This course equips participants with practical skills to apply artificial intelligence to business analysis activities, including problem definition, stakeholder analysis, requirements development, process improvement, and decision support. Participants learn to use generative AI to organize business information, develop analysis artifacts, and evaluate potential solutions while maintaining human judgment and accountability.

The course covers prompt development, validation of AI-generated outputs, responsible handling of business information, and identification of suitable AI use cases. It concludes with a capstone that brings these skills together in a business improvement scenario.

 

Duration 3 Days – 21 hrs.

 

Objectives

• Explain how AI can support business analysis and recognize its limitations.
• Define business problems, desired outcomes, and measurable success criteria.
• Develop structured prompts for common business analysis tasks.
• Use AI to support stakeholder analysis and requirements elicitation.
• Produce and refine business requirements, user stories, and acceptance criteria.
• Analyze current processes and identify improvement opportunities.
• Interpret business data and validate AI-assisted findings.
• Compare solution options based on value, feasibility, dependencies, and risk.
• Apply privacy, confidentiality, and human review controls to AI-assisted work.
• Develop a coherent business analysis package for an improvement initiative.

 

Target Audience 

• Business analysts and business systems analysts.
• Process analysts and business improvement professionals.
• Product owners and product managers.
• Project managers and project coordinators.
• Functional consultants and subject matter experts.
• Operations managers and team leaders.
• Professionals involved in requirements definition, digital transformation, or AI adoption.

 

Prerequisites 

• Basic understanding of business processes and organizational operations.
• Familiarity with common office productivity tools.
• Ability to interpret basic business documents, tables, and reports.
• Basic awareness of requirements gathering or project work is helpful.
• Access to an organization-approved generative AI tool for practical activities.
• No programming or machine learning experience is required.

 

 

Course Outline 

Day 1: Business Analysis Foundations and AI-Assisted Discovery

Module 1: Business Analysis and the Role of AI

• Business analysis purpose, responsibilities, and core activities.
• Overview of generative AI and its business applications.
• AI support across the business analysis lifecycle.
• Selecting tasks suitable for AI assistance.
• Limitations, hallucinations, bias, and the need for human judgment.

 Module 2: Defining Business Problems and Outcomes

• Identifying business needs, challenges, and opportunities.
• Distinguishing symptoms from underlying causes.
• Developing problem statements and business objectives.
• Establishing scope, constraints, assumptions, and success criteria.
• Using AI to explore and refine problem definitions.

 Module 3: Prompt Development for Business Analysis

• Structuring prompts with context, task, constraints, and output format.
• Providing reference material and examples.
• Breaking complex analysis into manageable tasks.
• Refining prompts through follow-up questions.
• Checking outputs for accuracy, completeness, and unsupported claims.

Module 4: Stakeholder Analysis and Requirements Elicitation

• Identifying stakeholders, interests, influence, and information needs.
• Preparing stakeholder maps and engagement considerations.
• Developing interview questions and workshop agendas.
• Organizing supplied interview notes and business documents.
• Extracting candidate requirements, assumptions, and open questions.
• Protecting confidential information when using AI.

  

Day 2: Requirements, Process Analysis, and Business Insights

Module 5: AI-Assisted Requirements Development

• Business, stakeholder, solution, and transition requirements.
• Functional and nonfunctional requirements.
• Writing clear, consistent, and testable requirements.
• Developing user stories and acceptance criteria.
• Identifying ambiguity, duplication, gaps, and conflicting requirements.
• Establishing requirements traceability and managing changes.

 Module 6: Business Process Analysis and Improvement

• Defining process boundaries, actors, inputs, and outputs.
• Documenting current-state workflows.
• Identifying bottlenecks, handoff issues, rework, and delays.
• Using AI to explore root causes and improvement options.
• Developing future-state processes.
• Distinguishing opportunities for process redesign, automation, and AI support.

 Module 7: AI-Assisted Business Data Analysis

• Framing business questions and identifying relevant data.
• Understanding data quality, completeness, and context.
• Using AI to support analysis of structured data and text feedback.
• Identifying patterns, exceptions, and possible contributing factors.
• Validating calculations and distinguishing evidence from inference.
• Presenting findings, limitations, and business implications.

 Module 8: Requirements Prioritization and Validation

• Prioritizing requirements by value, urgency, risk, and dependency.
• Applying common prioritization techniques.
• Reviewing requirements against business objectives.
• Identifying missing business rules and exception scenarios.
• Confirming requirements with stakeholders.
• Recording decisions and maintaining traceability.

  

Day 3: Solution Evaluation, Responsible AI, and Capstone

Module 9: Solution Options and Business Case Development

• Comparing process, technology, automation, and AI solution options.
• Evaluating feasibility and organizational readiness.
• Identifying benefits, costs, dependencies, and implementation risks.
• Developing a high-level business case.
• Using AI to draft option comparisons and executive summaries.
• Defining measures for benefits realization.

 Module 10: Requirements for AI-Enabled Solutions

• Defining the purpose and boundaries of an AI use case.
• Identifying data, integration, and user interaction requirements.
• Establishing quality expectations and acceptable limitations.
• Specifying human review, escalation, and fallback processes.
• Considering privacy, security, fairness, and transparency.
• Defining monitoring and ongoing improvement requirements.

 Module 11: Responsible AI and Business Analysis Governance

• Following organizational AI policies and approved usage boundaries.
• Handling sensitive, confidential, and personal information.
• Maintaining source references and decision records.
• Reviewing AI-generated artifacts before stakeholder use.
• Assigning ownership and accountability.
• Establishing practical controls for AI-assisted business analysis.

 Module 12: Capstone — AI-Assisted Business Improvement Proposal

• Select a business scenario involving a process or service challenge.
• Define the business problem, objectives, scope, and stakeholders.
• Develop a current-state process summary and supporting findings.
• Produce prioritized requirements, user stories, and acceptance criteria.
• Propose a future-state process and evaluate solution options.
• Identify an appropriate AI use case and required human controls.
• Compile a business analysis package with a recommendation, risks, and success measures.