“Is it done?” It’s a relatively simple question that most of us encounter in our jobs almost every day, regardless of our role. Most of the time, the answer is straightforward. Yet for product managers working on new features and designing user stories, the “Definition of Done” is far more challenging and comprehensive. It’s easy to focus solely on the core requirements, but here’s the reality: incomplete or ambiguous requirements lead to delays, rework and, at times, a less-than-ideal user experience.
That’s where AI can make a difference. AI has the ability to help teams tackle this challenge head-on, transforming how we define user stories and acceptance criteria, which later on directly impacts software testing and the way we define our test cases.
We’re already seeing AI’s influence as more companies harness its potential by integrating AI capabilities into their software solutions and teams leveraging it in some parts of their testing processes. This is the beginning of a significant transformation, where AI is not just a helpful tool but a driving force to deliver better software faster and with greater confidence.
‘Definition of Done’ And Acceptance Criteria
In Agile methodologies, there is a known term called “Definition of Done,” or simply DoD. Let’s make it clear right from the beginning: DoD and acceptance criteria are not the same thing. DoD is a set of criteria defined by the entire team that must be met in order for it to be ready for end-user usage. In a way, it is a shared understanding of what needs to happen for a feature to be considered complete, setting the minimum set of requirements we all agree on before moving forward.
Acceptance criteria, on the other hand, dives deeper into the specifics of a single user story. While the DoD provides a broad standard for all backlog items, acceptance criteria focus on the functionality and outcomes that define success for one particular user story. Together, these two tools give us the structure we need to deliver software that meets both business goals and user expectations.
Leveraging AI To Strengthen User Stories
Even after understanding the DoD and acceptance criteria, defining user stories is rarely as simple as it seems. User stories that look thorough on paper might fail to account for all the scenarios that matter, as there are edge cases, real-world conditions or even the way users interact with a system can be overlooked. And that’s where I see AI stepping in—not as a replacement for human expertise but as a powerful tool to augment it.
One way AI can be leveraged is by identifying gaps in existing requirements and suggesting improvements to make them more comprehensive. For example, AI can analyze the requirements provided and flag areas where critical details might be missing. On the other hand, AI can also generate initial drafts of user stories or acceptance criteria, providing a strong starting point for product managers. This ability to refine or initiate user stories ensures that teams have a more solid foundation to build on, saving time and reducing ambiguity.
AI can also analyze a complete set of stories already defined and developed as part of the product and point toward areas where similar functionality is already in place. This will help product managers ensure this new functionality fits these existing similar areas in the product.
Later on, AI can complement this process by analyzing test cases, bug reports or system usage data to recommend scenarios that might otherwise go unnoticed. For instance, AI can flag areas where similar features have failed in the past or highlight edge cases that have historically caused issues in production. These insights can help teams anticipate problems and create user stories that are not only complete but also proactive in addressing potential risks.
Of course, AI isn’t perfect. The output often requires refinement, and human judgment remains critical. AI also lacks the human factors of ingenuity and our ability to think outside the box. But when used effectively, AI can act as a second pair of eyes, helping to ensure that no detail is overlooked.
Enhancing QA With AI-Augmented Test Management
The job of QA teams is not to catch every single defect but to catch the critical ones that harm user expectations from our software. What needs to be done is to test smartly and design test cases using critical thinking that will cover the user stories from multiple possible scenarios and edge cases. It’s our job to anticipate how users will interact with the product, identify where it might break and ensure it performs as intended under all conditions.
AI is already starting to change how we approach test management. While still in its early stages, AI-powered test management tools can suggest tests based on user stories, generate test data such as steps and even analyze historical data to assess the value our test cases provide. For instance, AI can help teams determine the value of each test by analyzing patterns over time—highlighting tests that catch the most critical issues or identifying redundant ones that may no longer add value.
Of course, it still requires human intervention to refine test cases and validate generated steps. To put it into perspective, AI doesn’t replace the expertise of testers but rather complements it, acting as a force multiplier that allows testers to focus on tasks their human judgment is needed like test strategy and exploratory testing.
The Future Is Already Here
As AI evolves, we’ll likely see even greater capabilities: tools that can analyze stories and requirements and provide risk calculations, predict which tests will be most effective for a specific release, automatically prioritize test coverage or generate highly realistic test environments. By combining human expertise with AI’s growing capabilities, QA teams will be able to deliver higher-quality software faster, with fewer blind spots and greater confidence.
https://www.forbes.com/councils/forbestechcouncil/2025/01/16/accelerating-qa-shift-left-strategies-with-the-power-of-ai/a>