Operationalizing AI for CRM

This unit explores how sentiment analysis, recommendation systems, and generative ML models are operationalized within customer relationship workflows, emphasizing automation, human-AI collaboration, and business intelligence. Students learn to integrate models into marketing operations, manage rapid change, and assess explainability, governance, and unintended consequences to prepare for implementation and scaling in subsequent units.

Learning Objectives

• Analyze customer feedback and interaction data using sentiment and recommendation outputs to identify personalization and automation opportunities
• Apply human-AI collaboration frameworks to design workflows that balance automated decisioning with human oversight, explainability, and monitoring
• Demonstrate a deployment plan for integrating ML/Generative AI models into marketing operations, including data pipelines, automated model training, monitoring, and change-management steps
• Evaluate ethical, privacy, and operational risks (including unintended consequences and democratization downsides) and recommend governance, BI, and explainability strategies to mitigate them