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Artificial Intelligence in Organisations (MGMT90267)

Graduate courseworkPoints: 12.5Online

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Overview

Availability
Term 2 - Online
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The expectations on contemporary managers to make agile decisions in complex digitised environments are increasing. This subject focuses on the development of managerial capabilities to engage appropriate decision-making models and employ new-generation analytic tools that best-fit the scenario as it unfolds. This subject provides both conceptual frameworks and practical illustrations of quantitative decision-making techniques including decision trees and optimisation to tackle business problems. Students will be shown how to use these quantitative approaches to analyse business problems and, based on these analyses, make effective decisions. These frameworks can be applied to various decisions faced by organisations, with applications in different areas such as operations, marketing, financial and nursing management.

Managers must also decide if and how to employ Artificial Intelligence (AI) within their workplace to create value for the organisation. This subject explores the function, advantages and limitations of AI and machine learning, and how these tools can optimise decision making in complex environments. The subject focuses on: (1) the foundations of AI—inputs, processes, and outputs; (2) AI in action in various contexts to evaluate the opportunities and challenges; and (3) the critical analysis of AI to illustrate contrasting scenarios in which the adoption of AI creates value and destroys value for the organisation.

Intended learning outcomes

On completion of this subject, students should be able to:

  • Analyse and apply the function and processes of Artificial Intelligence (AI) to aid decision making in complex business environments.
  • Critically evaluate the opportunities, limitations, and ethicalities of adopting AI to create value for the organisation.
  • Analyse and apply the fundamentals of decision making for business problems and application of quantitative approaches to management.
  • Research and apply machine learning techniques such as K-Means, Classification Tree, Logistic Regression and Artificial Neural Network to various business problems.

Generic skills

  • Become skilled at analysing business decision problems, building analytics models and solving these to gain managerial insights
  • Critical thinking in relation to the effectiveness of solutions and managerial decision analysis
  • Application of theory to practice in the field of management science

Last updated: 4 March 2025

Eligibility and requirements

Prerequisites

None

Corequisites

None

Non-allowed subjects

None

Inherent requirements (core participation requirements)

The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.

Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home

Last updated: 4 March 2025

Assessment

DescriptionTimingPercentage

Written activity

  • 500 words
Week 210%

Written activity

  • 500 words
Week 410%

Report

  • 1500 words
Week 530%

Quiz

  • 1000 words (equivalent)
Week 720%

Report

  • 1500 words
Week 930%

Last updated: 4 March 2025

Dates & times

  • Term 2 - Online
    CoordinatorsMichal Carrington and Zahra Hosseinifard
    Mode of deliveryOnline
    Contact hours24 hours of online contact; 56 hours of case study analysis; 20 hours of online forum discussion activity; 70 hours of online activities, participation in simulations, and other management related analyses.
    Total time commitment170 hours
    Pre teaching start date28 April 2025
    Pre teaching requirementsDuring the pre-teaching period, students should familiarise themselves with the learning platform and the subject requirements.
    Teaching period 5 May 2025 to 29 June 2025
    Last self-enrol date29 April 2025
    Census date23 May 2025
    Last date to withdraw without fail13 June 2025
    Assessment period ends 6 July 2025

    Term 2 contact information

    Email: continuing-education@unimelb.edu.au

    Phone: + 61 3 8344 0149

    Contact hours: https://unimelb.edu.au/professional-development/contact-us

    Dr Zahra Hosseinifard zahra.h@unimelb.edu.au

    A/Prof Michal Carrington: michal.carrington@unimelb.edu.au 

What do these dates mean

Visit this webpage to find out about these key dates, including how they impact on:

  • Your tuition fees, academic transcript and statements.
  • And for Commonwealth Supported students, your:
       -  Student Learning Entitlement. This applies to all students enrolled in a Commonwealth Supported Place (CSP).
     

    Subjects withdrawn after the census date (including up to the ‘last day to withdraw without fail’) count toward the Student Learning Entitlement.

Last updated: 4 March 2025

Further information

  • Texts

    Prescribed texts

    There are no specifically prescribed or recommended texts for this subject.

Last updated: 4 March 2025