Short Course

Artificial Intelligence

AI Project Roadmap And Execution

Overview

This course introduces participants to the end-to-end process of planning, implementing, and sustaining AI projects within organizations. Designed for managers, analysts, and professionals, it emphasizes practical frameworks, governance, and adoption strategies. Learners will explore the journey from scoping AI initiatives to integrating them into organizational culture. The course provides a balanced mix of conceptual knowledge and applied practices, covering project scoping, ecosystem considerations, team management, risk mitigation, and change management. Real-world examples and tools are used to reinforce learning, ensuring participants gain actionable insights to execute AI projects effectively and responsibly across diverse business contexts.

Who Should Attend

This course is intended for

  • Business leaders, managers, and decision-makers overseeing AI or digital transformation initiatives
  • Project managers and analysts involved in AI strategy, planning, or implementation
  • Technical leads, data professionals, and AI champions seeking structured execution practices
  • Policy, compliance, and change management officers supporting organizational AI adoption

Pre-requisites

  • No pre-requisite required
  • Participants are required to bring their own laptop PC or MacBook for the practical hands-on portions of the workshop
Enroll Now

1 Day

8 Hours

Level

Beginner

What You Will Learn

Define AI Project Scope and Business Alignment

Understand how to scope AI projects effectively by identifying business problems, aligning AI capabilities with organizational objectives, and defining measurable success criteria that deliver tangible business value.

Plan and Manage AI Project Lifecycles

Apply structured project management frameworks, including project planning, milestones, timelines, and deliverables, to ensure AI initiatives are executed efficiently and achieve their intended outcomes.

Identify and Mitigate AI Project Risks

Evaluate common risks associated with AI projects, including data quality, ethical considerations, compliance, technical limitations, and stakeholder expectations, while implementing appropriate mitigation strategies.

Build and Lead High-Performing AI Teams

Develop the skills to structure multidisciplinary AI teams, allocate resources effectively, define roles and responsibilities, and provide leadership that supports successful project delivery.

Manage Stakeholders and Drive Organizational Change

Apply change management principles and stakeholder engagement strategies to encourage collaboration, address resistance, and promote the successful adoption of AI solutions across the organization.

Establish Sustainable AI Governance and Continuous Improvement

Develop governance practices, performance monitoring approaches, and continuous improvement strategies to ensure AI initiatives remain scalable, sustainable, and aligned with evolving business goals and organizational culture.

Course Outline

Day 1
  • Introduction to AI Project Scoping
    • Foundations of AI project planning
    • Challenges and opportunities in adoption
    • Frameworks for roadmap development
    • Skills and resources required for success
  • Defining and Scoping AI Projects
    • Identifying business problems suitable for AI
    • Setting goals and priorities for initiatives
    • Scoping techniques to evaluate feasibility
    • Tools and templates to support scoping
  • Ecosystem Considerations
    • AI technology landscape and opportunities
    • Internal environment: infrastructure, governance, and workforce readiness
    • External environment: markets, regulations, and trust factors
    • Evaluating ecosystem fit for sustainable adoption
  • AI Enterprise Tools
    • AI agents / AI workflows
    • Cloud based tools
    • Transfer learning / AI based frameworks
    • Office productivity tools
  • AI Project Considerations
    • CRISP-DM model deployment
    • Success factors
    • Risks and causes of failure
    • AI business model canvas
  • AI Framework and Implementation
    • Project scoping and requirements
    • Technology research and evaluation
    • Securing stakeholder buy-in
    • Education and training staff

Trainers Profile

 
Peter

Christopher Magendran

Christopher Magendran is currently a tech venture builder at Jukuru and he runs a tech consultancy firm called TSiD as well developing and consulting for local SMEs in Singapore. He currents leads a tech team in building enterprise software solutions for local SMEs and also designing and developing agentic AI workflows.