Generative AI for Business Analysts
Generative Artificial Intelligence (Gen-AI)
COURSE OVERVIEW
This intensive program focuses on the BA lifecycle: from discovery and elicitation to solution design and validation. Business Analysts will learn how to use Generative AI as a "force multiplier" to draft high-quality user stories, model business processes (BPMN), and perform advanced data storytelling. By the end of the course, participants will be equipped to lead AI initiatives and ensure that AI-driven solutions are both strategically aligned and ethically governed.
COURSE OBJECTIVES
By the end of this course, participants will be able to:
- Accelerate Requirements Elicitation: Use Gemini to design interview scripts and synthesize stakeholder notes into structured requirements.
- Master AI-Assisted Modeling: Generate process flows (BPMN), data models, and CRUD matrices using AI reasoning.
- Draft Precision User Stories: Produce consistent epics and user stories with clear acceptance criteria and edge cases.
- Perform AI-Augmented Analytics: Use Gemini and BigQuery to uncover trends and anomalies in large datasets without writing complex code.
- Govern AI Requirements: Evaluate AI solutions for accuracy, bias, and alignment with organizational security policies.
- Build a Business Analysis Canvas: Create a comprehensive project roadmap using AI-generated strategic insights.
Duration: 3 Days / 24 Hours
Delivery Method: Classroom-based, Virtual Instructor Led Training
COURSE OUTLINE
Day 1: The AI-Augmented BA & Discovery
Focus: Enhancing the initial phases of the project lifecycle.
- Generative AI in the BA Lifecycle: Identifying where AI adds value (Automation vs. Reasoning).
- Elicitation & Stakeholder Analysis:
- Using Gemini to categorize stakeholders and build RACI matrices.
- Drafting interview scripts and workshop agendas.
- Problem Framing & Discovery:
- Summarizing stakeholder "pain points" into a clear Problem Statement.
- Generating a preliminary Business Analysis Canvas.
- Hands-on Lab: Use Gemini to analyze a raw transcript from a stakeholder interview and extract a structured list of business needs and constraints.
Day 2: Modeling, User Stories & Solution Design
Focus: Turning raw data into structured BA artifacts.
- Process & Data Modeling:
- Using AI to draft BPMN 2.0 process flows and identify "Happy Paths" vs. "Exception Paths."
- Generating entity-relationship diagrams (ERD) and data definitions.
- Writing High-Quality User Stories:
- The INVEST principle in the age of AI.
- Generating Acceptance Criteria (AC) and Gherkin-style test scenarios.
- Prototyping & UX Logic:
- Using AI to describe functional narratives and screen-flow logic.
- Linking UI elements to specific business requirements.
- Hands-on Lab: Refine a vague business objective into 5-10 detailed User Stories with full Acceptance Criteria and a corresponding process map.
Day 3: Data Analytics, Ethics, and Governance
Focus: Smarter decision-making and responsible oversight.
- AI-Powered Data Analysis:
- Using Gemini to query spreadsheets and databases for trends and anomalies.
- Generating "Data Narratives"—turning raw metrics into executive summaries.
- Validation & Quality Control:
- Using AI to check for ambiguity and completeness in requirement documents.
- Building a Traceability Matrix with AI assistance.
- Ethics & Risk Management:
- Detecting bias in AI-generated personas and requirements.
- Understanding the EU AI Act and NIST Framework from a BA perspective.
- The Future BA Roadmap: Strategic change management and preparing the organization for AI adoption.
- Final Capstone: Participants present a "Project Initiation Document" for an AI-driven solution, including requirements, risks, and a governance plan.
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