Leading AI Strategy & Organisational Adoption
Duration |
1-Day Executive Seminar | Classroom / Virtual
Adoption doesn’t fail because of the technology. It fails because of the people.
Overview
A strategic leadership course focused on responsible AI adoption across teams and workflows. Participants develop practical capabilities in governance, change management, human oversight and adoption planning to support effective, sustained use of AI within organisational environments.
Learning Objectives
Strengthen AI governance: Develop practical approaches to clarify governance, accountability, data security and ethical considerations.
Lead organisational change: Build the capability to address workforce anxiety and resistance associated with AI adoption.
Prioritise AI opportunities: Develop the ability to identify and prioritise valuable AI applications within operational workflows.
Plan sustainable adoption: Develop practical strategies for embedding AI effectively across teams and workflows while maintaining quality.
Learning Outcomes
By the end of the course, participants will be able to:
Identify AI opportunities: Identify and prioritise high-ROI AI opportunities within operational workflows.
Establish governance: Establish team guidelines for data security, ethics and human-in-the-loop accountability.
Lead behavioural change: Address workforce anxiety and lead change to overcome resistance to AI adoption.
Develop an adoption roadmap: Formulate an actionable plan for embedding AI into team workflows while maintaining quality.
Learning Challenges vs. Learning Impact
This table connects AI adoption challenges with strategic solutions, highlighting governance, change management, prioritisation and roadmaps for sustainable organisational implementation.
Learning Challenge | Learning Impact |
Unclear governance and accountability: Teams may lack clear responsibilities and guidance for responsible AI use. | Establish clear governance: Define governance and human-in-the-loop accountability for data security, ethics and responsible AI use. |
Workforce anxiety and resistance to change: Employees may experience uncertainty or resistance as AI is introduced into their work. | Lead structured change: Apply structured change management approaches to reduce resistance and support workforce adoption. |
Scattered AI experimentation: AI initiatives may develop as disconnected experiments without coordinated direction. | Prioritise valuable use cases: Identify and prioritise high-ROI AI opportunities to create a more focused approach to adoption. |
No clear adoption roadmap: Teams may lack a practical plan for scaling AI adoption across workflows. | Create a practical roadmap: Develop an actionable roadmap that guides AI adoption across team workflows and supports sustained implementation. |