to adapt to the dialogue channels used-whether it is email, Web chat, SMS, phone, or other channels.

Generative artificial intelligenceKnowledge base best practices

1. Mutually exclusive and fully covered knowledge architecture

When designing the structure of the knowledge base, careful planning and preparation can avoid wasting a lot of time and energy in the future. This concept is usually called "ontology”.

The classification of each level in the knowledge tree should be “mutually exclusive and completely covered”. Through this structure, AI can efficiently answer most customer questions and be accurate and efficient.

Specifically：

• Mutual exclusion: It means that there is no overlapping content between different categories. Each type of information is independent, avoiding information conflicts. If a certain information needs to be updated, it only needs to be modified in one place and will not affect the content in other places, thereby reducing the workload of updating and maintenance.
• Full coverage: It means that all the categories are added together to fully cover all the information that customers may ask questions about. There are no missing parts to ensure that customers can get the answers they need.

2. Precise and detailed titles to clearly set the context

If you have multiple similar parts under the same topic, and their titles are the same, it will be difficult for AI to effectively grab information and provide customers with accurate answers. Therefore, the title and chapter header should become more and more precise as the customer gradually delves into the tree structure of the knowledge base.

Interestingly, descriptive headings are often more effective than question headings. If your knowledge base is organized in a question-and-answer format, make sure that each answer is independent and complete. For example, the question “Can I pay by credit card?" ”The answer should be “yes, you can pay by credit card” instead of simply replying “yes”.

If many articles discuss similar topics, make sure to make a clear distinction between the title and the body of the text, so as to avoid the information being misused or taken out of context when generating answers.

3. Independent complete article

The amount of content of the article, or the boundaries of the level of detail, usually depends on the specific situation. But the best practice is that each knowledge article only covers one topic, and to ensure that customers can get all the key information needed for that topic from this article, without the need to jump between different pages. This can ensure the consistency and completeness of information and improve the efficiency of customers in finding information.

MixdeskHelp enterprises useGenerative AI optimizes customer serviceAnd global marketing

Generative AIThe field of customer service is rapidly changing, and Mixdesk uses powerful AI-driven functions to help enterprises optimize automation processes and improve service efficiency. With the help of Mixdesk's generative AI technology, companies can quickly respond to customer needs, from automatic response to frequently asked questions to cross-system integration, personalized service and handling of more complex issues, and comprehensively improve customer satisfaction and interaction quality.

With continuous optimizationAI data insightswithAutomated workflow, Mixdesk provides more efficient and accurate operation space for customer service departments to help companies build lasting customer relationships and drive long-term growth. Through intelligent tools and powerful analysis capabilities, Mixdesk ensures that every customer interaction is full of value, helping companies to market globally.