About this course
The future of business analysis lies at the intersection of human creativity and technological leverage. Our AI for Business Analysis course is an immersive and interactive journey to prepare experienced BA professionals for the AI-empowered future.
Through real-world exercises with leading AI agents and tools, gain first-hand experience planning initiatives, analysing requirements and devising solutions. You will learn methods of “prompt engineering” for using generative AI agents to produce key BA artefacts, research new subject matters, evaluate work, and conduct interviews with subject matter experts. While balancing AI’s power to create content with human judgment, you will discover AI dialogue techniques that enable iterative and incremental analysis and development.
Rather than treating AI as a separate skill or a replacement for analysis, this 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, modelling processes and data, eliciting information, writing user stories, planning development, designing user experiences, and validating solutions.
What you will gain
An AI for Business Analysis digital badge will be available upon successful completion of the course from Skills Development Group.
This course will contribute 14 continuing development units (CDUs) or professional development hours towards certifications from the IIBA®.
This course will contribute 14 PMI® Professional Development Units (PDUs) towards your chosen certification (12 Ways of Working and 2 Business Acumen).
What you will 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 analyse interview results
- Organise and prioritise analysis work using AI while retaining control over decisions
- Recognise 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
What you need
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 Essentials course or have relevant experience working in a business analysis context.
This course is great for
- Business Analysis Professionals at any level of experience wanting to utilise 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.
Topics Covered
Understanding AI’s role in Business Analysis
- Capabilities and limitations
- How to evaluate AI outputs
- Its impact on analytical thinking and the BA role
Modelling the current state (process view)
- Using AI to create and refine behavioural artifacts
- Produce process model diagrams
- Identify alternate and exception flows to understand system behaviour
Modelling the current state (data view),
- Using AI to identify entities, attributes, and relationships
- Draft data models
- Construct a CRUD matrix
- Identify missing or unclear activities
Getting to know people
- Using AI to generate and evaluate user personas
- Create scenarios and journey maps
- Distinguish between AI-generated patterns and authentic stakeholder input
Interviewing and elicitation
- Using AI to design questions
- Conduct simulated interviews
- Analyse responses
- Distinguish tasks AI can support from those requiring human interaction
Writing user stories
- Using AI to generate, refine, and classify stories
- Evaluate them for accuracy and ethical considerations
- Maintain alignment with requirements and user needs
Planning development
- Organising user stories
- Using AI to propose MVP scope and sprint groupings
- Prioritising requirements
- Assessing AI recommendations for feasibility and stakeholder value
Designing the user experience
- Using AI to create and refine UI prototypes
- evaluate usability and accessibility
- link designs to requirements and data models
- Integrate AI output with stakeholder feedback
Writing tests
- Using AI to generate test cases and data
- Structure tests in formats like Gherkin
- Evaluate coverage
- Produce automation-ready examples.
Validating, prioritising, and coordinating
- Using AI to identify dependencies
- Update related artifacts
- Maintain traceability across requirements, stories, and tests,
- Support coordinated change management
Implementing AI-powered business analysis
- Identifying pilot opportunities
- Planning change-management strategies
- Develop an ethical framework
- Creating personal or team roadmaps to enhance productivity and quality
