User stories as a tool in app design using artificial intelligence
User stories are an excellent tool for the ideation and design phases of software development.
In the age of artificial intelligence, more and more subject matter experts are testing their app ideas themselves using AI before turning to professional software developers. No-code and low-code solutions, as well as large language models (LLMs), have lowered the barrier to entry and made application prototyping more accessible. Within companies, experts can now sketch out app ideas themselves, which AI tools then code without the expert needing any programming skills at all.
However, without clear guidelines, AI can easily generate generic ideas that do not necessarily meet the company’s actual needs. Another real risk lies in the fact that, without software development expertise, an AI solution may be taken too far, resulting in an application that nobody understands and that nobody is able to maintain or further develop.
A user story is an excellent tool for the ideation and design phases of software development. It gives an idea a human perspective and brings it to life at an early stage. It makes the application idea understandable and aligned with objectives – and at the same time, it provides a framework for artificial intelligence to produce useful results. In this way, the idea takes on a concrete form that both artificial intelligence and, later on, the team of software development professionals can easily grasp.
- What is a user story?
- Why do requirements specifications and user stories go hand in hand?
- User stories as a foundation for sprints and agile development
- How to create user stories using AI – a 3-step guide
- Context engineering complements user stories
- Good design makes an idea feasible
- When is it time to seek professional help?
What is a user story?
A user story is a short and clear description, commonly used in software development, of a user’s role, objective and reason – in other words, who the user is, what they want to do and why. Stories are written from the user’s perspective and illustrate how the work being done creates value for the customer. They do not contain technical details; instead, a detailed requirements specification is drawn up at a later stage in collaboration with the team.
Example:
“As a maintenance worker, I want to record maintenance details straight away in the mobile app so that the information isn’t forgotten and is available to my manager.”
This story highlights the user’s role, their goals and the value that achieving those goals brings. A user story is therefore, above all, a tool for understanding why the software matters to the end user. It provides developers and the business with a common language and prevents the technical solution from becoming detached from the actual need.
Why do requirements specifications and user stories go hand in hand?
Most IT project failures are not due to technical problems, but to unclear objectives, unrealistic timelines and insufficient resources. When requirements remain vague, project management becomes more difficult and the end result fails to meet business needs.
Artificial intelligence has made the process of coming up with ideas for apps and creating prototypes faster, but it does not eliminate the need for careful planning. On the contrary: the faster ideas can be refined, the more important it is to ensure that there are clear objectives and a shared understanding of users’ needs. If this stage is neglected, an app may seem promising in the early stages but prove difficult to maintain or develop further in practice.
Requirements specification is the cornerstone of a software project. It ensures that development has a clear direction and that resources are allocated to the right areas. User stories support this by concretizing requirements from the user’s perspective: they make the objectives understandable and help keep the discussion focused on real value, not just technical details.
The role of professionals is crucial in ensuring that stories and other requirements are woven into a coherent whole, leading to a technically sound end result that serves the business.
User stories as a foundation for sprints and agile development
User stories are also one of the cornerstones of agile software development, as they guide the planning of sprints and keep the team focused on the end-user’s needs. They form the smallest unit of work in the agile framework: a story always describes a user’s goal, not a single feature.
In agile development, the INVEST mnemonic (Independent, Negotiable, Valuable, Estimable, Small, Testable) supports the writing of good user stories, ensuring that the story is clear, delivers value and can be implemented within a sprint.
User stories fit seamlessly into both Scrum and Kanban workflows. In Scrum, they are added to a sprint and implemented during that sprint, enabling teams to learn to estimate and plan their work more accurately. In Kanban, user stories move from the backlog through the workflow, which helps manage work in progress and make processes more efficient.
How to create user stories using AI – a 3-step guide
Large language models, such as ChatGPT, Claude and Gemini, have become valuable tools for creating user stories. When provided with basic information about the user, their objectives and business needs, they can suggest several alternative user stories. This significantly speeds up the ideation phase and helps turn abstract ideas into concrete ones.
For example, artificial intelligence can formulate a story into a complete sentence that includes the user’s role, the action taken and the intended benefit. In addition, it can generate different variations of the same story and help determine which option best aligns with business objectives.
Below is an example template from Atlassian for creating user stories:
- “As a user [name of persona]”: Who are we building this for? The aim is not simply to assign a title, but to understand the person as a whole. The team should have a shared understanding of who, for example, Hilma the maintenance worker is.
- “I want to”: This describes the user’s intention, not a technical solution. What is the user actually trying to achieve? The sentence should not include details of the user interface, but should state the user’s goal as clearly as possible.
- “So that”: This explains how the user’s immediate need relates to the bigger picture. What is the benefit or value they are seeking? What larger problem does this solve?
Follow these three steps to quickly and effectively turn an idea into concrete stories. When formulating a prompt, you can use the following basic structure: “As [character’s name], I want to [do something] so that [a benefit is achieved].”
- Step 1: Define a user persona
- Before you begin, think about who the app’s user is. Give the user a name and briefly describe their role, goals and challenges. What is the bigger problem you’re trying to solve?
- Example: Hilma works for a maintenance company and carries out field maintenance. Her biggest challenge is having to fill in maintenance reports by hand on paper, which is slow and prone to errors.
- Step 2: Write a clear prompt
- Use a well-established user story template that provides a clear framework for the AI. Use the following basic structure: “As a [character name], I want to [do something] so that [a benefit is achieved].”
- Prompt: “I’m Hilma, a maintenance worker who’s fed up with paper maintenance reports. I want to record information straight from the field. Write three user stories describing how I could improve my work using a mobile app.”
- Step 3: Provide further context
- You’ll get the best results by providing the AI with more context. Include details about the use case or user journey in your prompt so that the AI can better understand the bigger picture.
- Example: Add to the prompt that Hilma wants to improve route optimisation or view the entire course of her working day within the app. This guides the AI to generate even more accurate and useful narratives that serve broader business needs.
This process immediately turns an idea into something tangible and gives you the tools to assess the value of your app idea even before the first meeting with the developers
Context engineering complements user stories
When the context (who the user is, what they want to do and why) is clearly stated in the prompt, AI is able to make the best possible use of the information. This is a practical example of the concept of context engineering: AI does not inherently understand business objectives, but a properly defined prompt provides it with the framework to generate solutions that support the business.
Context engineering combines use cases, user stories and user journeys into a single framework that enables AI to be guided more precisely. When AI understands who the user is, what they want and the journey they take within the application, prototypes and ideas better address real business challenges.
A use case defines the situation in which the application is used and the problem to be solved. A user story highlights the human perspective: who the user is, what they want to do and why it is important. A user journey, on the other hand, broadens the view from a single situation to the customer’s entire journey and the use of the application at different stages.
When these three elements are combined, the AI is given the tools it needs to generate application ideas and prototypes that genuinely solve business challenges and enhance the user experience.
Good design makes an idea feasible
Artificial intelligence is an excellent tool for refining ideas and creating early prototypes. When it comes to creating user stories, AI speeds up the process and helps give abstract ideas a concrete form. When user stories are combined with requirements specification, agile methods and context engineering, it ensures that AI produces solutions with genuine business value.
At this stage, the importance of design is also highlighted. Well-executed design brings together, among other things, user stories, business objectives and technical constraints. It acts as a bridge between the AI-generated prototype and the production-ready application, ensuring that the whole project remains manageable. The design phase takes into account integrations, data security, user management and scalability – factors without which the solution may become unusable or difficult to maintain.
When is it time to seek professional help?
Artificial intelligence can speed up the initial stages, but building a production-ready application is a complex process. Although AI tools can be utilized throughout the development process, for example in code generation and testing, professional assistance is essential when the application requires complex integrations, extensive user management, data security and scalability.
A professional ensures that the application does not remain merely an idea or a prototype, but becomes a functional system that delivers tangible value to the business. Successful implementation requires a combination of technical expertise, agile methodologies and a deep understanding of user needs – things we cannot leave solely to artificial intelligence.
Have you already created user stories and thought through your app idea in detail? That’s a great starting point for a successful project. Let us help you turn your idea into a functional and secure app.
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