The Power of Generative AI in User Story Creation

Ruth Hadari
Ruth Hadari
Agile Advocate, Engineering Ops Expert
Posted on
Jun 12, 2023
Updated on
Jun 12, 2023
Table of Content

In today's digital age, storytelling has become one of the most dominant aspects of brand communication and marketing. Every company has a unique story to tell, and human-centered stories that engage and inspire are essential to a brand's success. 

User stories, in particular, have become a crucial component in software development, as they outline the user's needs and goals in a specific scenario.

Understanding Generative AI

Generative AI has the potential to revolutionize user story development by automating the content creation process. But what is generative AI, exactly?

Generative AI is an advanced branch of artificial intelligence that utilizes machine learning algorithms to autonomously generate new content. It involves developing computer programs that can use data to understand and mimic creative processes similar to human creativity and intelligence. Generative AI can learn about various patterns and create unique, original content that aligns with specific goals or objectives.

Applying Generative AI to User Story Creation

The use of generative AI in user story creation has many real-world applications. Generative AI is a form of artificial intelligence that enables machines to generate content automatically. It has become increasingly popular in recent years due to its ability to create high-quality content quickly and efficiently.

Auto-Generating User Personas

Generative AI can help Agile project management teams quickly create user stories that align with the product's goals and user needs, enabling them to develop products more efficiently and effectively. In Agile project management, user stories are short, simple descriptions of a feature told from the perspective of the user. These stories are used to define the product's requirements and guide the development process. With generative AI, project managers can create user stories that are more accurate, comprehensive, and tailored to their specific needs.

For example, imagine a project manager who needs to create user stories for a new e-commerce website. Using generative AI, they can quickly generate user stories that reflect the needs and preferences of their target audience. This can save time and resources, while also ensuring that the final product meets the needs of its users.

Identifying Common User Scenarios

User scenarios help teams understand how users interact with the software and what their needs are. Traditionally, identifying common user scenarios has relied on manual analysis and brainstorming. However, with the advent of generative AI, teams now have a powerful tool to automate and streamline this process. In this blog post, we will explore how generative AI can be used to identify common user scenarios, providing teams with valuable insights for creating effective user stories.

Generative AI models can leverage the vast amounts of data they are trained on, and can learn patterns and generate realistic scenarios based on that knowledge. This means generative AI could analyze existing user data like:

  • Usage patterns
  • User feedback
  • User behavior
  • Recurring patterns

All this data would help the model predict high-quality user-scenarios that are not only detailed, but also realistic. 

Imagine an Agile team developing a mobile banking application. They want to understand the common user scenarios for creating user stories that reflect the needs of their users. In the past, they would rely on manual analysis, conducting surveys, and collecting user feedback. While these methods can be valuable, they are time-consuming and may not capture the full range of user scenarios.

With generative AI, the team can feed the AI model various types of user data, such as transaction histories, user interactions, and feedback. The model can then analyze this data, identify patterns, and generate common user scenarios automatically. For example, the AI model might identify scenarios such as "User A wants to transfer funds between accounts" or "User B wants to view their transaction history." These scenarios can then serve as the basis for user stories that accurately reflect the needs of the users.

Popular Generative AI Models for User Story Creation

When it comes to user story creation, several different generative AI models are available. These models employ advanced algorithms to generate high-quality text that can be used to craft compelling stories that engage readers and drive business results. Let's take a closer look at some of the most popular generative AI models used in user story creation.

GPT-3: The Game Changer in AI Text Generation

GPT-3 is an advanced AI model developed by OpenAI that has been making waves in the world of user story creation. This model utilizes deep learning algorithms to generate high-quality text that is nearly indistinguishable from text written by humans. 

One of the key advantages of GPT-3 is its ability to generate content that is contextually accurate and relevant to the topic at hand. This makes it an ideal choice for crafting user stories that resonate with readers and drive engagement.

Another advantage of GPT-3 is its ability to generate text in a variety of styles and tones. Whether you're looking for a conversational tone or a more formal approach, GPT-3 can deliver the content you need to create compelling user stories that connect with your audience.

BERT: The Bidirectional Encoder Representations from Transformers

BERT is another popular generative AI model that is widely used in user story creation. This model utilizes bidirectional training and transfer learning to generate text that is contextually accurate and relevant to the topic at hand. Unlike other models, BERT is able to understand the relationship between words and generate text that is both grammatically correct and semantically meaningful.

One of the key advantages of BERT is its ability to generate text that is free from common errors and mistakes. This makes it an ideal choice for crafting user stories that are polished, professional, and reflect positively on your brand.

XLNet: A Generalized Autoregressive Pretraining Model

XLNet is a generative AI model that uses unsupervised training to generate content without any human input. This makes it an ideal choice for creating user stories that are innovative and creative, and that push the boundaries of what is possible with generative AI.

One of the key advantages of XLNet is its ability to generate content that is free from the limitations of pre-existing data. This means that it can create user stories that are truly unique and original, and that stand out from the crowd.

Overall, when it comes to user story creation, there are several different generative AI models to choose from. Whether you're looking for a model that can generate content that is contextually accurate, grammatically correct, or truly innovative and creative, there is a model out there that can deliver the results you need to succeed.

Great Existing AI Tools for User Story Creation

User Story Generator

Planorama Design developed this free-to-use application with the purpose of assisting in the evaluation of product concepts, encompassing their potential features, user personas, and story narratives.


Easy-Peasy AI

Easy-Peasy AI is an AI-powered copywriting tool that can create long and descriptive text based on a given input. Amongst the many presets, there is one specifically for user story creation which takes your inputs and returns you up to 5 detailed user stories.



The AgileStory model has been trained on thousands of user story examples and data points. With AgileStory, a single sentence is all it takes to generate user stories, and it can be fine-tuned to produce user stories that perfectly align with your business needs. 

AgileStory saves you both the ideating and writing time needed to complete user stories, and you’re also provided the added benefit of an AI-generated feature list to further save you time and effort.


Looking Forward

Although it seems that we experienced a huge technological leap with LLMs and generative AI, the truth is that the technology will only advance from here. In the future, there will be even more use cases for generative AI within agile workflows, especially on the product side. Let’s have a look at where this technology could take us in the near-future.

Product Requirements Document

Traditionally, the Product Requirements Document (PRD) serves as a cornerstone for software development projects. It outlines the product vision, goals, and features, providing a blueprint for the development team. AI's influence on the PRD process will be transformative. With the power of generative AI, the creation and refinement of PRDs can be automated, saving time and effort. AI algorithms will analyze data, user feedback, and market trends to generate comprehensive PRDs that align with customer needs and business objectives.

Epic Description

In Agile, epics are large user stories that encapsulate significant product functionalities. With the emergence of generative AI, the creation of epic descriptions will undergo a profound change. AI models can analyze vast amounts of data, extract patterns, and generate detailed epic descriptions automatically. By leveraging AI's capabilities, Scrum teams will be able to expedite the process of identifying and defining epics, enabling more efficient planning and resource allocation.

User Stories

User stories form the foundation of Agile development, capturing the perspectives and needs of end-users. Generative AI has the potential to revolutionize how user stories are created and refined. AI models, trained on vast datasets of user feedback and historical project data, can generate user stories that encapsulate common requirements and pain points. This automation will streamline the process, allowing Agile teams to focus on higher-level discussions and innovation.

Tasks and Sub tasks

Once user stories are defined, Agile teams break them down into smaller tasks and subtasks for implementation. AI's influence will extend to this stage as well. Generative AI algorithms can analyze user stories, identify recurring patterns, and suggest suitable tasks and subtasks automatically. By leveraging AI-driven task generation, Agile teams can reduce the effort spent on repetitive task breakdowns, enabling them to allocate more time to critical development activities.


Generative AI is making significant strides in the field of user story development. By automating the content creation process, reducing time and effort, and enhancing collaboration and creativity, generative AI models and tools are transforming the way software development teams create user stories, effectively saving them resources, time, and effort. With continued advances in the field of generative AI, we can expect to see even more exciting real-world applications in user story creation and beyond.

About the author

Ruth Hadari
Agile Advocate, Engineering Ops Expert

Highly experienced in leading multi-organizational teams, groups, in-shore as well as off-shore. The go-to person who is able to simplify the complex. An agile advocate, experienced in all common methodologies. Responsible for the entire software development lifecycle process from development, QA, DevOps, Automation to delivery including overall planning, direction, coordination, execution, implementation, control and completion. Drives execution, and communicates on status, risks, metrics, risk-mitigation and processes across R&D.

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