AI BRD Generator: Write Business Requirements Documents in Minutes

In my BA work, the Business Requirements Document is one of the most-produced and most-debated artefacts. I’ve watched stakeholders argue about its length, format, and necessity in every organisation I’ve worked with. Most BRDs I see are too long for executives, too short for engineers, and too generic for either. AI has dramatically changed how I produce BRDs. The right prompts and a disciplined review process turn BRD writing from a multi-day chore into a 2-3 hour exercise that, in my experience, produces sharper output.

A working BRD has these sections:

• Executive summary (1 page max).
• Business context and drivers.
• Project scope (in / out).
• Stakeholders.
• Functional requirements.
• Non-functional requirements (performance, security, scalability,
• accessibility, compliance).
• Assumptions.
• Constraints.
• Dependencies.
• Acceptance criteria.
• Glossary.
• Appendices (process diagrams, data models, supporting research).

Length: 15-30 pages for medium projects, 30-60 for large. Anything longer is unread.

A working workflow:

• BA completes elicitation and synthesis.
• Inputs the synthesised material plus business context to AI.
• AI generates a structured BRD draft.
• BA reviews and edits aggressively.
• BA validates with stakeholders.
• BA finalises and signs off.

Total time from synthesis to final BRD: 2-3 days vs 1-2 weeks pre-AI.

A master prompt that consistently produces strong BRDs:

“You are a senior business analyst at [industry] company. Below is the synthesised research from 18 stakeholder interviews and supporting documents. Generate a BRD with these sections: Executive Summary, Business Context, Scope (In/Out), Stakeholders, Functional Requirements, Non-Functional Requirements (cover security, performance, scalability, accessibility, compliance), Assumptions, Constraints, Dependencies, Acceptance Criteria, Glossary. Tone: precise, no marketing language. Maintain traceability comments noting which interviews support each requirement. Length: 4,000 words. Flag sections where the input is insufficient and propose what additional research is needed.”

The BA edits 25-40% of the output and adds judgement on strategic sections.

Executive Summary: 1 page, lead with business impact. AI tends to write generic summaries. Edit aggressively for organisational voice.

Business Context: AI captures stated context well. Add political and historical context only humans know.

Scope: AI generates clear in/out lists. Validate boundaries with stakeholders.

Stakeholders: AI extracts mentioned stakeholders. Add political dynamics manually.

Functional Requirements: AI generates from synthesis. Validate completeness against stated needs.

Non-Functional Requirements: AI tends to generate generic NFRs. Push for specific numbers (e.g., “response time under 2 seconds for 95% of requests” not “system should be fast”).

Assumptions: AI lists obvious assumptions. Add organisational assumptions manually.

Constraints: AI captures stated constraints. Add unstated organisational constraints.

Dependencies: AI infers from synthesis. Validate with technical and operational teams.

Acceptance Criteria: AI generates from requirements. Make sure each AC is testable.

Different audiences need different framings of the same BRD:

• Executives: 2-page summary leading with business outcomes.
• Engineers: detailed functional and NFR sections.
• QA: detailed acceptance criteria and edge cases.
• Compliance: regulatory mapping and audit traceability.

A useful tailoring prompt:

“Take this BRD and rewrite the executive summary for a C-level audience. Lead with business outcomes. Length: 1 page. Tone: confident, no jargon.”

Before finalising:

• Every requirement has an owner.
• Every requirement is testable.
• Every requirement traces to an interview or document.
• No requirement is duplicated.
• No requirement contradicts another.
• All standard NFR categories are addressed.
• Glossary covers all jargon.
• Acceptance criteria are specific.
• Out-of-scope is explicit.
• Dependencies are named.

A 30-minute pass through this checklist catches most issues before stakeholder review.

Traceability links requirements to:

• Source interviews.
• Stakeholders who validated.
• Implementation tickets.
• Test cases.
• Acceptance evidence.

AI tools (Jama, Modern Requirements, IBM DOORS Next) automate much of this. For BAs without dedicated tools, a simple traceability matrix in Excel or Notion suffices.

For most BAs, a general LLM plus a documentation platform is sufficient.

These are the patterns I see most often when BAs lean on AI for BRD work. I’d flag the first two as the ones that have done the most damage to teams I’ve coached.

• AI hallucinated requirements. In my experience, this is the most damaging error. Always validate against source material.
• Generic NFRs. AI defaults to vague language. I push for specifics every time.
• Bloated documents. AI generates more text than needed. Trim aggressively.
• Missing traceability. Without it, change management becomes painful.
• Skipping validation. AI drafts are starting points. Stakeholder validation remains essential.
• Voice drift. Without a style guide, every BRD reads differently.
• No version control. BRDs evolve. Track versions.

Modern BAs increasingly maintain BRDs as living documents:

• Version-controlled in Confluence/Notion/SharePoint.
• Linked to implementation tickets and test cases.
• Updated with approved change requests.
• Reviewed monthly with stakeholders.
• AI summary of changes since last review.

This pattern eliminates the “stale BRD” problem that plagues classical BA work.

Sign-off rituals matter more than the document itself:

• Per-section sign-off for large BRDs.
• Workshop sign-off with key stakeholders together.
• Asynchronous sign-off with comments enabled.
• Conditional sign-off with explicit assumptions noted.

AI helps draft sign-off summaries, track who has reviewed, and surface remaining open questions.