And the POs Will Inherit the Earth: How User Stories Make You Better at AI Prompting Link to heading

Product Owners, Business Analysts, and Solution Architects are unnaturally good at prompt engineering. Not because they studied it, but because they’ve been doing it for years—just with humans instead of AI.

The skills are identical. Breaking down complex problems into clear, testable requirements. Specifying role, goal, and outcome. Defining what “done” looks like. If you can write a good user story, you can write an excellent AI prompt.

The User Story Format (That You Already Know) Link to heading

Standard Agile user story:

As a [role]
I want [feature/capability]
So that [business value/outcome]

Example:

As a customer
I want to filter products by price range
So that I can find items within my budget

This structure forces clarity. Who needs this? What do they need? Why? Three questions that prevent vague requirements and scope creep.

The Prompt Engineering Translation Link to heading

Adapt this for AI:

Acting as a [role]
I want you to [task/capability]
So that [desired outcome]

Example:

Acting as a senior Python developer
I want you to refactor this function for better readability
So that junior developers can maintain it without confusion

The difference is two words: “Acting” and “you to.”

Add “Acting” at the start, change “I want” to “I want you to,” and your user story becomes a structured AI prompt that:

  1. Establishes role/persona (Acting as a senior Python developer)
  2. Specifies the task (refactor this function)
  3. Defines success criteria (junior developers can maintain it)

The AI now knows what expertise to draw from, what action to take, and what the output should achieve. You’ve gone from “rewrite this code” (vague) to a prompt that produces useful results on the first try.

Acceptance Criteria: Teaching AI When It’s Done Link to heading

Good user stories include Acceptance Criteria—concrete, testable conditions that define “done.” This is where POs separate themselves from casual prompt writers.

Standard Gherkin format:

1
2
3

Given [initial context]
When [action occurs]
Then [expected outcome]

For AI prompts, this becomes your validation framework:

Acting as a technical writer
I want you to explain mutual TLS authentication
So that developers without security backgrounds can implement it

Acceptance Criteria:
Given a developer with basic HTTP knowledge
When they read this explanation
Then they can:
  - Explain what mTLS is in one sentence
  - Describe the certificate exchange process
  - Identify when to use mTLS vs regular TLS
  - Implement a basic mTLS client in their language

Now the AI has testable criteria. It’s not guessing what you want—you’ve specified exactly what “good enough” looks like. More importantly, you can verify the output against these criteria. Did it meet all four conditions? No? Iterate.

This separates effective prompt engineering from trial-and-error. You’re not asking “does this look right?” You’re asking “does this meet the acceptance criteria?”

INVEST: Quality Control for Prompts Link to heading

Apply INVEST criteria to AI prompts:

Independent: Each prompt should stand alone. Don’t reference “that thing we discussed earlier” unless you’ve explicitly included that context.

Negotiable: Be specific about what matters, flexible about implementation. “Format as Markdown table” is specific. “Make it look nice” is not.

Valuable: Every prompt should produce usable output. Don’t ask for summaries you won’t read.

Estimable: Know roughly what you’re asking for. “Rewrite this paragraph” is estimable. “Improve my entire codebase” is not.

Small: One clear task per prompt. Don’t ask the AI to “design the system, write the code, generate tests, and create documentation.” That’s four prompts.

Testable: You need to verify the output. If you can’t test it against criteria, you can’t know if it’s right.

Run your prompts through INVEST before sending them. Clear, focused requirements produce better results.

Why Systems Thinkers Win at This Link to heading

Product Owners, Business Analysts, and Solution Architects share a critical skill: systems thinking. You take complex, ambiguous business problems and decompose them into discrete, actionable requirements.

This is exactly what effective AI prompting requires. The business problem is your goal. The AI is your implementation team. Your job is translating one into actionable instructions for the other.

Compare:

Naive prompt: “Make our API docs better.”

Systems thinker’s prompt:

Acting as a technical writer with API documentation expertise
I want you to audit our authentication endpoint docs
So that developers can implement OAuth2 without support tickets

Acceptance Criteria:
Given a developer new to our API
When they read the /auth endpoint documentation
Then they can:
  - Obtain credentials without contacting support
  - Understand the token refresh flow
  - Handle common error codes
  - See working code examples in 3 languages

Current docs: [paste documentation]

You’ve scoped the problem (authentication endpoints, not all docs), specified the role (technical writer + API expertise), defined success (no support tickets), and provided testable criteria. This is systems thinking applied to prompt engineering.

The Convergence Link to heading

We’re entering an era where breaking down complexity into clear, testable requirements is the highest-leverage skill in software development. Not because requirements are new, but because the implementation layer has fundamentally changed.

POs, BAs, and SAs have spent years developing this skill. You already know how to specify role, goal, and outcome. You already write acceptance criteria. You already apply INVEST. You’re already systems thinkers who decompose complexity.

The only thing that changed is who’s implementing your requirements. It’s not a development team anymore—it’s an AI system. But the requirements discipline is identical.

The Product Owners will inherit the earth. Not because AI makes your job obsolete, but because your job was always the hard part: translating ambiguous business needs into clear, testable specifications. The only difference now is that your user stories execute in milliseconds instead of sprints.

Your competitive advantage isn’t knowing Python or frameworks. It’s knowing how to specify what “done” looks like, testably and completely, for any problem domain. That skill doesn’t deprecate. It compounds.

For more on AI integration and Agile practices: