Operationalizing Customer AI Systems

This unit focuses on taking AI and data tools from prototype to production in customer-facing contexts: sentiment analysis, recommendation engines, automated data collection, AutoML, and generative models. Students learn to design human-AI workflows, assess trade-offs (data vs. model, explainability vs. accuracy), and develop governance, change-management, and BI integration plans to mitigate risks and measure impact.

Learning Objectives

• Analyze model performance and data-quality trade-offs by comparing evaluation metrics (e.g., precision, recall, AUC, calibration) across sentiment, recommendation, and generative-model tasks on provided customer datasets
• Apply human-AI collaboration frameworks to design operational workflows that allocate responsibilities between automated systems and human agents, producing a role-responsibility matrix and escalation criteria
• Demonstrate the ability to assemble and evaluate an end-to-end automated pipeline (data ingestion, preprocessing, model training/AutoML, deployment, and monitoring) for a customer-insights use case and produce a brief runbook
• Evaluate ethical, explainability, and democratization risks and create a mitigation plan that includes explainability trade-offs, change-management steps, BI integration points, and metrics for unintended consequences