AI Text Generation: Top 12 Use Cases & 2 Case Studies in 2024

As a branch of AI, generative AI enables to create content in the form of new text, code, images, shapes, videos, etc. based on existing inputs. According to Gartner, generative AI is one of the top strategic technology trends for 2022 and has various applications in gaming, advertising, banking, surveillance, and healthcare.¹ AI text generation is an especially important application of generative AI.

The purpose of this article is to explore how generative AI can be used to generate content in the form of text via 4 use cases and 2 case studies of AI text generation.

What is AI text generation?

Text generation is a field that has been developing since the 1970s and is regarded as a subsection of NLP(Natural Language Processing).² Developing deep learning models for text generation is an ongoing process in the field of NLP. ³ As an example, the researchers are training Generative adversarial networks (GANs), which are generative models that are composed of a generator and discriminator and used for generating synthetic outputs for text generation.

What are AI text generator models?

One of the AI models that can generate text is GPT (Generative Pre-trained Transformer), or generative pre-trained transformer. This language model, built by OpenAI and released in 2020, has different models, including GPT-3.

GPT-3 is a much larger model than its predecessor, with over 175 billion parameters. It is trained on a variety of data sources, including books, articles, and code repositories to generate realistic texts like human writers. It is possible to create summaries, answer questions, use as a grammar checker, learn new ideas and make translations through GPT-3.   

Another approach to text generation is to use a template-based model. ⁴  Unlike GPT-3, these models do not work independently, and intermediate steps require human intervention. It is possible, however, to produce more structured texts based on templates without requiring humans to edit and control them after they are generated. ⁵

Tools for AI Text Generation

AI text generation tools create and provide ready-made templates to create high quality content like: 

• Blog posts
• Social media posts
• E-mails
• Meta descriptions
• Product descriptions
• Slogans, etc. 

Furthermore, they offer collaboration and commercial rights to the produced content, making them useful for business processes. Please feel free to read our article on generative AI tools if you want to learn more about and compare these tools.

What are the use cases of AI generated text?

Using AI text generator tools, businesses can save time, allocate employees’ time for creative projects, generate error-free texts, and streamline their processes. 

There are a number of different ways that AI text generators can be used in business, such as:

Content Creation

An AI writer tool can be used to create all sorts of content supporting these business functions:

Marketing:

1. Blog posts based on keywords and the desired length

2. Product descriptions based on data about its features and benefits

3. Social media posts

4. Media campaigns (e.g. ads)

All business units:

5. Reports like regional sales reports

Media:

6. Automated article generation for regular events like sports matches

All of these can help businesses to save time and ensure that their digital presence is always up to date.

Text Summarization

An AI writer can be used to create summaries of longer texts. They offer various possibilities for creating content such as:

7. Creating newsletters

8. Summarizing internal company documents

9. Assisting educators in preparing educational material by providing them with summarized content of sources

10. Facilitating the review of literature in research contexts, and much more

11. SEO optimized content

In order to make a blog post or article more optimized for search engines, AI text generators assist companies in the process of deciding the headline, meta description, and keywords of an article. With these tools, it is possible to discover the most searched topic clusters, and their keyword volumes, and reach best-ranking URLs to increase SEO visibility from ranking higher than earning just a few clicks.

12. Customer support

A text generation tool can provide real-time chatbot support to customers, as well as prepare personalized customer service answers. Such tools can shorten response times and improve customer satisfaction.

Case Studies

1. Evaluating claims

Insurance companies evaluate long-written applications in their claims management process to decide whether a case is eligible for the insurance settlement process.

An insurance company had difficulty in processing all these materials, sharing responsibilities, speeding up decision-making processes, and seeking a solution for improving the claim settlement process.⁶

A deep learning model called sequence-to-sequence architecture was implemented to resolve the problem. This is a neural network type commonly used for machine translation, answering questions, and summarizing text. As a result of the adoption of this model, summaries of applications are generated, which makes the decision-making process faster and prevents the waste of time. 

2. AP automated financial report generation

Business reporters produce quarterly financial reports that require gathering the income statement, balance sheets, and cash flow statement of a company. Regularly preparing these reports is time-consuming, reducing the amount of time that can be allocated to writing creative journal articles. 

In order to overcome this problem, Associated Press, which suffers from the same problem, adopted a language generation tool that converts the collected data into a coherent report, allowing for 15-times more financial reports to be generated.⁷

Learn more about report automation tools. 

If you have questions or need help in finding vendors, reach out:

External Links

• 1. “Top Strategic Technology Trends for 2022: Generative AI”. Gartner. October 18, 2021. Retrieved November 11, 2022.
• 2. “Zhang L.; Sun J. (2009). “Text Generation”. Encyclopedia of Database Systems.
• 3. “Iqbal T.; Qureshi S. (2022). “The survey: Text generation models in deep learning”. Journal of King Saud University – Computer and Information Sciences. Volume 34: 2515-2528.
• 4. “How much AI is in NLG? and what are GPT models?” Retresco. 2021. Revisited January 20, 2023.
• 5. “How much AI is in NLG? and what are GPT models?”. Retresco. June 6, 2021. Retrieved November 11, 2022.
• 6. “Plagge, Felix (November 12, 2020). “5 Practical Examples of NLP Use Cases”. Statworx. Retrieved November 11, 2022.
• 7. “Associated Press”. Automated Insights. Retrieved November 11, 2022.