If you’re a business analyst and you know AI is the next skill you need to master—but you’re not sure how to get real, tangible, and useful results—this post is for you.
Maybe you’ve tried AI, but the output felt a little off. Or maybe you haven’t even started because it feels overwhelming and you don’t know where to begin.
Or perhaps you’re already using AI daily (which is a fantastic practice!), but you still sense there’s untapped potential waiting for you.
Here’s the truth: It’s not just about using AI—it’s about how you communicate with it. Prompt engineering is a critical skill for business analysts who want to be more purposeful and intentional in how they use AI.
In this post, I’ll walk you through five types of results you can expect from AI, along with practical prompt examples to help you draft, refine, and even improve a business process. Let’s dive in!
Why Prompt Engineering Matters for Business Analysts
AI isn’t magic. It’s powerful, but it needs clear input to give you the right output. That’s where prompt engineering comes in.
Think of it like this: You wouldn’t give vague requirements to your development team and expect a perfect solution. The same applies to AI. You have to be clear, specific, and intentional.
The 5 Types of Results Business Analysts Can Get When With Good AI Prompts
When you craft your prompts effectively, you can unlock five high-value types of results:
1. Prompt Engineering Opportunity: Draft a Deliverable
AI can generate a solid first draft of just about any type of deliverable. Your results improve significantly when you use annotated templates to “train” AI on what you expect.
Prompt example:
“Using this business process template and the instructions provided, please draft a current state business process to enroll new composting customers.”
We’ll walk through this one in a demo below.
2. Prompt Engineering Opportunity: Refine A Deliverable
Already have a deliverable? Ask AI to revise or enhance it to meet a specific standard or expectation.
You can provide specific updates to make, such as improving clarity, applying formatting, or removing passive voice.
Prompt example:
“Please revise this document to remove passive voice and ensure every step specifies who performs the action.”
3. Prompt Engineering Opportunity: Identify Questions to Ask Stakeholders
Upload a deliverable, meeting notes, or user story, and ask AI:
“What questions should I be asking to clarify or validate this with stakeholders?”
This can help you create stronger alignment, reduce ambiguity, and uncover potential risks early.
4. Prompt Engineering Opportunity: Optimize a Process or Solution
Ask AI to suggest improvements based on a particular business objective.
Prompt example:
“How can we streamline this process to reduce customer onboarding time by 20%?”
This shifts AI from a passive assistant to a strategic collaborator.
5.Prompt Engineering Opportunity: Research
Before you even get to deliverables, AI can help you research terminology, explore new domains, or understand systems—without accessing any confidential information.
Prompt example:
“Explain the typical components of a supply chain management system for the retail industry.”
And remember: Always check your organization’s AI use policies before inputting any proprietary or sensitive data.
You Don’t Need Perfectly Engineer Prompts (Just Start Somewhere)
Some business analysts feel stuck because they think they need to engineer the “perfect prompt” from the beginning. That’s not the case.
Personally, I treat AI like a collaborative partner. I’ll often write a rough prompt, see what comes back, then revise it based on what I learn
Here’s a trick I love:
“What would you say to a highly intelligent high school student to explain what you need?”
That’s usually the right level of detail to give AI clear context.
You can also go the other way—invest more time into building a deep, detailed prompt. Both strategies work, and it depends on your style.
Prompt Engineering Demo: Drafting a Business Process With AI
Let’s walk through a real example: using ChatGPT to draft a business process.
I started with a business process template.
This template includes annotated instructional text, originally written for BAs—but it doubles as guidance for AI.
Sections in the template include:
Purpose
Entry criteria
Inputs
Activity descriptions
Exception flows
Outputs
Interested in learning more? Check out this video tutorial on analyzing a business process.
Step 1: Upload the Annotated Template and Write Your Prompt
I uploaded the template and gave AI a prompt like:
“Using this template and the instructions provided, please draft a current state business process to enroll new composting customers.”
I also included a few sample steps and business rules I already knew, just to steer AI in the right direction.
Step 2: Review the First Draft
The output was solid:
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Clear purpose statement
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Defined start and end points
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Good activity descriptions
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Useful exception handling
But I noticed a few areas for improvement, such as:
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A step was written in passive voice: “Once billing is confirmed…”
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Exception flows were embedded in activity steps rather than referenced separately
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Some ambiguity remained around responsibility for key actions
Step 3: Prompt AI to Refine the Output
So I asked AI to make four updates:
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Number the exception flows
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Remove exception details from activity descriptions and reference them instead
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Rewrite passive language into active steps
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Review the entire process for similar ambiguities
The updated version was cleaner, clearer, and easier to follow. Was it perfect? No. But I’d reached the point of diminishing returns—and it made sense to finish the refinements manually.
Final Thoughts: Let AI Do the Heavy Lifting (But Keep the Analyst in Charge)
You’re not asking AI to be perfect. You’re using it to accelerate your work so you can focus on analysis, decision-making, and stakeholder collaboration.
And as you learn and get better at engineering prompts for your work as a business analyst, you are going to be more prepared to help your teams and your stakeholders do the same. And that’s what you know coming next. There are already some organizations that are on the leading edge of deploying AI within their businesses, within their business processes, and there’s some great business analysts doing that work.
But if your organization is not, the skill set to focus on now is prompt engineering.
The more intentional you are with your prompts, the better your results will be. AI needs context and will do better when you are specific about what you want as a result. Annotated templates are a great way to train AI on what you expect.
Ready to Start?
Grab the free Business Process Template, try the prompt examples above, and begin using AI more confidently today.
Looking for More Guidance on How to Engineer Prompts? Use AI to Draft a Scope Statement
A strong AI skill set will support you in thriving in a continued career as a business analyst. If you do want even more help on amplifying your value and being more strategic and thinking ahead about what’s coming both with AI but also with strategic business analysis, I have a video on creating a scope document that will really help you be more forward thinking and proactive about managing scope.
And in this video, I share another example of how to engineer an AI prompt to draft documentation.