Upcoming Sessions

Sessions with asterisk (*) are guaranteed to run. Click date to register.

Feb 9 - 11, 2026 12:00pm - 4:30pm EST* (SESSION FULL)

Mar 10 - 12, 2026 12:00pm - 4:30pm EDT

Apr 20 - 22, 2026 12:00pm - 4:30pm EDT

Jun 3 - 5, 2026 12:00pm - 4:30pm EDT

Aug 12 - 14, 2026 12:00pm - 4:30pm EDT

Oct 5 - 7, 2026 12:00pm - 4:30pm EDT

Dec 7 - 9, 2026 12:00pm - 4:30pm EST

Course Format and Delivery

Delivery Method: LiveOnline 
Schedule: 3 sessions of 4.5 hours
Cost: $1,350 USD

All sessions are delivered live by an expert instructor in a fully interactive online environment.

*20% off for group bookings when booking 3 or more attendees from the same organization on the same course dates in the same transaction.

About this course

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.

Why this course

This course stands out because it teaches experienced business analysts how to use generative AI as part of real analysis work, not as a shortcut or a novelty. Instead of focusing on tools, theory, or certification checklists, it walks through the full flow of business analysis—from starting a project through modeling, elicitation, requirements, planning, design, and validation—showing where AI helps, where it fails, and how to stay in control. The emphasis is on judgment, evaluation, and responsibility: you learn how to guide AI, correct its assumptions, connect its outputs into coherent analysis, and remain accountable for decisions. This makes the course directly applicable to day-to-day BA work in a way most AI training does not.

What you will earn

This course will contribute 14 continuing development units (CDUs) or professional development hours towards certifications from the IIBA®.

What you will learn

1. Use generative AI to get started on analysis work even when information is incomplete.

2. Write prompts that clearly describe your problem, context, constraints, and role as a business analyst.

3. Review AI-generated analysis artifacts and identify errors, gaps, assumptions, and bias.

4. Turn AI output into usable BA artifacts such as process models, data models, user stories, and backlogs.

5. Keep requirements, stories, designs, and tests consistent and traceable with AI support.

6. Use AI to prepare for stakeholder interviews and analyze interview results.

7. Organize and prioritize analysis work using AI while retaining control over decisions.

8. Recognize when AI is helping your analysis and when it is adding confusion or noise.

9. Apply ethical and responsible practices when using AI, including attention to data sensitivity and bias.

10. Create a practical plan for integrating generative AI into your own business analysis work.

What you will 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 Bootcamp course or have relevant experience working in a business analysis context.

This course is great for

• 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.

Topics Covered

1 - Understanding AI’s Role in 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.

Objectives

• 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.

Exercise

Reflect on your role as a business analyst, including your core activities, the artifacts you create, the inputs you receive, and the BABOK perspective that best fits your work. Then explore how AI could support you in that role. This involves writing a clear prompt that describes your work, inputs, outputs, and perspective, then asking how AI might help. Compare responses from multiple chatbots and use follow-up questions to refine and deepen the results.

2 - Using AI to Jumpstart a Project

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.

Objectives

• 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.

Exercise

Get AI to propose approaches to the coffee problem by experimenting with different prompting styles and using multiple chatbots. Engage in dialogue with the chatbots to correct assumptions, such as identifying out-of-scope features, and then compare, contrast, and merge their responses. Next, ask AI to generate content for the Business Analysis Canvas, then evaluate the generated content and refine it until you have a single canvas the whole team can agree on.

3 – Modeling the Current State (Process View)

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.

Objectives

• 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.

Exercise

Use AI to help describe how the system behaves today from a process perspective. Begin by asking AI to produce a usage narrative, user journey, or use case description for the coffee ordering scenario. Review the output to identify assumptions, missing steps, or unclear responsibilities, and refine it through follow-up prompts.

Next, convert the refined behavioral description into a simple process diagram. Compare the textual and visual views of the process, noting where the diagram clarifies flow, roles, alternate paths, or exceptions that were unclear in text alone. Adjust both artifacts until they tell a consistent story of current behavior.

4 – Modeling the Current State (Data View)

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: what entities exist, how they relate to one another, and how they are defined. 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. See how behavior and information models reinforce one another and reveal missing or undefined activities.

Objectives

• 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.

Exercise

Shift focus from behavior to information. Ask AI to identify key data entities involved in the current system and to draft a basic data model diagram. Review the model and generated definitions to check for missing concepts, vague terminology, or invented relationships.

Then use AI to create a CRUD matrix linking process activities from the previous module to the data entities. Analyze the matrix to identify gaps, such as data that is used but never created, or processes that change data without clear ownership. Refine the process or data models to resolve these inconsistencies.

5 – Getting to Know People

Business analysis is about people first. You’ll explore how AI can help identify stakeholders and visualize roles while staying alert to bias and oversimplification. You’ll practice using AI to draft stakeholder maps, RACI matrices, and personas—then critique them for realism, inclusivity, and fairness.

Objectives

• 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.

Exercise

Ask AI to identify stakeholders for the coffee system and to group them by role, influence, or interest. Use AI to draft artifacts such as a stakeholder map, RACI matrix, or personas. Review these outputs critically, looking for oversimplification, stereotypes, missing perspectives, or unrealistic responsibilities.

Revise the artifacts to better reflect real organizational dynamics, constraints, and diversity of viewpoints. Discuss where AI helped accelerate understanding and where human judgment was required to correct or contextualize the results.

6 – Interviewing and Elicitation

You’ve chatted with AI. Can you interview AI like subject-matter experts? Explore how prompting parallels interviewing: both rely on asking clear, purposeful questions. You’ll practice using AI as a stand-in for stakeholders to rehearse interviews, refine your questioning techniques, and generate interview plans or surveys.

Objectives

• 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.

Exercise

Use AI to prepare for elicitation by generating interview questions or survey prompts for different stakeholder roles. Then conduct simulated interviews by asking AI to respond as specific personas or subject-matter experts.

Analyze the simulated responses to practice summarizing, identifying themes, and spotting ambiguities or contradictions. Reflect on which elicitation tasks AI can support effectively (preparation, rehearsal, synthesis) and which require direct human interaction.

7 – Writing User Stories

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.

Objectives

• 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.

Exercise

Provide AI with inputs such as interview summaries, personas, and process models, and ask it to generate user stories. Review the stories for clarity, correctness, privacy concerns, and alignment with real user needs.

Classify and organize the stories into a simple story map or backlog structure. Refine wording, acceptance criteria, and traceability so that stories connect clearly back to stakeholders, processes, and business goals.

8 – Planning Development

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.

Objectives

• 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.

Exercise

Use the user stories to ask AI to propose a backlog ordering, MVP scope, or sprint plan. Examine the suggested sequencing and priorities, paying close attention to hidden assumptions, ignored dependencies, or unrealistic capacity expectations.

Adjust priorities using explicit criteria such as value, risk, or dependency. Produce a backlog or MVP definition that reflects informed human decisions rather than automated ranking.

9 – Designing the User Experience

For many customers, it’s IKIWISI—I’ll Know It When I See It. 

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.

Objectives

• 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.

Exercise

Ask AI to generate low-fidelity UI sketches or screen descriptions for selected user stories. Review these designs for usability, accessibility, and alignment with user goals and data constraints.

Iterate on the designs using feedback loops, connecting interface elements back to requirements, stories, and data models. Identify where AI-generated designs accelerate exploration and where human judgment is essential to assess fit and quality.

10 – Writing Tests

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. We’ve also used AI to evaluate. So it stands to reason that AI can be used to write test cases.

Objectives

• 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).

Exercise

Use AI to generate test cases and test data based on use cases, user stories, or UI designs. Express selected tests in a structured format such as Gherkin.

Evaluate the generated tests for coverage, clarity, redundancy, and missing scenarios. Refine test cases to ensure they are meaningful, verifiable, and suitable for either manual execution or automation.

11 – Validating, Prioritizing, and Coordinating

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.

Objectives

• 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.

Exercise

Introduce a change to one artifact, such as a requirement or user story, and ask AI to identify affected processes, data elements, tests, and designs. Use AI to help draft updates across artifacts.

Construct or update a traceability matrix linking requirements, stories, and tests. Review AI-assisted updates carefully to ensure consistency and correctness, and decide where manual intervention is required to maintain quality and control.

12 - Implementing AI‑Powered Business Analysis

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. 

Objectives

• 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.

Exercise

Reflect on your own business analysis practice and identify areas where AI could realistically add value. Use AI to brainstorm small, low-risk pilot experiments and success measures.

Define guardrails for ethical and responsible use, including data handling, bias awareness, and accountability. Produce a short personal or team roadmap outlining how you will integrate AI into your BA work and evaluate its impact over time.