Programme outline

Learning objectives and structure

By the end of the course, participants will:

• Refine and Sharpen Problem Statements:
  • Revisit the problem and opportunity statements developed in Module 2 and enhance them to accurately capture business challenges and productivity constraints.
  • Utilise analytical frameworks to delineate clear, measurable objectives that are addressable through AI interventions.
• Evaluate Artificial Intelligence (AI) Tool Relevance:
  • Critically assess the suitability of various AI tools and solutions in addressing the refined business challenges.
  • Evaluate how selected AI interventions can improve business productivity and deliver measurable ROI, considering both technical feasibility and commercial impact.
• Identify Change Management Requirements:
  • Determine the organisational change management needs essential for the successful integration of AI, including stakeholder engagement, process re-engineering, and training necessities.
  • Integrate change management strategies into the evolving business case to ensure a smooth transition.
• Establish the Right Teams:
  • Identify and assemble appropriate cross-functional teams, ensuring representation from key areas such as Information Technology (IT), operations, finance, Human Resource (HR), and leadership.
  • Define clear roles and responsibilities to enable effective collaboration on the AI business case.
• Prepare for AI Implementation Planning:
  • Develop a draft business case that integrates the refined problem statement, ROI assessment, and change management strategies.
  • Create a preliminary skills map to identify and address capability gaps, setting the foundation for detailed implementation planning and use case development.
• Develop Comprehensive AI Use Cases:
  • Build on the refined problem statements and evaluated AI tools from Module 3 to create detailed AI use cases that address identified business challenges.
  • Align AI use cases with targeted productivity improvements and operational efficiency gains.
• Finalise the Implementation Plan:
  • Formulate a detailed implementation roadmap that outlines the stages of AI deployment, including timelines, budgets, and change management strategies.
  • Incorporate a clearly considered Return On Investment (ROI) analysis, ensuring that each proposed AI solution is justified by its commercial and operational impact.
• Competency Mapping for Impacted Teams:
  • Develop a comprehensive competency map for the teams identified in Module 3, outlining the current skills gaps and required training initiatives.
  • Define strategies for staff capability development to ensure a smooth transition during and after AI implementation.
• Integrate Change Management and Stakeholder Engagement:
  • Finalise change management strategies that were identified in Module 3, ensuring that stakeholder engagement, process re-engineering, and training are fully addressed within the implementation plan.
• Prepare for Real-World Deployment:
  • Produce a complete, actionable AI implementation plan that includes a robust business case, ROI analysis, and a detailed skills development roadmap for all affected teams.

Day 1

• Refine and Sharpen Problem Statements
• Evaluate AI Tool Relevance
• Identify Change Management Requirements
• Establish the Right Teams
• Prepare for AI Implementation Planning
• Develop Comprehensive AI Use Cases
• Finalise the Implementation Plan
• Competency Mapping for Impacted Teams
• Integrate Change Management and Stakeholder Engagement
• Prepare for Real World Deployment
• Summary and Assessment

Assessment

Class participation and in-class project presentation.