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 Duration 14 hours

Course Outline

Foundations and Practical Uses of Gen AI

Overview of Generative AI

  • Defining Gen AI and understanding its underlying mechanics
  • Language models and their inherent restrictions
  • The critical role of humans in AI-supported work

Gen AI for the Business Analyst

  • Assistance in problem analysis
  • Structuring and organizing information
  • Drafting various documents and reports

Practical Prompting Techniques

  • Strategies for crafting effective prompts
  • Defining context, objectives, and constraints
  • Refining outputs through iterative processes

Enhancing Business Analysis

  • Defining and articulating business problems
  • Formulating testable hypotheses
  • Conducting scenario analysis

AI-Assisted Analytical Documentation

  • Documenting business requirements
  • Describing business processes
  • Summarizing workshop notes and proceedings

Incorporating AI into Daily Workflows

Gen AI as a Cognitive Assistant

  • Critical evaluation of AI-generated outputs
  • Verifying and validating content accuracy
  • Preventing indiscriminate or thoughtless automation

AI in Stakeholder Communication

  • Preparing communications and progress updates
  • Simplifying complex content for clarity
  • Customizing messages for diverse audiences

Risks and Ethical Responsibility

  • Data privacy and confidentiality safeguards
  • Accountability for AI-generated content
  • Addressing ethical considerations

Developing a Custom BA Workflow with AI

  • Exploring practical use-case scenarios
  • Integrating AI with existing productivity tools
  • Adopting team best practices

Requirements

  • Professional background in roles associated with data analysis, reporting, or business process support.
  • Technical proficiency: Competence in MS Excel, including lookup functions, pivot tables, and basic charting skills.
  • Domain awareness: A fundamental understanding of Key Performance Indicators (KPIs) relevant to your business sector, along with familiarity with the organizational data lifecycle.

Target Audience

  • Business and Systems Analysts.
  • Product Owners and Product Managers.
  • Business Consultants.
  • Requirements Engineering Specialists.

Testimonials (2)

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