Generative AI: Revolutionizing Business Analysis Techniques Course

Lesson 1 • Risks, Limitations, and Ethical Boundaries

Covers hallucination, bias, data privacy, and misuse risks inherent in generative AI. Grounds analysts in responsible use before applying tools to real business problems.

Lesson 2 • Core Generative AI Modalities

Surveys text, image, code, audio, and multimodal generation capabilities. Helps analysts match the right modality to specific business analysis tasks.

Lesson 3 • What Generative AI Actually Is

Defines generative AI, contrasts it with traditional ML, and maps its core mechanisms. Provides the conceptual baseline required for every technique introduced later.

Lesson 1 • Prompt Testing and Quality Control

Teaches systematic evaluation of prompt outputs for accuracy, completeness, and bias. Connects prompt quality to downstream BA deliverable reliability.

Lesson 2 • Building a Reusable Prompt Library

Guides analysts in cataloging, versioning, and sharing effective prompts across teams. Transforms individual prompt skills into organizational assets.

Lesson 3 • Prompting Strategies and Patterns

Introduces zero-shot, few-shot, chain-of-thought, and other proven prompting patterns. Analysts learn when each pattern fits different BA problem types.

Lesson 1 • Generating Interview Questions with AI

Uses AI to draft targeted stakeholder interview guides aligned to project scope. Reduces preparation time while improving question depth and coverage.

Lesson 2 • Extracting Requirements from Documents

Applies AI to parse contracts, policies, and legacy documents for embedded requirements. Teaches analysts to structure and validate extracted content.

Lesson 3 • AI-Assisted Workshop Facilitation

Integrates AI tools into live and async workshops to capture, organize, and synthesize stakeholder input in real time. Improves workshop output quality and speed.

Lesson 1 • AI-Driven Process Discovery

Applies AI to extract process flows from interviews, logs, and documents without manual mapping. Builds the process baseline needed for all subsequent analysis.

Lesson 2 • AI-Generated Process Redesign Options

Prompts AI to generate multiple to-be process scenarios and evaluate trade-offs. Gives analysts a structured method for presenting redesign choices to stakeholders.

Lesson 3 • Generating and Refining Process Models

Uses AI to draft BPMN diagrams, swimlane charts, and flowcharts from text descriptions. Accelerates model creation while maintaining notation accuracy.

Lesson 1 • Validating AI-Generated Insights

Establishes verification methods to confirm AI-produced insights against source data and domain knowledge. Prevents flawed insights from reaching decision makers.

Lesson 2 • Exploratory Analysis Using Generative AI

Uses AI to generate descriptive statistics, trend summaries, and anomaly flags from structured data. Teaches analysts to direct AI exploration efficiently.

Lesson 3 • Translating Data into Business Narratives

Applies AI to convert analytical findings into clear, audience-specific business narratives. Bridges the gap between data outputs and stakeholder understanding.

Lesson 1 • Drafting Business Requirements Documents

Uses AI to generate structured BRD sections from elicited requirements and process models. Connects documentation directly to earlier elicitation and analysis outputs.

Lesson 2 • Automating Status Reports and Meeting Notes

Leverages AI to transform raw notes and data into formatted status reports and meeting minutes. Frees analyst time for higher-value analytical work.

Lesson 3 • Writing User Stories and Acceptance Criteria

Applies AI to convert requirements into well-formed user stories with testable acceptance criteria. Ensures agile teams receive clear, actionable backlog items.

Lesson 1 • Scenario Planning and What-If Analysis

Applies AI to generate plausible future scenarios and stress-test assumptions across business variables. Equips analysts to present risk-aware recommendations.

Lesson 2 • Presenting Recommendations to Stakeholders

Teaches analysts to use AI to tailor recommendation framing, anticipate objections, and prepare supporting evidence. Maximizes the persuasive impact of analytical work.

Lesson 3 • Structuring Decision Problems with AI

Uses AI to decompose complex decisions into criteria, options, and trade-offs. Establishes a rigorous decision architecture before modeling begins.

Lesson 1 • Governance, Standards, and Quality Assurance

Establishes policies for AI tool use, output review, and quality standards within BA teams. Ensures consistent, trustworthy AI-assisted deliverables across projects.

Lesson 2 • Building Organizational AI Capability

Guides analysts in training peers, creating learning resources, and fostering a culture of continuous AI experimentation. Scales individual expertise into team-wide capability.

Lesson 3 • Measuring and Communicating AI-Driven Value

Defines metrics for tracking productivity, quality, and business impact of AI-augmented BA work. Enables analysts to demonstrate and sustain executive support for AI initiatives.

Lesson 4 • Designing AI-Augmented BA Workflows

Maps generative AI touchpoints into existing BA processes without disrupting proven practices. Creates hybrid human-AI workflows that maximize analyst effectiveness.