AINS6201: Automation & Process Optimization#
Aurnova MSAI track: Business AI
Credits: 3
Format: 8-week online graduate course
Examines automation, RPA, extraction, optimization, agentic workflows, controls, and business cases.
This course follows the Aurnova/Castalia course-site pattern used by AINS6003: each module includes book prose, an assignment notebook, slide notebook, narration, instructor notes, and an executable lab.
Course Outcomes#
By the end of the course, students will be able to:
explain the major concepts and tradeoffs in Automation & Process Optimization;
build or evaluate applied AI artifacts aligned with the course domain;
document assumptions, evidence, limitations, and operational risks;
connect technical work to governance, stakeholder needs, and deployment readiness.
Module Map#
Process discovery and workflow mapping — Which processes are good candidates for automation?
Robotic process automation basics — How do rule-based and AI-assisted automation differ?
Document and data extraction — How does AI convert unstructured work into structured workflows?
Optimization and scheduling — How can models improve resource allocation?
Agentic workflow orchestration — When should AI coordinate tools and humans?
Controls, auditability, and failure handling — How do automated processes remain accountable?
Change management and workforce impact — How should organizations adopt automation responsibly?
Automation business case — What evidence justifies automation investment?