This is what usually happens: 100+ pages of a BRD land in your inbox on Monday, and sprint planning is Tuesday morning. Then, as a BA, Product Owner, Scrum Master, etc., you start with the usual run:
- You pull-out high level work items like epics.
- Turn them into user stories and write acceptance criteria manually.
- Create a CSV file.
- Import that CSV into Azure DevOps, then map to relevant work items and refine them.
In this process, steps might get missed along the way. One analyst on Microsoft’s Q&A forum explained that he imported stories under a feature and found the parent links silently dropped because Azure DevOps ignores a Parent ID column.
In this blog, we are going to cover why BRD to user-stories conversion is not a simple task, how to use AI tools within Azure DevOps to extract user stories from a BRD while staying compliant, and review those user stories before publishing them in the backlog.
Why a BRD Resists Conversion Into User Stories
A BRD rarely comes in a format that can be directly converted into user stories and added into the backlog. Here is why many teams struggle:
- Requirements buried in prose: A single section or paragraph can contain business objectives, field validation rules, compliance rules, etc., and then analysts need to decide whether each belongs to an epic, feature, user story, or supporting requirement. During manual extraction, it becomes easy to skip a condition or repeat something already captured.
- Actors are not always clear: BRDs often say “the system should” instead of naming the person who needs the outcome. A useful user story needs a clear user or stakeholder, an action, and a reason.
- Acceptance criteria need another pass: Turning a requirement into a story is only part of the job. The analyst still has to define what must be true before the story can be closed. Microsoft recommends using acceptance criteria as the basis for checking whether the requirement is complete.
- The backlog needs structure: Once requirements are extracted, teams need to structure them in a proper ADO hierarchy. In such cases, manually linking relevant work items is the best way to miss important links.
- Manual work creates gaps: By the time the BRD becomes a backlog, someone has read, extracted, rewritten, reviewed, imported, mapped, and refined the requirements. Every extra handoff creates another place for context to disappear.
Turning BRDs Into ADO Work Items With Copilot4DevOps
In this section, let’s look at how to solve challenges explained in the previous section and automate ADO work item extraction from BRDs by using Copilot4DevOps, a requirements management AI assistant that works directly within Azure DevOps.
Step 1 – Upload BRD file
Simply create a BRD file and upload it into the Elicit function of Copilot4DevOps instead of pasting fragments in a chat window.
Step 2 – Write instructions
After that, set the type of ADO work item you want to generate. It allows you to generate epics, features, user stories, test cases, tasks, etc., directly from the BRD. Furthermore, you can add additional instructions about content format and length, and generate work items in 40+ languages.
Step 3 – Generate work items and review
Once setup is done, you can generate work items from the BRD. Copilot4DevOps reads the whole document and processes the content and images, and generates different types of work items based on what you selected. The best part is that it does not miss requirements buried inside paragraphs and writes edge cases also.
Step 4 – Edit work items and publish inside ADO backlog
Once work items are generated, you can use AI Edit functionality and update them using natural language instructions. Once everything looks good, you can directly publish into the ADO Backlog without switching between multiple tabs.
Also Read: How to Write Acceptance Criteria in Azure DevOps (With Real Examples)
What Copilot4DevOps Decides and What You Still Decide
Copilot4DevOps just writes the first draft of ADO work items. After that, a human needs to review those work items and give final approvals. This is very important for teams working in regulatory industries.
| Copilot4DevOps Handles | You Still Decide |
|---|---|
| Reads the supplied document and uses it as context for elicitation. | Whether the source requirement is actually clear and complete. |
| Suggests Epics, Features, User Stories, descriptions, and other selected work-item fields. | Whether each item belongs at the right requirement altitude. |
| Generates additional candidates when the first set is incomplete. | Whether the actor, outcome, and business need are correct. |
| Uses AI Edit to rewrite, shorten, clarify, or refine work-item content. | Whether the wording still reflects what the business actually asked for. |
| Creates linked work items in Azure DevOps and keeps the parent-child structure connected. | Whether contradictions, duplicate requirements, or out-of-scope statements should be removed or clarified. |
| Carries the work into Azure DevOps so the backlog is ready for review. | What should finally be accepted, changed, or rejected before planning. |
What Should Not Become a User Story
Not every sentence in a BRD should become the user story. So, here is what you should not add as a user story.
- Business objectives should be written as an epic or feature instead of user stories. For example, goals like “increase customer retention” explain the reason for the work, not a specific user outcome.
- Out-of-scope statements should not be included in the user stories, but teams can keep them in the document always.
- Implementation instructions should not be written as user stories; instead, they should be followed while writing user stories.
- Vague sentences such as “the system should be easy to use” need clarification before they enter the backlog.
- Duplicate or conflicting requirements should be flagged for review rather than turned into separate stories.
Also Read: AI Test Case Generation from User Stories in Azure DevOps
Final Steps
From this, you can clearly understand that turning a BRD into Azure DevOps work items should not be a manual task that introduces gaps, which are very expensive to cover in regulatory industries.
Also, instead of using scattered AI tools like ChatGPT, Claude, etc. for generating ADO work items from a BRD, which might miss edge cases and do not generate work items in a format defined by the organization, teams can use Copilot4DevOps that follows organizational templates. If you are working in the regulatory industry, it also logs when a work item is generated using Copilot4DevOps, which is very helpful while preparing an audit report.
FAQs
Yes, you can either upload a BRD, or you can give an existing ADO work item as context to generate user stories using Copilot4DevOps. This is useful when you have generated epics and features from a BRD and want to convert those work items into user stories or test cases.
Yes, you can add custom instructions that might contain naming rules, format, description length, etc. Copilot4DevOps follows those instructions and generates ADO work items. You can even save reusable instructions into the text block and reuse them while generating work items.
Yes. The Elicit workflow can generate different Azure DevOps work-item types, including Epics, Features, User Stories, Tasks, Bugs, and test-related items, depending on the workflow and project setup.
You can still use Copilot4DevOps. Azure DevOps uses different requirement work-item names depending on the process template. For example, Agile uses User Stories, while Scrum uses Product Backlog Items.
Copilot4DevOps is SOC Type 2 certified, so it doesn’t use your data to train models or share it with any third-party service providers. All your data is stored within your ADO workspace only.
When any requirement changes are introduced, Copilot4DevOps helps teams to assess the impact of those changes on other work items. Once impacts are identified, teams can use the AI Edit functionality of Copilot4DevOps to edit/update work items using natural language instructions.
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