AI for Business Analysis

Begin by exploring what generative AI actually does and how it applies to business analysis. See examples of chatbots, compare their behaviors, and discuss how AI changes your work—not by replacing analysis but by accelerating it. You'll learn to treat AI as a collaborator that still requires your judgment and critical thinking.

When a new project begins, the hardest part is starting from nothing. In this module, you'll use AI to generate early project artifacts—from an initial idea to a structured Business Analysis Canvas. You'll practice prompting strategies, comparing outputs, and refining AI suggestions into something realistic and actionable.

Learn to describe processes using text-based artifacts such as usage narratives, user journeys, use case briefs, and fully-dressed use cases. Then experiment with converting those into simple process diagrams. See how AI can produce both a narrative understanding of behavior and a visual representation that clarifies responsibilities, success scenarios, and exceptions.

Continue the work of describing the current system by shifting from behavior to information. Learn how a data model captures the vocabulary of the domain. Use AI to generate a data model diagram and to produce definitions for each element. Then learn how to use AI to perform CRUD analysis to connect activities in the process model to the entities they create, update, or delete.

AI can help you understand user needs faster—but you stay responsible for what you learn. In this module, you'll use AI to build detailed personas, then test those personas by generating scenarios, journeys, and empathy maps. You'll learn to evaluate AI outputs for realism and bias, ensuring personas reflect actual users, not stereotypes or assumptions.

AI can help you plan better interviews and analyze results faster—but you're the one who asks follow-up questions, reads body language, and builds trust. In this module, you'll use AI to draft interview scripts and practice with simulated stakeholders. You'll also learn how AI can summarize findings while you retain the judgment to validate those summaries and integrate them into your analysis.

AI can speed up story writing—but only you can ensure stories reflect real users and true business value. In this module, you'll use interviews, surveys, and models as story inputs, evaluate AI-generated stories for quality and privacy, and organize them into coherent, traceable structures that your team can act on.

Building on your user stories, you'll organize them into a prioritized backlog and identify the minimum viable product (MVP). You'll explore how AI can suggest sequencing, dependencies, and scope adjustments, and how to maintain human control over what gets built first.

Generative AI can sketch interfaces in seconds—but it's the BA's role to judge usability and fit. You'll experiment with AI-produced prototypes, apply design feedback loops, and connect UI ideas to user stories and data models. You'll learn when to iterate with AI and when human insight must lead.

Many test cases are necessary to completely validate a software system, yet writing all those tests can be a boring, repetitive, error-prone job. We've used AI to create and evaluate, so it stands to reason that AI can be used to write test cases.

AI can help you see the system as a connected whole. In this module, you'll explore how a single change ripples through requirements, stories, models, and tests. You'll use AI to trace dependencies, maintain consistency, and coordinate updates—strengthening quality and reducing rework.

So what does all of this mean? Now it all comes together. You'll assess where AI can add the most value in your own practice, plan small experiments, and design an ethical framework for responsible adoption. The focus is on practical next steps—how to pilot, measure, and lead change as an AI-empowered business analyst.