Foundations of Enterprise AI Adoption

This unit introduces core concepts, organizational challenges, and practical frameworks for adopting AI at enterprise scale. Students learn responsible AI principles, use-case identification and prioritization, roles and team structures, reference architectures, MLOps fundamentals, and cloud/data considerations to prepare for hands-on design and implementation in later units.

Learning Objectives

• Analyze organizational challenges, benefits, and key factors (including cloud-based data processing and service selection) that affect enterprise-wide AI adoption and produce a prioritized list of candidate use cases using a structured identification process
• Evaluate roles, responsibilities, and people-centric strategies; apply a team-building matrix to propose an organizational plan that aligns stakeholders and resources to achieve defined AI business outcomes
• Demonstrate application of the pillars of Responsible AI by completing a Responsible AI design template for a selected use case and specifying monitoring, governance, and mitigation strategies for model deployment
• Compare reference architecture patterns, MLOps components, and ML algorithm/platform trade-offs and assess their suitability for a chosen use case based on data needs, scalability, and operationalization requirements