How to use genAI for requirements gathering and agile user stories

Generative AI is driving a significant paradigm shift in how developers write code, develop software applications, reduce technical debt, and improve quality. GenAI isn’t just writing code, and there are opportunities for the entire agile development team to use LLMs, AI agents, and other genAI capabilities to deliver improvements across the software development lifecycle.

Improving requirements gathering and the quality of agile user stories is becoming a significant opportunity for generative AI. As developers write code faster and more efficiently, deeper agile backlogs with more user stories and stronger acceptance criteria are needed.

How AI copilots increase developer productivity

Developers use genAI to transform software development, including generating code, performing code reviews, and addressing production issues. BairesDev reports that 72% of developers are now using genAI capabilities, and 48% use genAI tools daily.

AI copilots and genAI code generators are impacting productivity significantly. A recent report based on field experiments with software developers at Microsoft and Accenture found that using a coding assistant caused a 26% increase in the weekly number of completed tasks, a 13% increase in the number of code commits, and a 38% increase in the number of times code was compiled.

Developers are also reporting productivity impacts. According to DORA’s 2024 State of DevOps Report, more than one-third of respondents described their observed productivity increases as either moderate (25%) or extreme (10%) in magnitude.

Why requirements gathering is the new bottleneck

As agile development teams become more proficient with code generators, the velocity and quality of requirements gathering and agile user stories must increase. Additionally, the structure of agile user stories and the completeness of their acceptance criteria have become more important as developers use them to prompt AI agents to develop, test, and document code.

“In a world where copilots are writing code, planning will take on a much more important role, and the requirements documents must become more detailed than the days when teams sat together in the same room,” says David Brooks, SVP of evangelism at Copado. “Business analysts will use genAI to summarize feature requests and meeting transcripts to capture all of the inputs and help prioritize based on the level of need. GenAI can then write the first draft or review the human-written draft for completeness to ensure that it aligns with the company’s format.”

The key to success is engaging end-users and stakeholders in developing the goals and requirements around features and user stories. This engagement must go beyond the usual responsibilities of agile product owners; software developers should engage stakeholders in understanding objectives, discussing risks, and devising experiments.

How genAI improves requirements gathering

Chris Mahl, CEO of Pryon, says genAI is reshaping requirements gathering from a documentation exercise to a collaborative discovery process. “Product owners now use AI to generate initial requirement drafts from stakeholder interviews, then refine them through feedback cycles. The business analyst role is evolving from documentation specialist to AI orchestrator, and success requires proficiency in prompt engineering and framing business problems to elicit optimal AI responses.”

The business analyst partners with the agile product owner and team lead to oversee the end-to-end requirements process. They are especially valuable for more technical agile teams working on microservices, integrations, and data pipelines. User stories in these technical deliverables have significant non-functional acceptance criteria, and testing often requires building synthetic test data to validate many use cases.

Mahl adds, “The technology excels at translating business needs into technical specifications and vice versa, bridging communication gaps. Critical thinking becomes essential as analysts must validate AI-generated content for accuracy and business alignment.”

Critical thinking is a crucial skill set to develop as more requirements, code, and tests are developed using genAI tools. Agile developers must learn how to ask questions, include the most important details in prompts, and validate the completeness and accuracy of genAI responses.

Business analysts and product owners have new tools to accelerate translating conversations, brainstorming, and other meeting notes into ideas, epics, and features. Tameem Hourani, principal at RapDev, says, “By joining conference calls, analyzing them, summarizing them, and extracting takeaways from them, you can suddenly groom backlogs for epics of all sizes.”

How genAI supports rapid prototyping and faster delivery

A second opportunity for agile development teams is to use genAI to reduce cycle times, especially around proof of concepts and iterating through end-user experiences. GenAI should help agile teams incorporate more design thinking practices and increase feedback cycles.

“GenAI tools are fundamentally shifting the role of product owners and business analysts by enabling them to prototype and iterate on requirements directly within their IDEs rapidly,” says Simon Margolis, Associate CTO at SADA. “This allows for more dynamic collaboration with stakeholders, as they can visualize and refine user stories and acceptance criteria in real time. Instead of being bogged down in documentation, they can focus on strategic alignment and faster delivery, with AI handling the technical translation.”

One opportunity is in the development of low-code platforms that can generate applications from genAI prompts. Platforms like Adobe, Appian, Pega, Quickbase, and SAP use genAI tools to accelerate the prototyping and development of apps and agents.

Use genAI tools to focus more time on human innovation

Product owners and business analysts have more significant roles than grooming backlogs and documenting requirements. Their strategic importance lies in promoting innovations that matter to end users, delivering business value, and creating competitive advantages. They must also adhere to devops non-negotiable standards, steer agile development teams toward developing platform capabilities, and look for ways to address technical debt.

“GenAI excels at aligning user stories and acceptance criteria with predefined specs and design guidelines, but the original spark of creativity still comes from humans,” says Ramprakash Ramamoorthy, director of AI research at ManageEngine. “Analysts and product owners should use genAI as a foundational tool rather than relying on it entirely, freeing themselves to explore new ideas and broaden their thinking. The real value lies in experts leveraging AI’s consistency to ground their work, freeing them to innovate and refine the subtleties that machines cannot grasp.”

GenAI can enable transformation capabilities when organizations look beyond productivity drivers. Agile development teams should use genAI to accelerate and improve requirements gathering and writing agile user stories. GenAI provides the opportunity to streamline these tasks and improve quality, leaving more time for product owners and business analysts to increase focus on where technology provides lasting value to their organizations.