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4 ChatGPT Automation Use Cases in 2024

ChatGPT is one of the latest trends for both popular web users and the tech world. According to SimilarWeb numbers, it has reached 610M visits after its release 3 months ago. Among the large language models, it is by far the most popular and sophisticated one. 

As a generative artificial intelligence tool, ChatGPT has and will continue to have a significant impact to automate tasks that are time consuming and laborious. In this article, we will list some use cases for ChatGPT automation. 

Top 4 use cases of ChatGPT automation

1- Content creation automation

ChatGPT can achieve content automation by using its natural language generation (NLG) capabilities to generate human-like text. With its large dataset and advanced neural network architecture, ChatGPT can learn patterns in human language and generate coherent and contextually appropriate responses to various prompts.

To use ChatGPT for content creation automation, a business or individual can provide prompts or topics to the model, and the model can generate content based on that input. For example, a business could provide a list of keywords or phrases related to their product or service, and ChatGPT could generate a series of blog post titles and outlines based on those inputs.

Some content creating automation use cases of ChatGPT include:

  • Copywriting
  • Blogging
  • Social media marketing
  • Email marketing
  • Creative writing
  • Product descriptions

2- Translation automation

ChatGPT can be trained on a large corpus of text in multiple languages, which enables it to learn the patterns and nuances of different languages and quickly generate translations that are accurate and contextually appropriate.

  • Website localization
  • Text translation
  • Document translation

3- Coding automation

By understanding natural language prompts and taking into account the context of the task, ChatGPT can generate accurate and high-quality code for a wide range of programming tasks. Additionally, ChatGPT can be trained on specific programming languages, fine-tuned on specific tasks, and adjusted to optimize for code quality.

Code generation

ChatGPT is a language model that has been trained on a vast amount of text data, including code written in various programming languages. It can write code automatically based on natural language prompts (see Figure 1). This process involves understanding the meaning of the input text, selecting appropriate code templates, filling in the necessary code blocks, and generating syntactically correct code for a programming language.

Figure 1. Generating an HTML form and JavaScript submit code with ChatGPT

Source: Medium


ChatGPT can assist with automation in debugging code, but it may not be able to fully automate the process. Debugging involves identifying and fixing errors in code, which often requires a deep understanding of the codebase and the context of the problem. Some code debugging tasks ChatGPT can help to automate are:

  • Code analysis
  • Code refactoring

Test data & test scripts creation

Test data creation involves generating data sets for testing software applications or systems. Test scripts are automated tests that simulate user interactions with a software application or system. These processes can be time-consuming and tedious, especially for large or complex applications.

ChatGPT can assist with test data creation by using natural language prompts to generate test data. For example, a natural language prompt like “Create a data set of 1000 customer records with unique email addresses” can be understood by ChatGPT, which can then generate the appropriate test data.

ChatGPT can also help with test script generation. For example, a natural language prompt like “Create a test script that checks if a user can log in to the system” can be understood by ChatGPT, which can then generate the appropriate test script.

4- Customer service automation

Customer service automation refers to the use of technology to automate customer service tasks, such as answering customer inquiries, providing support, and processing requests, to improve customer satisfaction efficiently.

ChatGPT can be used to automate customer service tasks by using natural language processing (NLP) and machine learning algorithms to understand customer inquiries and provide the appropriate response. ChatGPT can also be integrated with customer service chatbots or other customer service platforms to provide automated responses and support to customers.

Figure 2. ChatGPT making conversation labeling for customer requests

Some specific examples of how ChatGPT can achieve automation in customer service include:

  • Customer inquiries: ChatGPT can be used to automate the processing of customer inquiries, or conversation labeling, providing accurate and timely responses to common questions and concerns (see Figure 2 above).
  • Support requests: ChatGPT can assist with support requests by generating automated responses to common issues or escalating the request to a human representative if necessary.
  • Order processing: ChatGPT can be used to automate order processing tasks, such as tracking orders or updating shipping information.
  • Appointment scheduling: ChatGPT can automate appointment scheduling by providing available time slots and confirming appointments with customers.
  • Product recommendations: ChatGPT can be used to generate automated product recommendations based on customer preferences and purchase history.

For more on AI use cases in customer service, check out our article.

Will ChatGPT replace human jobs?

While it is still too early to make conclusive judgments, there is a potential risk that generative AI, specifically ChatGPT, may contribute to unemployment in specific industries. This risk arises from the possibility that ChatGPT may automate creative and elaborate tasks or processes that were previously carried out by humans, resulting in the displacement of human workers.

For instance, if a company employs a ChatGPT and fine-tunes it to create content for its marketing campaigns, this could lead to the replacement of human workers who were previously responsible for producing such content. 

Similarly, if a company uses ChatGPT to automate customer service tasks, it could result in the displacement of human customer service representatives. Furthermore, since ChatGPT is capable of generating code, it may pose a threat to programmers.

However, it is still too early to discuss the likely consequences of ChatGPT and generative AI on such topics. If you are interested in other ethical problems these technologies pose, you can check our articles on the ethical problems around generative AI and the ethics of AI.

Don’t hesitate to reach us if you have any questions on generative AI:

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Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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