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Course Overview
AI for Business Analysis a practical, hands-on course for experienced business analysts who want to integrate generative AI into real analysis work—responsibly and effectively. Rather than treating AI as a separate skill or a replacement for analysis, the course positions AI as an accelerating collaborator that still requires your judgment, critical thinking, and accountability. You will work with leading AI assistants to explore how AI can support everyday BA activities such as starting projects, modeling processes and data, eliciting information, writing user stories, planning development, designing user experiences, and validating solutions.
Throughout the course, you will engage in realistic exercises using a shared case study. You will use AI to generate analysis artifacts, then evaluate those outputs for accuracy, completeness, bias, and assumptions. The emphasis is not on producing more content faster, but on learning how to guide, question, refine, and connect AI-generated material into coherent, usable analysis. You will apply techniques such as comparative prompting, iterative refinement, traceability, and ethical review across the full lifecycle of business analysis work.
By the end of the course, you will have a clear, experience-based understanding of where AI adds value, where it introduces risk, and how to maintain human oversight while benefiting from AI’s speed and flexibility. The course is designed for business analysts, product owners, and related roles who want to adopt generative AI in a grounded, professional way—improving productivity and insight without compromising rigor, responsibility, or trust.
Who Should Attend
- Business Analysts wanting to utilize AI to automate and assess analytical tasks and artefacts.
- Development team members wanting to accelerate content creation and insights whilst balancing responsible and ethical oversight.
- Anyone looking to be skilled in AI augmentation and innovation.
Course Objectives
- Use generative AI to get started on analysis work even when information is incomplete.
- Write prompts that clearly describe your problem, context, constraints, and role as a business analyst.
- Review AI-generated analysis artifacts and identify errors, gaps, assumptions, and bias.
- Turn AI output into usable BA artifacts such as process models, data models, user stories, and backlogs.
- Keep requirements, stories, designs, and tests consistent and traceable with AI support.
- Use AI to prepare for stakeholder interviews and analyze interview results.
- Organize and prioritize analysis work using AI while retaining control over decisions.
- Recognize when AI is helping your analysis and when it is adding confusion or noise.
- Apply ethical and responsible practices when using AI, including attention to data sensitivity and bias.
- Create a practical plan for integrating generative AI into your own business analysis work.
Course Outline
Understanding AI’s Role in Business Analysis
- Describe the capabilities and limits of generative AI in business analysis.
- Distinguish between mechanical output generation and analytical reasoning.
- Evaluate AI responses for accuracy, completeness, and relevance.
- Reflect on how AI changes your professional role and responsibilities.
Using AI to Jumpstart a Project
- Apply prompting techniques to create project overview artifacts.
- Compare and consolidate outputs from multiple chatbots.
- Critique AI-generated content for scope, assumptions, and missing details.
- Construct a preliminary Business Analysis Canvas that summarizes the work ahead of you.
Modeling the Current State (Process View)
- Explain the purpose of behavioral artifacts and how they help describe the current state of a system.
- Use AI to generate and refine behavioral artifacts, including use cases.
- Produce a basic process model diagram, based on previously created behavioral descriptions.
- Recognize where alternate and exception flows fit into a complete understanding of system behavior.
Modeling the Current State (Data View)
- Use AI to identify important entities, attributes, and relationships within the current state.
- Use AI to draft a data model diagram in and generate definitions for each element.
- Construct a CRUD matrix that links process activities to data changes. Identify missing or unclear activities.
Getting to Know People
- Identify and categorize stakeholders with AI assistance.
- Construct RACI matrices and personas that reflect diverse, accurate viewpoints.
- Detect and correct bias or stereotypes in AI-generated profiles.
- Balance AI-driven efficiency with genuine human engagement and ethical awareness.
Interviewing and Elicitation
- Design interview questions and scripts with AI assistance.
- Conduct simulated stakeholder interviews using AI personas.
- Analyze and summarize AI-generated interview data or transcripts.
- Differentiate between preparation tasks AI can support and those requiring human interaction.
Writing User Stories
- Generate, refine, and merge user stories from multiple data sources.
- Classify stories by actor, process, or priority to create a story map.
- Evaluate AI-produced stories for accuracy, empathy, and ethical data use.
- Maintain alignment between stories, requirements, and user needs.
Planning Development
- Organize user stories into a backlog or story map.
- Use AI to propose MVP scope and sprint groupings.
- Prioritize requirements using defined criteria or heuristics.
- Assess AI-based recommendations for feasibility and stakeholder value.
Designing the User Experience
- Generate and iteratively refine UI prototypes using AI tools.
- Evaluate AI-produced designs for usability, accessibility, and alignment with business goals.
- Link interface elements to related requirements, data models, and user stories.
- Facilitate design discussions that integrate both AI output and stakeholder feedback.
Writing Tests
- Generate test cases and data from use cases and UI designs.
- Express tests in structured formats such as Gherkin.
- Evaluate the adequacy of AI-generated test coverage.
- Translate test descriptions into automation-ready examples (e.g., Selenium).
Validating, Prioritizing, and Coordinating
- Identify dependencies and impacts across BA artifacts using AI assistance.
- Update related artifacts to reflect requirement or design changes.
- Construct and interpret a traceability matrix linking requirements, stories, and tests.
- Evaluate how AI can support coordinated change management while maintaining control and accuracy.
Implementing AI-Powered Business Analysis
- Identify high-potential areas to pilot AI within your BA processes.
- Plan change-management and measurement strategies for AI adoption.
- Develop an ethical framework for responsible use of AI tools and data.
- Create a personal or team roadmap for integrating AI as a productivity and quality multiplier.