INTRODUCTION AND LEVEL-SET

• Course expectations, outcomes and success metrics
• Overview of hands-on AI training methods for L&D teams
• Understanding generative AI mechanics and model capabilities
• Key AI technologies relevant to L&D
• Realities and myths of AI: strengths, weaknesses, and limitations

Activity: Brainstorming AI Workflow Discovery

THE L&D FOUNDATION 

• Common AI adoption obstacles and how to overcome them 

• Addressing employee concerns about AI integration and impact 

Activity: AI Readiness Assessment 

ENTERPRISE AI STRATEGY 

• The strategic role of L&D in AI transformation 

Activity: Mapping the AI Training Stakeholder Ecosystem 

• Enterprise-wide AI strategy and learning framework alignment 

AI FOR ROLES & DEPARTMENTS 

• Capturing compliance, policy and risk considerations in AI integration 

• Designing training programs based on role and departmental AI training needs 

Activity: Department-Specific AI Use Cases 

BASELINING ORGANIZATIONAL CAPABILITY 

• Building the business case for AI learning prioritization 

• AI-related functions, needs and gap analysis 

• Adapting the AI literacy framework to organizational strategy 

AI CAPABILITY AT THE INDIVIDUAL LEVEL 

• Goals and methods for AI competency baselining 

• Developing and administering effective AI skills assessments 

• Apply AI learning journeys and use case mapping to any functional role or department  

MEASURING AI TRAINING SUCCESS – KPIS & METRICS 

• Setting organizational and individual AI training goals 

• Managing AI-driven training metrics, objective and key results (OKRs) and return on investment (ROI) 

Activity: AI Adoption Metrics

DEVELOPING AI TRAINING PROGRAMS 

• Training Program Lifecycle Overview 

• Assessing learner group readiness for AI adoption training 

• Structuring AI literacy programs across roles 

• Evaluating capabilities to develop impactful AI training for employees 

Activity: AI Enablement Program Planning 

COACHING LEADERS, MANAGING CHANGE 

• Strategies for leading AI transformation and overcoming resistance 

• Change management for AI training programs and initiatives 

• How to talk to senior leadership about AI strategy 

• Designing learner engagement and AI adoption strategies 

Demo: Using AI with survey data for learner sentiment analysis 
Activity: AI Change Management Scenario Planning 

COMPLIANCE & REGULATORY TRAINING CONSIDERATIONS 

• Navigating AI regulations and compliance frameworks 

• AI policies impact on workforce and corporate training programs 

• Case studies in regulated industries 

Activity: AI Ethics & Governance Policy Socialization 

AI IN THE L&D WORKFLOW 

• AI-powered job aids in L&D departments 

• AI-assisted learning journey design and content creation 

Activity: Multi-Tiered AI Training Curriculum Design 

STAKEHOLDER ENABLEMENT & ENGAGEMENT 

• Stakeholder priority matrix 

• Conducting stakeholder surveys to uncover resistance and build psychological safety 

• Using AI to create effective communication plans 

Activity: Cross-Functional AI Community Building 

COURSE WRAP-UP & ACTION PLANNING 

• Key takeaways and reflections 

• Action planning to accelerate AI adoption and workforce readiness 

• Next steps for driving enterprise-wide AI enablement 

Founded in Training Industry’s Training Manager Competency Model™, this certificate is completed over three four-and-a-half-hour sessions in a virtual instructor-led (VILT) classroom setting. Throughout that time, you’ll work with peers to apply AI in various activities and discuss how what you’ve learned can be applied in your own organization.

If you are looking to integrate AI tools for L&D and validate your AI skills, consider the AI Essentials for Training Managers Certificate.