Module 1: Understanding AI and the Business Impact

• The Evolution of AI: From Rule-based Systems to Machine Learning to Generative Models
• What Makes Generative AI Distinct: Creating New Content Rather Than Predicting Outcomes
• Why 2023–2025 Marked A Major Shift in AI Adoption and Business Impact
• How AI Is Reshaping Key Sectors: Healthcare, Finance, Retail, Media, And Manufacturing
• A Leadership-Focused Lens for Understanding and Evaluating AI
• Setting Personal and Organizational Goals for Learning and Application

Module 2: How Generative AI Works

• What Large Language Models Are, Explained Without Technical Jargon
• How Inputs, Outputs, And Context Windows Shape Model Behavior
• Why AI Hallucinations Occur and Their Impact on Organizational Use
• How Tokens, Cost, And Model Size Affect Budgeting and Deployment Decisions
• What Multimodal AI Enables Across Text, Images, Audio, And Code
• How Leading Models Differ at a High Level and What Matters for Leadership Decisions
• Key Warning Signs to Watch for When Evaluating AI Vendor Claims

Module 3: Prompt Engineering

• What Prompt Engineering Is and Why It Matters
• Core Techniques: Zero-shot, Few-shot, Chain-of-thought, Role Prompting
• Prompting For Executive Tasks Such as Briefings, Strategy Memos, And Stakeholder Communication
• Prompting For Team Tasks Including Documentation, Summaries, And Reporting
• Automating Routine Workflows with Reusable Prompt Templates
• Prompt Security and What to Avoid Sharing in Public AI Tools
• Hands-on Practice to Build Your Personal Prompt Library

Module 4: AI Across the Organization

• Marketing And CX: Personalization, Content Generation, Service Automation
• Operations: Workflow Automation, Documentation, Procurement, Supply Chain
• HR And Talent: Recruiting, Onboarding, Learning and Development, Performance
• Finance: Reporting, Forecasting, Compliance Summarization
• Product And Innovation: Ideation, Prototyping, Market Research
• Leadership And Strategy: Competitive Intelligence, Board Communication, Scenario Planning
• Case Studies: Klarna, Coca-Cola, JPMorgan and Others
• Use-case Evaluation: Assessing Value, Risk and Organizational Readiness

Module 5: Ethics, Governance, and Responsible AI

• The Ethics Landscape and What Is at Stake When AI Makes Decisions
• Where Bias in AI Comes from and How Leaders Can Mitigate It
• Privacy, Data Sovereignty, And Guidelines for Employee Use of AI Tools
• When Transparency and Explainability Are Required for Auditable AI
• The Regulatory Environment, Including the EU AI Act, U.S. Frameworks, And Sector Rules
• How Responsible AI Frameworks from Google, Microsoft, And NIST Translate into Practice
• Disinformation, Deepfakes, And Content Integrity Challenges
• How To Begin Building an Internal AI Policy For Your Organization

Module 6: Building an AI-Ready Organization

• What AI Readiness Means Across Technology, Data, People, And Culture
• Common Failure Patterns in AI Adoption and How to Avoid Them
• Communicating About AI Amid Uncertainty and Rapid Change
• Upskilling Your Workforce and Identifying Essential Future Skills
• Addressing Ai-related Anxiety and Supporting Psychological Safety
• Roles Of The CDO, CAIO, And AI Center Of Excellence
• Applying Change-management Frameworks Such as Kotter and ADKAR To AI Initiatives
• Building Internal AI Champions And Governance Structures

Module 7: The AI-Enabled Economy – Workforce, Disruption, and Opportunity

• The Economic Impact of Generative AI Across Productivity, Displacement, And New Value Creation
• Which Jobs and Tasks Are Most Exposed to Automation, And Which Remain Resilient
• AI Augmentation Versus Replacement and How Leaders Can Apply This Distinction
• How AI Is Reshaping Competitive Dynamics Through First-mover Advantage and Differentiation
• What Ai-native Competitors Reveal and What Incumbents Can Learn from Them
• Global AI Investment Trends and Their Implications for Sector Strategy
• How Leadership Is Evolving and Which Human Capabilities Grow in Importance

Module 8: Creating An AI Strategy

• Reviewing The Strategic AI Framework and Bringing All Concepts Together
• Using The AI Opportunity Canvas to Map Use Cases to Business Value
• Applying An Effort-impact-risk Matrix to Prioritize AI Initiatives
• Building An AI Roadmap Across Quick Wins, Mid-term Initiatives, And Long-term Transformation
• Crafting The Business Case for AI Investment and Making the Ask Effectively
• Presenting Your AI Strategy to Boards, Investors, And Skeptical Stakeholders
• Capstone Workshop to Develop and Peer-review Your Organizational AI Strategy
• Final Reflection on the Kind of AI Leader You Aim to Become

Capstone Project

The course includes a capstone project where learners synthesize concepts from all modules into an actionable AI strategy for their organization. Participants develop a prioritized opportunity map, build an adoption roadmap with owners and timelines, and outline key risks, dependencies, and success metrics. The capstone ensures that every learner leaves with a concrete, organization-ready plan that translates learning into practical, strategic action.

Module 1: Foundations of Generative and Agentic AI

• ⁠Evaluate the strategic value of AI functionalities such as chatbots, reasoning, and multimedia
• Construct an evaluation of the cost of an AI system
• ⁠Distinguish between major AI model types and terminology

Module 2: The Rise of Agentic AI and Emerging AI Platforms

• Explain the most relevant AI platform or approach for a specific sector and its application to agentic AI use cases
• Evaluate the key factors influencing the selection of open-source versus proprietary AI platforms within a specific organizational context
• Develop a landing page using AI
• Prompt AI to create a visual mock-up and functional HTML code
• Activate the code by saving and reuploading
• Explain a new AI workflow in an organization

Module 3: Connecting Agents to Digital Ecosystems

• Construct a use case demonstrating agent-based interaction across integrated tools
• Write a structured email-style proposal that outlines a specific use case for an AI agent within an organizational context
• Analyze a business workflow to determine how an AI agent could improve efficiency, reduce costs, or enhance user experience
• Design an integration approach that specifies how the proposed agent would connect with existing systems, platforms, or application programming interfaces (APIs)
• Evaluate the potential risks, ethical considerations, and success metrics associated with deploying the proposed AI agent

Module 4: Cybersecurity: Classic Scenarios, Agent Risks, Disinformation, and Systemic Impact

• Analyze organizational AI systems and workflows to identify potential cybersecurity risks using the National Institute of Standards and Technology (NIST) Cybersecurity Framework categories
• Evaluate current security practices to identify gaps in access control, monitoring, response, and recovery capabilities
• Develop a structured AI risk and security plan, including stakeholders, training, and incident response procedures
• Recommend actions to improve organizational readiness across identify, protect, detect, respond, and recover domains
• Analyze organizational AI systems and workflows to identify potential cybersecurity risks
• Evaluate how accountability is defined and enforced alongside security practices and governance in AI systems

Module 5: AI Agents by Business Function

• Describe the organizational context, including industry, organization type, and department, relevant to a proposed AI-driven product design initiative
• Summarize the current product design workflow within an organization to establish a baseline for improvement
• Select an appropriate AI technology for integration into a product design process based on its capabilities and relevance
• Develop a structured plan outlining how AI can be integrated into a product design workflow to improve efficiency, effectiveness, or quality
• Identify an appropriate AI agent architecture for a given organizational context and explain key trade-offs
• Identify opportunities for AI-enabled BPO and describe their potential organizational impact

Module 6: The Last Mile- From Pilot to Practice

• Propose measurable key performance indicators (KPIs) that evaluate the effectiveness of an AI system in relation to business outcomes.
• Describe the organizational context including sector, organization type, and department relevant to an AI implementation
• Summarize the purpose and functionality of a proposed AI system within a business workflow
• Write three to five key performance indicators (KPIs) that measure the effectiveness of an AI implementation
• Evaluate how the selected KPIs align with business goals, and indicate whether the AI system is achieving its intended outcomes

Module 7: Governance, Compliance, and Agent Testing

• Identify applicable regulatory frameworks (e.g., GDPR, CCPA, HIPAA) relevant to a specific AI use case
• Analyze the risks associated with deploying AI systems, including both compliance and operational risks
• Apply appropriate testing strategies (e.g., sandboxing, A/B testing, safety checks) to evaluate AI system behavior
• Develop a comprehensive AI governance plan that integrates regulations, testing, risk mitigation, and documentation practices
• Create guiding questions that identify key regulatory and implementation considerations in real-world AI healthcare scenarios
• Classify AI use cases using the risk speed quadrant framework

Module 8: Ethics and Capstone

• Explain how AI can be strategically integrated into organizational functions to create business value
• Evaluate the suitability of AI technologies for specific organizational use cases
• Analyze the cost, security, and operational implications of AI adoption
• Assess the human and organizational factors that influence successful AI implementation
• Synthesize course concepts into a structured approach for organizational AI adoption
• Evaluate ethical risks in a proposed AI system by identifying a potential issue, assessing its business impact, and recommending an appropriate mitigation strategy

Capstone Project

The course culminates in a capstone project where participants apply course concepts to evaluate an organizational AI opportunity. They will assess the suitability of AI technologies for a specific use case, analyze the associated business, operational, security, and ethical considerations, and develop a structured approach for responsible AI adoption. The final deliverable includes recommendations for implementation, risk mitigation, and value creation within an organizational context.