Course Format and Delivery

Delivery Method: LiveOnline 
Schedule: 3 sessions of 4.5 hours
Cost: $1,250 USD

All sessions are delivered live by an expert instructor in a fully interactive online environment.

*20% off for group bookings when booking 3 or more attendees from the same organization on the same course dates in the same transaction.

About this course

Understanding and integrating generative AI is no longer an option — it's a necessity. This workshop will equip you with the practical skills, actionable strategies, and understanding necessary to serve as a primary enablement champion for successful adoption of AI in your organization. You'll return to your role not only with theoretical knowledge but with hands-on, actionable plans that can be immediately implemented to drive real change and results.

What you will learn

• AI tool user skills: Get hands-on with the latest AI technologies and learn how to select the right tools for your organization's unique needs, including enterprise licensing options, managed service provider options, and private LLM configurations.
• Effective AI policy frameworks: Develop robust governance and policy frameworks to ensure ethical, compliant, and effective AI usage. We will provide you with acceptable use templates and policy outlines you can use to craft practical policies for your own organization.
• Change management and education strategies: Learn the specific change management considerations specifically for AI implementation. Leave with learning plans and enablement calendars to ensure smooth transitions and widespread adoption.
• AI implementation roadmaps: Craft detailed AI adoption roadmaps that outline step-by-step, from pilot to full-scale rollout, ensuring every phase of deployment is thoughtfully planned and executed. You'll start with needs assessments and stakeholder engagement strategies, and finish with a well-defined rollout plan you can use to guide the adoption - no matter where you are in your current journey.
• Performance monitoring and iteration: Learn how to set up effective monitoring systems using key performance indicators tailored to AI projects, enabling ongoing improvement and alignment with business goals.

Join us to become your organization's driver of AI adoption — strategically, ethically, and effectively. Be the catalyst for change and a champion of innovation with the framework you learn in this workshop.

This course is great for

• Chief Information Officer (CIO)
• Chief Technology Officer (CTO)
• Chief Learning Officer (CLO)
• Human Resources Director
• Talent Development Manager
• Organizational Development Specialist
• Corporate Training Manager
• Innovation Manager
• Transformation Leader
• Change Management Director
• Digital Strategy Manager
• IT Project Manager
• AI Implementation Specialist
• Head of Employee Experience
• Learning and Development Consultant
  
  

Topics Covered

Section 1: AI Foundations and Level-Setting.

1. Understanding AI in the enterprise context

• Clarify your understanding of different AI types, including machine learning, deep learning, natural language processing, and generative AI
• Explore the transformative nature of AI adoption and its parallels to internet and mobile technology revolutions
• What generative AI is good at (and not good at)

• Understanding AI capabilities and limitations
  • Text generation and language tasks
  • Pattern recognition and analysis
  • Current challenges with mathematical reasoning and hallucinations
• Practical applications across departments

Workshop breakout - Due Diligence Exercise: Participants will identify what they need to know first before implementing AI in their organizations, considering their specific contexts and constraints.

Section 2: Building the Learning & Development Foundation.

1. Understanding obstacles to AI adoption

• Identifying common barriers: Leadership hesitation, unclear strategy, policy uncertainty, employee skills gaps, and trust issues
• Developing strategies to address resistance: Communication approaches, skill development plans, and change management tactics

2. Change management fundamentals for AI

• Addressing job displacement concerns
• Building confidence through training and experimentation
• Creating psychological safety around AI adoption
• Aligning with labor policies and collective bargaining agreements

Workshop breakout - Barrier Analysis: Teams will map their organizational obstacles and develop targeted strategies to overcome them using proven change management frameworks.

Section 3: Enterprise AI Strategy Framework

1. Organizing the strategic dimensions

• Four key dimensions of AI strategy:
  • Overall goals and outcomes for AI
  • AI use cases identification
  • Technical considerations
  • Human factors and AI literacy
• Creating alignment across dimensions

2. Understanding AI maturity progression

• Individual contributor use cases with public tools
• Team training and enablement
• Integration with technical environment
• Strategic, compliant AI execution at scale

Workshop breakout - Strategy Alignment: Participants will map their organization's current state across the four dimensions and identify priority areas for development.

Section 4: Role-Based AI Enablement

1. Conducting role-based due diligence

• Understanding role requirements:
  • Primary responsibilities and decision points
  • Repetitive tasks and workflows
  • Current tools and collaborations

• Identifying AI opportunities:
  • Speed and efficiency improvements
  • Accuracy and consistency enhancements
  • Available tools and required literacy level

2. Designing role-specific enablement

• Compliance and risk considerations by role
• Training design based on role needs
• Success metrics for role-based adoption

Workshop breakout - Role Enablement Canvas: Using provided templates, participants will develop a comprehensive enablement plan for a specific role, including AI opportunities, training needs, and success metrics.

Section 5: Organizational Capability Assessment

1. Baselining current AI capabilities

• Assessment across key areas:
  • Defined objectives and goals
  • Current tool usage and adoption
  • Governance and policy status
  • Existing challenges and barriers
    
    
• Creating organizational heat maps

2. Building capability rubrics

• Department-by-department assessment
• Identifying priority needs and gaps
• Resource allocation planning

Workshop breakout - Capability Mapping: Teams will assess their organization's AI readiness across departments and functions, creating a visual heat map of capabilities and needs.

Section 6: Individual and Team AI Skills Development

1. Identifying tactical AI opportunities

• Automating documentation and reporting
• Data analysis and insights generation
• Communication and content creation
• Workflow optimization

2. Building AI literacy programs

• Skill domain development:
• Creating proficiency rubrics

Workshop breakout - Use Case Brainstorming: Participants will identify specific workflows in their departments where AI could add immediate value, assessing risks and implementation requirements.

Section 7: Designing Comprehensive Training Programs

1. Training program lifecycle

• Five-phase approach:
  1. Clarify objectives and organizational readiness
  2. Engage stakeholders and secure alignment
  3. Design and pilot the program
  4. Roll out, reinforce, and iterate
  5. Evaluate, measure, and scale

2. Program design best practices

• Choosing high-impact, low-risk use cases
• Building minimum viable training
• Creating reinforcement mechanisms
• Scaling successful pilots

Workshop breakout - Program Timeline Development: Using templates, participants will create a 12-week implementation timeline for their AI training initiative.

Section 8: Leadership Coaching and Change Management

1. AI leadership considerations

• What AI can't do: Lead change, develop people, think strategically, or guide transformation
• Human leadership strengths: Building trust, guiding through uncertainty, developing vision, coaching teams

2. Supporting leaders through AI adoption

• Communicating the "why" behind AI
• Acknowledging and addressing resistance
• Creating a culture of continuous learning
• AI discussions into regular operations

3. Data governance and value alignment

• Understanding the data lifecycle
• Connecting AI initiatives to business value
• ROI planning and measurement

Section 9: Compliance and Governance Frameworks

1. Understanding AI governance requirements

• Tool categories and risk levels:
  • Individual accounts on public services
  • Enterprise subscriptions
  • API integrations
  • Private LLMs and custom development
    
    
• Policy development essentials

2. Creating acceptable use policies

• Data privacy and security considerations
• Intellectual property protection
• Ethical use guidelines
• Compliance with regulations

Workshop breakout - Policy Development: Using provided templates, participants will draft key elements of an AI acceptable use policy addressing common implementation barriers.

Section 10: AI Integration in Organizational Workflows

1. Identifying workflow improvement opportunities

• Help desk and IT support automation
• Project management assistance
• Document processing and compliance
• Customer service enhancement

2. Measuring workflow impact

• Phased measurement approach:
  
  
  • Initial impact (first month)
  • Mid-term impact (six months)
  • Long-term impact (one year)
    
    
• ROI calculation methods

Section 11: Stakeholder Engagement and Communication

1. Strategic stakeholder management

• Using the stakeholder priority matrix:
  
  
  • Champions (high interest, high influence)
  • Skeptics (high influence, low interest)
  • Enthusiasts (high interest, low influence)
  • Passive stakeholders (low both)

2. AI-assisted engagement planning

• Survey design and feedback collection
• Communication plan development
• Empathetic messaging strategies

Workshop breakout - Stakeholder Engagement Toolkit: Participants will create a comprehensive engagement plan including stakeholder mapping, survey questions, and communication strategies using AI assistance.

Section 12: Metrics, Measurement, and ROI

1. Defining success metrics

• Cost savings and efficiency gains
• Response time improvements
• Error reduction and accuracy
• Satisfaction and service delivery
• New value stream creation

2. Building measurement frameworks

• Setting appropriate KPIs by initiative type
• Creating monitoring dashboards
• Iterative improvement processes

Workshop breakout - KPI Design Session: Teams will develop specific, measurable KPIs for their AI initiatives, including data collection methods and success thresholds.

Section 13: Portfolio Approach to AI Initiatives

1. Understanding portfolio management for AI

• Three levels of scale:
  • Team/tactical initiatives
  • Epic or initiative level
  • Strategic priorities

• Balancing the portfolio

2. Prioritization frameworks

• Aligning with organizational goals
• Resource allocation strategies
• Risk and return assessment
• Dependency management

Workshop breakout - Portfolio Planning: Participants will map their AI initiatives across the portfolio framework, identifying priorities and resource requirements.

10 Immediate Benefits of This Workshop:

By the end of this class, you will have:

1. Comprehensive literacy of today's AI capabilities: Gain a deep understanding of various AI technologies and their applications within a corporate setting, equipping you with the knowledge to make informed decisions.
2. Hands-on experience with AI tools: Engage directly with the latest AI tools during the workshop, providing you with practical experience that can be directly applied to your own projects.
3. Ready-to-implement AI strategies: Develop strategic AI implementation plans during the course, enabling you to start AI integration immediately upon return to your organization.
4. Effective change management skills: Learn and apply change management strategies tailored for AI adoption, ensuring smoother transitions and higher acceptance rates among your teams.
5. AI policy and governance frameworks: Craft robust AI governance and policy frameworks to guide ethical and compliant use of AI technologies within your organization.
6. A tailored AI implementation roadmap: Create detailed, step-by-step roadmaps for AI deployment, from pilot testing to full-scale implementation, designed to meet your organization's specific needs.
7. Performance measurement expertise: Establish key performance indicators (KPIs) and monitoring systems for AI projects, enabling you to measure success and make data-driven adjustments.
8. Upgraded leadership and influence: Position yourself as a leader and influencer in your organization's AI journey, driving strategic initiatives and fostering an innovative culture.
9. Networking opportunities with your peers: Connect with other professionals, industry experts, and potential collaborators, expanding your professional network and sharing valuable insights and experiences.
10. A complete stakeholder engagement toolkit: Leave with templates, communication plans, and engagement strategies to ensure buy-in and support across your organization.