Operationalizing Enterprise AI

This unit guides students through scoping, building, and managing enterprise AI solutions with attention to data strategy, regulatory risk, and cross‑functional delivery. Learners will move from project setup to selecting algorithms, designing data pipelines, forming teams, and creating governance and monitoring plans to ensure reliable, valuable AI outcomes.

Learning Objectives

• Analyze enterprise AI project requirements and produce a scoped project plan that includes goals, stakeholders, milestones, success metrics, and a risk register for data, legal, and operational concerns
• Apply data engineering and quality practices to design a scalable ingestion and validation pipeline that supports model performance, reproducibility, and proactive failure detection
• Evaluate legal, regulatory, and ethical constraints (including medical and other high‑risk domains) and develop a compliance and review workflow that integrates with project governance
• Demonstrate selection and justification of algorithms and team roles by mapping business benefits to algorithmic approaches, proposing a data science team structure, and outlining deployment and monitoring strategies