Objective: Introduce participants to the AIMM framework, its components, and its significance in organizational AI transformation.

• Overview of AIMM Framework
• Evolution of AIMM and its significance
• Key components and maturity levels

Outcome: Participants will understand the structure of AIMM and its relevance in assessing AI maturity within organizations.

Self-Assessment Test: Quiz assessing participants’ understanding of AIMM components and their purpose.

Objective: Equip participants with the knowledge and skills necessary to conduct AIMM assessments effectively.

• Understanding assessment criteria
• Data collection methodologies
• Conducting AIMM assessments

Outcome: Participants will be able to collect relevant data, apply assessment criteria, and conduct AIMM assessments within their organizations.

Self-Assessment Test: Scenario-based exercises where participants demonstrate their ability to apply AIMM assessment criteria.

Objective: Enable participants to analyse AIMM assessment findings and interpret results accurately.

• Analysing assessment findings
• Identifying strengths and weaknesses
• Prioritizing areas for improvement

Outcome: Participants will be able to identify strengths, weaknesses, and areas for improvement based on AIMM assessment results.

Self-Assessment Test: Case study analysis where participants interpret AIMM assessment results and propose actionable recommendations.

Objective: Guide participants in developing strategic plans based on AIMM assessment findings.

• Crafting strategic plans based on AIMM results
• Setting realistic goals and timelines
• Allocating resources effectively

Outcome: Participants will create AIMM-driven roadmaps outlining goals, timelines, and resource allocations for advancing AI maturity within their organizations.

Self-Assessment Test: Participants will develop a sample AIMM roadmap based on the provided AIMM assessment results and organizational objectives.

Objective: Explore the role of leadership and organizational culture in driving AI transformation initiatives.

• Role of leadership in driving AI initiatives
• Creating a culture of AI innovation
• Overcoming resistance to change

Outcome: Participants will understand the importance of leadership support and cultural alignment in successful AI adoption.

Self-Assessment Test: Reflective exercises where participants assess their organization’s current leadership support and culture related to AI initiatives.

Objective: Provide participants with strategies for translating AIMM insights into actionable initiatives.

• Translating AIMM insights into actionable steps
• Selecting appropriate AI technologies and tools
• Monitoring progress and adjusting strategies

Outcome: Participants will be able to select appropriate AI technologies, tools, and methodologies to implement AIMM-driven initiatives effectively.

Self-Assessment Test: Participants will develop an implementation plan for one of the initiatives identified in their AIMM roadmap.

Objective: Examine real-world case studies and best practices of AIMM implementation.

• Real-world examples of AIMM implementation
• Success stories and lessons learned
• Best practices for maximizing AI maturity

Outcome: Participants will gain insights from successful AIMM implementations and learn best practices for maximizing AI maturity.

Self-Assessment Test: Multiple-choice questions assessing participants’ understanding of key takeaways from presented case studies and best practices.

Objective: Explore emerging trends and anticipate challenges in AI maturity advancement.

• Emerging trends in AI and its impact on AIMM
• Anticipating challenges in AI maturity advancement
• Strategies for staying ahead in the AI landscape

Outcome: Participants will be equipped to anticipate future trends and challenges in AI adoption and develop strategies to address them.

Self-Assessment Test: Essay questions prompting participants to discuss potential future trends and challenges in AI maturity and propose mitigation strategies.