Building and Governing Customer AI

This unit focuses on applying machine learning and AI techniques to customer-facing problems—sentiment analysis, recommendation systems, automated data collection, and generative models—while addressing deployment, explainability, and organizational change. Students will learn practical pipelines, human–AI collaboration patterns, risk mitigation strategies, and how to integrate models with business intelligence and operations.

Learning Objectives

• Analyze customer interaction data (text, voice, behavioral) to select and justify appropriate models and pipelines for sentiment analysis and recommendation tasks
• Apply automated data-collection, feature-engineering, and AutoML workflows to build, validate, and deploy customer-facing models that integrate with BI systems
• Evaluate trade-offs among data quantity, model complexity, explainability, and predictive performance and propose mitigation strategies for unintended consequences and democratization risks
• Demonstrate a human–AI collaboration and change-management plan that includes governance, monitoring, and ethical safeguards for productionized customer AI