Course Code: ANL325
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Synopsis

Many machine learning models are initially developed in experimental environments such as notebook platforms (e.g., Jupyter Notebook or Google Colab). This course prepares students to operationalise these models by embedding them into business processes, automation pipelines and organisational decision contexts. Students will learn a systematic approach to analysing business processes, identifying automation opportunities, applying Robotic Process Automation (RPA) and designing AI-supported tasks within operational environments. This course emphasises how AI systems support data processing, predictive analysis, and operational decision-making within automated workflows. Students will also develop competencies in monitoring deployed models, including techniques for detecting model bias, analysing model drift, and evaluating model performance over time. Through hands-on exercises and case-based learning, students will design scalable automation solutions that integrate data analysis, predictive modelling, RPA-enabled workflows and operational decision-making. This course enables students to understand how AI technologies evolve from isolated analytical models into integrated components of business processes and AI-augmented work environments, preparing them to contribute to organisational digital transformation and operational innovation.
Level: 3
Credit Units: 5
Presentation Pattern: EVERY JULY

Topics

  • Introduction to AI-Enabled Business Automation
  • Identifying Automation Opportunities in Business
  • Designing AI-Supported Tasks in Business Workflows
  • Human-in-the-Loop Automation in Business Processes
  • Data Infrastructure and Data Integration for Automation
  • Machine Learning Models for Business Automation
  • Automating the Operational Decision-Making
  • AI-Enabled Enterprise Integration Pathway
  • Monitoring Model Performance and Model Drift
  • Detecting and Mitigating Model Bias
  • Designing a Scalable Enterprise Integration and Automation Framework
  • Ethics, Governance and Risk Management

Learning Outcome

  • Discuss the role of AI in business automation and supporting decision-making in organisations.
  • Demonstrate an understanding of the key components of AI-enabled automation systems for business operations.
  • Analyse business processes to identify opportunities for AI-enabled automation.
  • Implement machine learning procedures in an automated workflow.
  • Design AI-enabled automation solutions that integrate data pipelines, models, and deployment strategies.
  • Evaluate the performance of deployed AI-enabled automation systems.
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