Introduction

AI can help initiative teams prioritize faster by organizing ideas, scoring initiatives against criteria, and highlighting trade-offs and capacity constraints. This practical program equips initiative advisors with simple AI-supported methods to build prioritization models, produce decision-ready shortlists, and maintain transparency and governance—while keeping human judgment and validation at the center.

Course Objectives

By the end of this course, participants will be able to:

• Use AI to structure initiative lists and standardize descriptions
• Build simple scoring criteria and weighting models
• Use AI to compare options and highlight trade-offs
• Create decision-ready prioritization packs and shortlists
• Apply safe-use rules and quality checks

Target Audience

This course is designed for:

• Initiatives advisors and coordinators
• Strategy execution and PMO support teams
• Performance and reporting specialists
• Program/project teams shaping portfolios
• Teams supporting executive decision forums

Course Outline

Day 1: AI Basics for Prioritization

• Where AI helps in prioritization
• AI limits and verification needs
• Prompting basics for structured outputs
• Safe use and confidentiality
• Activity: Build a prioritization prompt list

Day 2: Structuring Initiatives for Scoring

• Standard initiative one-pager fields
• Clean problem statements and outcomes
• Defining assumptions and scope
• Grouping initiatives by theme
• Workshop: Create a standardized initiative list

Day 3: Scoring Models and Weighting

• Choosing criteria (value, effort, risk)
• Setting weights and thresholds
• Using AI to suggest scoring inputs (with checks)
• Ranking and sensitivity basics
• Activity: Build a simple scoring sheet

Day 4: Trade-offs and Decision Packs

• Portfolio balance (quick view)
• Capacity and dependency notes
• AI-assisted decision briefs and summaries
• Handling conflicts and edge cases
• Case study: Prioritization meeting simulation

Day 5: Governance and Adoption

• Approval workflow and documentation
• Quality checks and audit trail
• Updating scores and re-prioritizing
• Simple KPIs for prioritization process
• Final project: AI prioritization playbook