Overview
This certification program provides business leaders and professionals with the knowledge and skills to understand, lead, and implement AI and Generative AI initiatives within their organizations. Participants will gain a strategic view of AI technologies, explore responsible and ethical adoption practices, and learn practical techniques to guide AI projects from concept to execution. Through real-world case studies and leadership-focused frameworks, attendees will be prepared to champion AI transformation efforts and position their organizations for long-term success in an AI-driven world.

Target Student
This course is designed for executives, managers, and business professionals responsible for strategic decision-making and organizational change. Ideal for those seeking to understand AI’s potential, guide responsible adoption, and lead cross-functional teams in AI initiatives. No technical background is required, though familiarity with general business and technology concepts is helpful.

Prerequisites
No prior AI or programming experience is required. Basic understanding of business processes and organizational leadership is recommended.

COURSE OUTLINE

Introduction to AI and Generative AI Leadership
Defining Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, and Generative AI
How Generative AI differs from traditional AI and why it matters now
The business case: how AI is driving change and creating opportunities
The evolving role of leaders in the AI era

AI and Generative AI Foundations for Leaders
Core AI and GenAI concepts: foundation models, large language models, and modalities (text, image, audio, etc.)
The machine learning lifecycle: data, training, deployment, and management
Understanding data types: structured vs. unstructured, labeled vs. unlabeled, and their business implications
Choosing the right models for business needs and impact
Layers of the GenAI landscape: infrastructure, platforms, agents, and applications
Real-world use cases across industries

Responsible, Secure, and Ethical AI
Responsible AI principles: fairness, transparency, privacy, and accountability
Security and governance considerations in AI adoption
Risk management and building trust in AI solutions

Techniques to Improve Model Output
Introduction to prompt engineering and its importance in practical AI use
Grounding and retrieval-augmented generation (RAG): connecting AI outputs to real-world information
The human-in-the-loop approach: balancing automation and oversight
Model settings and continuous improvement in AI applications

Business Applications and Use Cases
Identifying and prioritizing high-impact AI use cases for your organization
Mapping Generative AI capabilities to business functions and goals
Case studies and lessons learned from successful AI implementations

Building and Leading AI Teams and Initiatives
Strategies for leading AI-driven change and digital transformation
Building and enabling cross-functional AI teams
The AI project lifecycle: from vision to value measurement
Leadership best practices and common pitfalls in AI adoption

Future Trends and the Evolving AI Landscape
The pace of AI innovation and what it means for business leaders
Preparing your organization for ongoing change and disruption
Ethical frontiers and the future of AI in society