Upon completion of this course, participants will be able to:

• Differentiate between AI, Machine Learning, Deep Learning, and their respective business applications.
• Identify, evaluate, and prioritize high-value AI/ML use cases across core business functions (e.g., marketing, operations, finance).
• Understand the full ML lifecycle, from problem framing and data preparation to deployment and model monitoring.
• Develop a strategic roadmap for building the necessary data infrastructure and talent capabilities to support enterprise AI.
• Navigate the critical organizational challenge of building trust and driving adoption of AI-driven decisions.
• Understand the ethical considerations, bias risks, and governance requirements inherent in AI deployment.
• Calculate the potential ROI of an AI initiative, focusing on both cost savings and revenue generation.

• Business Unit Leaders and General Managers
• Heads of Data, Analytics, and IT
• Product Managers overseeing AI-driven features
• Strategic Planners and Corporate Development Teams

The methodology is structured for non-technical leaders to make strategic AI decisions. **Scenarios** involve deciding on the feasibility of an AI use case given known data constraints and bias risks. **Case studies** analyze companies that achieved massive scale with AI (e.g., Netflix recommendations, Amazon logistics) and the data infrastructure they built. **Group activities** focus on collaboratively mapping a process to identify and prioritize the top three AI use cases for a fictional organization. **Individual exercises** require participants to define the necessary data sources and governance requirements for a chosen AI project. **Syndicate discussions** debate the best organizational structure for the AI function (centralized COE vs. decentralized embedding).

UNIT 1: AI Foundations and Business Value

• Delineating AI, ML, Deep Learning, and different model types (Supervised, Unsupervised)
• Analyzing high-impact AI use cases across industries (e.g., personalized marketing, predictive maintenance)
• Framing business problems that are suitable for AI/ML solutions (The Feasibility Check)
• The concept of "Augmented Intelligence": AI enhancing, not replacing, human labor

UNIT 2: The ML Project Lifecycle and Data Readiness

• The critical role of data quality, data governance, and feature engineering in AI success
• Understanding the stages of the ML lifecycle (MLOps): Training, Validation, and Deployment
• Organizational readiness: Identifying necessary talent (Data Scientists, ML Engineers, Product Owners)
• Building the initial business case and measuring the potential financial uplift

UNIT 3: Managing Model Performance and Trust

• Understanding Model Drift and techniques for continuous model monitoring and retraining
• Introduction to Explainable AI (XAI) and why transparency is essential for trust and compliance
• Identifying and mitigating algorithmic bias in training data and model outputs
• Governance frameworks for responsible and ethical AI deployment

UNIT 4: Scaling AI Across the Enterprise

• Designing a central AI/ML platform to standardize tools and accelerate development
• The importance of cloud infrastructure (e.g., GPU access, specialized services) for scaling AI
• Developing an "AI-first" mindset in product and process design
• Strategies for change management and driving adoption of AI recommendations within the workforce

UNIT 5: Advanced Topics and Future Trends

• The strategic differences between traditional ML and modern Generative AI (LLMs, Image Generation)
• Introduction to Reinforcement Learning and its application in optimization and control systems
• Analyzing the impact of Federated Learning and Edge AI on data privacy and latency
• Developing a talent strategy to maintain a competitive edge in AI capability