There is a skill gap emerging in the BA profession right now, and most BAs do not yet know it is happening.
You have probably already started using AI to help draft requirements. Maybe you use it to generate user stories from meeting notes, produce a first draft of acceptance criteria, or structure a business rules document. That is a real productivity gain, and it is worth doing.
But there is something we need to address: The requirements you are producing are increasingly going to be consumed not just by human developers, but by AI development agents. Tools like GitHub Copilot, Cursor, and a growing category of agentic coding systems are reading your specs, interpreting your acceptance criteria, and generating code from them with minimal human intervention. And they do not read requirements the way a human developer does.
The other shift related to this is that these same tools can create specs. They can read existing code, documents, and more; interpret your project information and create the specs.
A human developer can ask a clarifying question, and AI Agents that code do as well! They can infer intent from context. They can recognize when something is ambiguous and make a reasonable judgment call, then flag it for review. An AI development agent also fills in gaps with its own assumptions, not what you meant.
If your requirements are not structured to be read by AI systems, you are not just getting slower results. You are getting wrong results, and you may not realize it until the wrong thing has already been built.
This is the skill the next generation of BAs needs to develop, and it is not the same as writing good traditional requirements. It requires a different way of thinking about what a requirements document is actually for.
The Document Has a New Reader
For most of BA history, requirements documents were written for human readers: developers, testers, project managers, and business stakeholders. Good requirements meant unambiguous, complete, testable, and traceable.
When a human reads a requirement, they will probably ask a clarifying question or make a reasonable assumption and run it by someone. The ambiguity gets resolved through human communication. And, of course we have also seen this go sideways and poor requirements and judgement has created buggy solutions.
When an AI development agent reads “the system should handle errors gracefully,” it has no such anchor. It will generate something. It will look plausible. It may even pass a superficial review. But it will reflect the AI’s interpretation of that phrase, not yours, and not the business’s.
The same problem shows up in every kind of ambiguity that BAs have historically gotten away with leaving in requirements: implicit assumptions about user context, vague performance expectations, undefined edge cases, business rules that were “understood” by the team, acceptance criteria that relied on a reviewer’s judgment to interpret.
Human teams developed workarounds for all of these. They asked questions. They had conversations. They built shared understanding through iteration. AI agents do not do that in the same way.
Writing requirements for AI Agents means being far more structured, disciplined, and deliberate than most BAs have been trained to be. Not because human teams could not benefit from that precision too, but because AI agents have less fallback when precision is missing.
Tip 1: Make Context Explicit, Not Assumed
The most common gap in requirements that both humans and AI agents struggle with is not the absence of details. It is missing context. This issue compounds when AI Agents are the ones coding.
AI development agents perform much better when they understand not just what to build, but why it is being built, who it is being built for, and what constraints shape the solution. That context, which a human team absorbs through meetings, conversations, and project history, needs to be written directly into the requirements structure if it is going to be available to an AI agent working from a spec.
This means every requirements document or feature spec needs to open with a context layer that establishes the business objective, the user or customer need being addressed, the key constraints the solution must operate within, and any organizational or technical context that shapes what good looks like.
For each feature or capability you are specifying, ask yourself: if this requirement was the only thing the AI agent read, would it have enough information to make the right decisions about implementation? If not, what is missing? Business objective. User scenario. Relevant constraints. Known edge cases. Related systems or dependencies.
An AI agent that builds from rich context produces outputs that need less correction. An AI agent that builds from thin context produces outputs that require the team to diagnose what went wrong and why, often without a clear trail back to the requirements gap that caused it.
Tip 2: Structure Requirements for Machine Parsing, Not Human Reading
The second shift is about format and structure, not just content.
This is evolving fast, and can be AI Agent/Tool dependent, as their parsing and memory processing vary.
This means that after the context, being clear about who the users are and what actions users can take. Structuring user goals and features, showing how they relate. Formats like Given/When/Then work well for acceptance criteria because they make the triggering condition, the action, and the expected outcome explicit and separable. For business rules, enumerated, conditional formats work better than prose descriptions because they make the logic machine-parseable. For non-functional requirements, quantified thresholds work better than qualitative descriptions because they give the agent a measurable target.
This Is a New BA Skill For Many, and an Extension of an Old One
It is worth being direct about what this represents.
Writing AI-readable requirements is not a minor adjustment to how BAs have always worked. For many, it is a skill that needs to be developed deliberately, because the mental model underneath it is different. It requires thinking about your document as input to a system, not just communication to a team. It requires understanding how AI development agents interpret and execute on requirements, not just how human developers do. It requires building habits around context completeness, structural consistency, and precision that go beyond what most have experienced.
The BAs who develop this skill early are going to have a genuinely differentiated capability. As AI development agents become more prevalent, the quality of the requirements they receive will become one of the most significant variables determining how well AI-assisted development works. A team that provides rich, structured, AI-readable specs will get dramatically better results from AI development tools than a team that feeds the same tools requirements written for a different era.
That quality gap will be visible. And it will be attributable to the BA practice.
This is an area where early investment pays compounding returns. The habits of writing for AI readers, once built, also improve the clarity and usability of requirements for human readers. The teams that build these habits now will have a significant head start when AI-assisted development becomes the standard way organizations build software, which is not a distant prospect.
The BAs who figure this out first are not just keeping up. They are setting the standard.