How AI Helps You Create User Stories Automatically

Mar, 31, 2025 | Read 4 min.

User stories are one of the most widespread practices in agile development. In theory, these should be simple and focused on delivering value to the user. However, in reality, writing good user stories is not as easy as it seems.

So let’s start at the beginning. In this article, we’ll explain what a user story is, the most common mistakes made when writing them, and how artificial intelligence can help you create and refine them automatically. The result? I In just a few minutes, you’ll have a clear, estimable, and business-aligned backlog.

What is a user story?

A user story is a short and concise description of a functionality or need from the perspective of the end user. It is a key tool within agile methodologies, designed to communicate what is needed, who needs it, and why.

The most common format is:

As [role or user type]
I want [functionality or action]
So that [expected value or benefit]

Example:

As a salesperson, I want to be able to export the monthly sales report in PDF format, so that I can analyse the performance of the sales team.

Characteristics of a Good User Story: The INVEST Model

For a user story to be truly effective, it should meet these 6 principles of the INVEST model, which is an acronym for:

Independent: Can be developed separately from other stories.
Negotiable: it is not a rigid contract, but a starting point for conversation.
Valuable: it provides a specific benefit to the user or the business.
Estimable: The team can calculate the effort required.
Small: Must be able to be completed in a single sprint.
Testable: it includes clear acceptance criteria that allow it to be verified.

Applying these principles of the INVEST model is the first step in making sure that a story is well-constructed. But there’s more, because just good writing may not be enough. Now it is necessary to define how we will know if that story is fully and correctly implemented. This is where acceptance criteria come into play.

Acceptance criteria: the piece that makes the difference

The acceptance criteria define the conditions that must be met for a story to be considered finished. They work as test scenarios, ensuring quality and alignment with requirements, and are essential to avoid ambiguities and facilitate the work of QA and testing.

Example of acceptance criteria for the previous story:

The admin user can select a date range before exporting.
The generated report must contain sales data organised by month.
The exported file must be in PDF format, with a maximum size of 5 MB.

Thanks to these acceptance criteria, the team can know exactly what is expected and the risk of misunderstandings between business, development, and testing is reduced. They act as a quality contract and validation point: if the criteria are met, the story can be considered finished and ready to deliver value. In addition, by including them right from the start, it makes test automation and planning easier, helping to speed up the feedback process.

The most common mistakes when writing user stories

At this point, you might think that writing these user stories doesn’t seem that difficult. But the reality is that, even in experienced teams, it’s quite common to find stories that:

Are written in an ambiguous or vague way.
Are too large or too small, impacting planning.
Lack acceptance criteria or have poorly defined ones.
Focus on the technical solution rather than the user’s problem.
Are not well prioritised, leading to non-urgent stories that distract the team from real business needs.

And when these mistakes are made, it ends up being reflected in various areas: low productivity, conflicts between roles, incomplete deliveries and a loss of focus on the end user. Have you ever experienced it?

How Quanter solves these problems with generative AI

The explosion of Artificial Intelligence opened the door to new solutions. What if we could automatically improve the quality of user stories before proceeding to estimate them? And that’s how we at Quanter developed a new functionality based on Generative Artificial Intelligence (AGI). Let’s see how it works.

Enhanced Requirements and User Stories with AI: analyse and optimize your stories automatically

It is very simple. Just enter the story in natural language and QuanterAI will do all this for you:

Rewrite the user story in the correct format and justify the changes made.
Identify ambiguities or inaccuracies.
Add consistent and comprehensive acceptance criteria.
Adapt the story to your organization’s guidelines and standards.

From user story to backlog in minutes

Once optimized, the story is ready to be estimated. Quanter converts the functionality described into an accurate estimate of effort and cost, and this allows you to make better decisions from the start.

And also … Full integration with the rest of your organization’s tools

Export stories wherever you need them: Jira, Excel, PDF, etc.
Copy, paste, and share clear and validated stories with the whole team.
It automates the definition-estimation flow and improves planning speed.

As you can see, there are many benefits of using AI to write better user stories, but if you still have doubts, we give you a brief review of the most important ones:

Saving time in drafting and validation
Fewer ambiguities and fewer misinterpretations
Improved cross-role collaboration
Faster and more realistic planning
Cleaner, prioritized backlogs

Try QuanterAI and transform your user stories

So now you know, with Quanter and its AI-powered Requirements and User Story Enhancement, you no longer need to waste hours writing and arguing about what the writer did or didn’t mean. Now you can trust that each story complies with best practices and is ready to be estimated and planned. And your teams can focus on what really matters: developing high-quality software with less uncertainty and more efficiently.

Want to see it in action? Try Quanter and transform the way you work with user stories.