# 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

1. **Process discovery and workflow mapping** — Which processes are good candidates for automation?
2. **Robotic process automation basics** — How do rule-based and AI-assisted automation differ?
3. **Document and data extraction** — How does AI convert unstructured work into structured workflows?
4. **Optimization and scheduling** — How can models improve resource allocation?
5. **Agentic workflow orchestration** — When should AI coordinate tools and humans?
6. **Controls, auditability, and failure handling** — How do automated processes remain accountable?
7. **Change management and workforce impact** — How should organizations adopt automation responsibly?
8. **Automation business case** — What evidence justifies automation investment?