AI & Machine Learning
AI for Business Analysis
Use generative AI as a real partner in business analysis.
3 daysIntermediate12 modules
About this course
Use generative AI as a real partner in business analysis. Over three days you will jump-start projects with AI, model processes and data, prepare interviews, write user stories and design user experiences, while learning to spot AI errors, gaps and bias.
What you'll learn
- 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.
Curriculum
12 modules
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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).
- 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.
- 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.
Who it's for
Business analysts, product owners and anyone who elicits requirements and models business processes.
Before you start
- To get the most out of this course, it is recommended that participants have foundational knowledge of business analysis through formal training like our Business Analysis Bootcamp course or have relevant experience working in a business analysis context.
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