Details for MAN540 Artificial Intelligence and Data-Driven Decision-Making

AI is reshaping how organisations decide, but leaders still carry the responsibility. Artificial Intelligence and Data-Driven Decision-Making shows you what business intelligence, predictive analytics and generative AI can and can’t do. You’ll learn to question data quality, read model output, recognise bias, and apply Australian AI governance and ethics frameworks with human dignity in view. You’ll finish ready to recommend, and defend, whether your organisation should adopt an AI capability.

Quick Stats

• Currently offered by Alphacrucis: Yes
• Course code: MAN540
• Credit points: 10

Subject Coordinator

Prerequisite

Nil

Unit Content

Outcomes

1. Critique the assumptions, data quality and inferential limits of business intelligence and predictive analytics outputs used to inform managerial decisions.
2. Evaluate the respective capabilities, costs and limitations of business intelligence, predictive analytics, and generative artificial intelligence for a defined organisational problem.
3. Design an evidence-based decision process that integrates analytic and generative AI outputs with human judgement, accountability, and oversight.
4. Appraise the ethical, regulatory, and governance obligations related to an organisation’s use of artificial intelligence, including its obligations to affected stakeholders and to human dignity.
5. Advocate a justified recommendation on the adoption, modification, or rejection of an AI-enabled decision capability to specialist and non-specialist audiences.

Subject Content

1. Decisions, evidence and judgement: what makes a decision ‘data-driven’
2. The organisational data environment: quality, provenance, lineage and the cost of poor data
3. Business intelligence: descriptive analytics, dashboards, KPIs and the limits of hindsight
4. From business intelligence to artificial intelligence: what changes when a system infers rather than reports
5. Predictive analytics I: prediction as a managerial input – models, features and forecasting
6. Predictive analytics II: reading model output – accuracy, error, drift and false confidence
7. Bias, fairness and the reproduction of existing patterns in machine learning
8. Generative AI as a managerial tool: capabilities, failure modes and verification practice
9. Deploying generative AI in the organisation: workflow redesign, productivity claims and the evidence base
10. Human judgement, dignity and the moral limits of automated decision-making
11. AI governance I: the Australian environment – the National AI Plan, Guidance for AI Adoption, Australia’s AI Ethics Principles, and privacy obligations for automated decision-making
12. AI governance II: assuring what you cannot inspect – AS ISO/IEC 42001:2023, model opacity, drift and vendor dependency, and what AI adds to an organisation’s existing risk and assurance regime (builds on BUS502)
13. Building decision capability: AI strategy, organisational maturity, change, and the decision not to adopt

This course may be offered in the following formats

• Face to face on site 
• E-learning (online) 
• Intensive (provide details) 

Please check the timetable to see when this subject will next be offered at Alphacrucis University College.

Assessment Methods

1. Analytical critique of a business intelligence or analytics output (individual written report, 1,800 words) (30%)
2. Applied AI decision exercise: annotated artefact and reflective commentary (individual; artefact plus 2,000-word commentary) (40%)
3. AI adoption and assurance recommendation with oral defence (30%)