Why Writing a BRD Still Takes Longer Than It Should
If you have ever sat down to write a business requirements document after a dense round of workshops and realised you are staring at forty pages of notes, three conflicting stakeholder positions, and a deadline that is closer than it looks, you will know exactly what I mean. The analysis is done. The thinking has happened. But the document does not write itself, and translating good analysis into a well-structured, stakeholder-ready BRD is genuinely hard work.
I have worked through this process more times than I can count across government, utilities, and enterprise environments. The challenge is never that the BA lacks knowledge. It is that converting elicited requirements into a coherent, traceable, professionally structured document is time-consuming in a way that eats into the hours you need for stakeholder management, sign-off chasing, and everything else on the project.
That is the specific problem that a purpose-built AI tool for business analysts can solve. Not by replacing your judgement, but by handling the scaffolding so you can focus on the substance.
What a Generic AI Tool Gets Wrong
Before I explain what works, I want to be direct about what does not. General-purpose AI tools like standard ChatGPT or Copilot can produce something that looks like a BRD. The sections are roughly right. The language sounds professional. But in my experience, the output consistently fails in three specific ways.
- It does not distinguish between business requirements and functional requirements. This is a foundational distinction in BA practice. A business requirement describes what the organisation needs to achieve. A functional requirement describes how a system must behave to support that. Conflating the two is one of the most common errors in requirements documents, and generic AI tools make it repeatedly. If you want to understand why this matters in practice, the article on business requirements documents versus functional requirements documents covers the distinction clearly.
- It has no awareness of BA document conventions. A BRD is not an essay. It has a specific structure: scope, stakeholder context, assumptions, constraints, requirements with unique identifiers, acceptance criteria. Generic tools produce flowing prose where a numbered, traceable requirements register should be.
- It cannot ask you the right questions. The quality of a BRD depends on the quality of the elicitation behind it. A general AI tool takes whatever you paste in and reformats it. It does not notice that you have not documented the out-of-scope items, or that your assumptions section is missing entirely.
A Real Example: Where the Draft Fell Apart
On Project X, I was brought in mid-delivery to support a struggling requirements phase on a payments processing improvement programme for Organisation B, a local government entity. The team had already held four workshops with the finance and operations teams, and the workshop notes ran to thirty-two pages. Two stakeholder groups had actively contradictory positions on how the daily close-out process should work, particularly around how cash and cheque payments were handled across shifts before being consolidated for banking.
The incumbent BA had used a general-purpose AI tool to produce a first draft BRD from the workshop notes. When I reviewed it, the document had seventeen items listed as requirements. Twelve of them were process descriptions, not requirements. There were no unique requirement IDs. The assumptions section had been populated with three lines that were actually constraints. And critically, the document did not reflect the cash-handling discrepancy between the morning and afternoon shift processes at all. The Finance Officer’s reconciliation step had simply been omitted.
When I raised this with the project manager, the initial response was that the draft was close enough to work from. I had to push back firmly. A BRD that does not accurately represent a cash-handling process, including the float adjustment and shift consolidation steps, is not a minor editorial issue. It is an analytical gap that will surface in UAT or, worse, after go-live. The document needed to be rebuilt, not edited.
What I needed at that point was not more AI text generation. I needed a tool that understood BA document structure well enough to ask me: have you captured the exception paths? Have you separated what the business needs from what the system needs to do? Have you documented what is explicitly out of scope? Those are the questions that shape a BRD worth signing off.
How Ash Approaches BRD Writing Differently
Ash is an AI tool built specifically for business analysts, and the difference in approach is structural rather than cosmetic. When you use Ash to write or develop a BRD, it does not just reformat your notes. It works through the document with you using the logic of BA practice.
The starting point is always the problem statement and scope. Ash will prompt you to define what the project is solving for before it helps you write a single requirement. This matters because a BRD written without a clear problem statement almost always ends up with requirements that are disconnected from the business need. If you are still working on clarifying the scope, the business problem statement guidance on this site is worth reading first.
From there, Ash helps you build requirements in the correct format: uniquely identified, written at the right level of abstraction, separated from functional or technical detail. It flags when something you have drafted reads more like a solution than a requirement, and it prompts you to complete sections that are missing rather than leaving gaps.
The Specific BRD Sections Where AI Help Is Most Valuable
In my experience, there are four parts of a BRD where the drafting effort is highest and where having a structured AI tool makes the most tangible difference.
- Assumptions and constraints. These are the sections most often completed last and least carefully. Ash prompts you to distinguish between the two and to frame each one in language that stakeholders can actually review and challenge.
- Scope boundary statements. Documenting what is out of scope is just as important as documenting what is in scope. Most BRD drafts I review are weak here, and it is one of the most common sources of scope creep downstream. For more on that, the article on scope creep governance is directly relevant.
- Requirements phrasing and testability. A requirement that cannot be tested is not a requirement. Ash checks for testability by prompting you to consider how each requirement would be verified during UAT. This alone saves a significant rework cycle.
- Stakeholder impact statements. Who is affected by each requirement, and in what way? This context is routinely absent from first-draft BRDs. Having it in the document reduces sign-off friction because stakeholders can see their interests reflected.
What Ash Does Not Do (And Why That Matters)
Being clear about the boundaries of an AI tool is part of using it well. Ash does not conduct your elicitation for you. It cannot attend your workshops, read the room when a stakeholder is giving you the answer they think you want rather than the truth, or make the judgement call about which requirements to prioritise when two business units are in conflict. Those decisions require human analytical skill and stakeholder relationship management.
What Ash does is reduce the time between finishing your analysis and producing a document that is professionally structured, internally consistent, and ready for stakeholder review. For early and mid-career BAs, that gap is often where quality gets lost, not because the analysis was poor, but because the document production under time pressure shortcuts the structure. Having a tool that holds the structure firm while you focus on the content is practically useful in a way that general AI tools are not.
If you are working from an existing template, Ash complements that approach well. The BRD template in Word format available on this site gives you a starting structure that Ash can help you populate with well-formed requirements and supporting content.
Getting the Most Out of AI-Assisted BRD Writing
After working with AI tools in BA contexts across several projects, I have settled on a few practices that make the output genuinely useful rather than just fast.
- Start with your problem statement, not your notes. Before you put anything into Ash, write a clear two or three sentence statement of what the project is solving for. Everything else should connect back to this.
- Work section by section, not all at once. The temptation is to dump everything in and ask for a full draft. You will get a better document by working through the scope section, then assumptions, then requirements in sequence.
- Challenge every requirement it helps you draft. Ask yourself: is this testable? Is this a business requirement or a system requirement? Is this in scope? Ash will prompt some of these questions, but your own critical reading matters too.
- Use the output as a structured first draft, not a final document. The document still needs your review, stakeholder input, and sign-off. AI-assisted drafting gets you to that review stage faster. It does not replace the review itself.
The analysts who use AI tools most effectively are the ones who treat them as a way to preserve their analytical quality under time pressure, not as a shortcut around it. The BRD is still yours. The thinking is still yours. The AI handles the scaffolding so that the thinking shows up clearly in the document, rather than getting lost in the drafting.
Frequently asked questions
Can I use Ash to write a BRD from my workshop notes directly?
Yes, but the article is clear that dumping all your notes in at once and asking for a full draft is not the most effective approach. You will get a better result by starting with a clear problem statement first, then working through the BRD section by section: scope, assumptions and constraints, then requirements. Ash is built to prompt you through that structure rather than just reformat whatever you paste in.
What is wrong with using ChatGPT or Copilot to draft a BRD?
The article identifies three specific problems with general-purpose AI tools for BRD writing. They routinely conflate business requirements with functional requirements. They produce flowing prose rather than numbered, traceable requirements registers. And they cannot identify what is missing from your analysis: they will not tell you that your assumptions section is absent or that you have not documented out-of-scope items. A tool built around BA document conventions catches those gaps rather than glossing over them.
How does Ash handle conflicting stakeholder positions in the requirements?
Ash does not resolve stakeholder conflicts for you, and the article is direct about that. What it does is help you surface and document those conflicts clearly within the BRD structure, so they are visible for review and sign-off rather than silently omitted. The Project X example in the article shows exactly what happens when a conflicting position between shift processes gets left out of a draft: it becomes an analytical gap that surfaces in UAT or after go-live.
How do I know if a requirement I have drafted is testable enough to include in a BRD?
The article flags testability as one of the four areas where AI-assisted drafting adds the most value. Ash prompts you to consider how each requirement would be verified during UAT. As a quick self-check when reviewing your own drafts: if you cannot describe a concrete test that would confirm the requirement has been met, it is not yet written at the right level. A requirement that cannot be tested is not a requirement.
Is AI-assisted BRD drafting suitable if I am an early-career BA working without much oversight?
The article addresses this directly. For early and mid-career BAs, the gap between finishing the analysis and producing a professionally structured document is often where quality gets lost, not because the analysis is poor but because drafting under time pressure shortcuts the structure. Using a tool that holds the document structure firm while you focus on the content is particularly useful when you do not have a senior BA reviewing your first draft. The output is still a first draft that needs your critical review, but it gives you a solid, structured starting point.
Try Ash, Your Virtual BA
If you are working through a BRD right now and feeling the gap between solid workshop analysis and a professionally structured, stakeholder-ready document, Ash can help you close it. Ash is built around BA document conventions, not general text generation, so it prompts you to define scope before drafting requirements, flags when something reads more like a solution than a business need, and pushes you to complete the sections most BRD drafts leave thin: assumptions, constraints, out-of-scope statements, and testability. Try Ash Virtual BA
Further reading
- How Business Analysts Use AI to Improve Requirements, Elicitation, and Stakeholder Alignment | IIBA
- Workshop. Creating Requirements AI Assistant | Belarus
Written by Sam Cordes, founder of the Business Analyst’s Toolkit.