Course coordinator
Associate Professor Saeed Akhlaghpour
Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- Study level
- Postgraduate Coursework
- Location
- External
- Attendance mode
- Online
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Business School
To remain competitive in the digital era, organisations need to undergo a digital transformation into data-driven organisational structures. Such transformation requires a careful strategy considering new structures and communities, culture, IT infrastructure (e.g. cloud) and legacy systems. This course explores the success factors for data driven transformation of business, and develops students' knowledge and skills in developing a transformation strategy.
This course explores how firms within the digital economy integrate, leverage, and expand their strategic competitive positioning using Artificial Intelligence (AI) and digital technologies. The course explores in detail the concept of digital maturity and associated capabilities, unpacking what it means to be a truly digital first organisation. Contemporary digital and business maturity models from academia and industry are utilised across the course.
The course is divided into five modules:
- Digital Transformationﾠ
- Strategy
- Technology
- People and Culture
- Bringing it Together: AI and Digital Trends and Predictions
This course offers a fully online, flexible, student experience that engages students through an interactive learning platform. The content engagement is further enhanced with a series of live sessions, reflections, and ethical debates on a social platform. Case analysis forms a key component across the modules, coupled with open discussion, and hands-on real-world problem solving. Studentsﾠneed to prepare their case analysis individually and engage in critical discussions with their peers.ﾠ
Course requirements
Recommended prerequisites
We recommend completing the following courses before enrolling in this one:
BSAN7205 or DATA7001
Restrictions
MBusAn, MDataSc
Course contact
Course staff
Lecturer
Associate Professor Saeed Akhlaghpour
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Please note: Teaching staff do not have access to the timetabling system to help with class allocation. Therefore, should you need help with your timetable and/or allocation of classes, please ensure you email business.mytimetable@uq.edu.au from your UQ student email account with the following details:
- Full Name
- Student ID
- Course Code
Aims and outcomes
This course aims to equip students with tools and insights ﾠto buildﾠ competitive advantage by leveraging AI and digital technologies. Students will develop deep business knowledge on how to successfully transform business in the digital age.ﾠﾠ
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Describe and apply critical factors for using AI and digital technologies successfully.
LO2.
Analyse and evaluate the current state of an organisation's digital maturity to identify focus areas for transformation.
LO3.
Design and justify an organisational roadmap to elevate digital maturity and gain competitive advantage by identifying short, medium, and long term opportunities.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Paper/ Report/ Annotation, Presentation | Digital Transformation Priorities | 40% |
26/08/2024 4:00 pm |
| Paper/ Report/ Annotation, Presentation | Digital Transformation Roadmap | 60% |
Presentation 18/10/2024 2:00 pm Final Report 25/10/2024 2:00 pm |
Assessment details
Digital Transformation Priorities
- Mode
- Product/ Artefact/ Multimedia, Written
- Category
- Paper/ Report/ Annotation, Presentation
- Weight
- 40%
- Due date
26/08/2024 4:00 pm
- Learning outcomes
- L01, L02
Task description
In this assessment, students are asked to read a teaching case study and develop a plan for a mindful transformation of G Telecom aimed at making the most out of Telco AI. Students will consider how the organisation’s strategy, structure, offerings and incumbent work routines should change to be ready for a new, data-driven era. As part of this assessment, students will prepare a short video and business report summarising their insights and priorities for change.
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
The assessment will be submitted via the course's Learn.UQ site.
Deferral or extension
You may be able to apply for an extension.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Digital Transformation Roadmap
- Mode
- Oral, Written
- Category
- Paper/ Report/ Annotation, Presentation
- Weight
- 60%
- Due date
Presentation 18/10/2024 2:00 pm
Final Report 25/10/2024 2:00 pm
- Learning outcomes
- L01, L02, L03
Task description
Building on the outcomes from the first assessment, the main task in this assessment is to devise a final plan for the digital transformation of G Telecom and communicate it in the form of a presentation and final report.
This plan should consider how G Telecom’s strategy, technology and operations, people, and culture need to change to enable successful digital transformation. Specifically, students are to develop recommendations for change, referencing the Digital First Framework; design a one-page roadmap for digital transformation including key steps for implementation, anticipated business outcomes, and potential risks and challenges.
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Deferral or extension
You may be able to apply for an extension.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 29 |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 30 - 46 |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 47 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
Grades will be allocated according to University-wide standards of criterion-based assessment.
Supplementary assessment
Supplementary assessment is available for this course.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Own copy required
You'll need to have your own copy of the following reading resources. We've indicated below if you need a personal copy of the reading materials or your own item.
| Item | Description |
|---|---|
| Book |
Designed for digital: how to architect your business for sustained success
by Ross; Jeanne W.; Beath; Cynthia Mathis; Mocker; Martin - 2019 Publisher: MIT Press ISBN: 9780262354776; 9780262354783 |
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Week 1 |
Not Timetabled |
Module 1: Digital Transformation Self-Directed Learning Sub-module 1.1: This sub-module focuses on the characteristics of Second Machine Age technologies and the differences between physical native vs digital native organisations. Learning outcomes: L01 |
Workshop |
Live Session This session includes a discussion on Organisations in the Second Machine Age Learning outcomes: L01 |
|
Week 2 |
Not Timetabled |
Module 1: Digital Transformation Self-Directed Learning Sub-module 1.2: This sub-module focuses on the challenges the Second Machine Age presents for organisations. Learning outcomes: L01 |
Workshop |
Live session This session includes a discussion on digital maturity and focuses on understanding and applying selected digital maturity frameworks. Learning outcomes: L01 |
|
Week 3 |
Not Timetabled |
Module 2: Strategy Self-Directed Learning Sub-module 2.1: This sub-module focuses on key components of organisational strategy and digital strategy, and relevant maturity frameworks and models. Learning outcomes: L01 |
Case-based learning |
Live Session This session includes a discussion of activities linked to the Carrefour Case study, as well as a discussion on Assessment 1 (Q&A). Learning outcomes: L01, L02 |
|
Week 4 |
Not Timetabled |
Module 2: Strategy Self-Directed Learning Sub-module 2.2: This sub-module focuses on strategic opportunities and implications as organisations plan for digital transformation. Learning outcomes: L01, L02 |
Workshop |
Live Session This session includes an active discussion around competitive positioning and organisational readiness for digital transformation. Learning outcomes: L01, L02 |
|
Week 5 |
Not Timetabled |
Module 2 - Reflection week Self-Directed Learning Opportunity to reflect on learnings from Module 2, progress any outstanding readings, collaborate with peers through Ed Discussion, and continue working on Assessment One. |
Week 6 |
Not Timetabled |
Module 3: Digital Technologies Self-Directed Learning Sub-module 3.1: This sub-module explores the nature of digital innovation and layered modular architectures. Learning outcomes: L01, L02 |
Workshop |
Live Session This session includes a discussion on technological capabilities Learning outcomes: L01, L02 |
|
Week 7 |
Not Timetabled |
Module 3: Digital Technologies Self-Directed Learning Sub-module 3.2: This sub-module focuses on some of the challenges with AI systems, switching risks and social and technical inertia. Learning outcomes: L01, L02, L03 |
Workshop |
Live Session This session explores Social and Technical Inertia / Digital Options Learning outcomes: L01, L02 |
|
Week 8 |
Not Timetabled |
Module 3 - Reflection week Self-Directed Learning Opportunity to reflect on learnings from Module 3, progress any outstanding readings, collaborate with peers through Ed Discussion, and work on Assessment Two. |
Week 9 |
Not Timetabled |
Module 4: People and Culture Self-Directed Learning Sub-module 4.1: This sub-module focuses on the intangible assets of people and culture and the role they play in an organisation's digital maturity journey. Learning outcomes: L01, L02 |
Workshop |
Live Session Assessment 2 Discussion and Q&A / Talent Attraction and Capability Development Learning outcomes: L01, L02 |
|
Mid Sem break |
No student involvement (Breaks, information) |
In-Semester Break |
Week 10 |
Not Timetabled |
Module 4: People and Culture Self-Directed Learning Sub-module 4.2: This sub-module discusses the concepts of governance as it relates to digital technologies, referencing the Campbell Soup Case Study. Learning outcomes: L01, L02, L03 |
Case-based learning |
Live Session This session focuses on the Campbell Soup Case Study and governance mechanisms used to support the transformation journey Learning outcomes: L01, L02, L03 |
|
Week 11 |
Not Timetabled |
Module 4 - Reflection week Self-Directed Learning Opportunity to reflect on learnings from Module 4, progress any outstanding readings, collaborate with peers through Ed Discussion, and continue working on Assessment Two. |
Week 12 |
Not Timetabled |
Module 5: Integration - Bringing it all together Self-Directed Learning Sub-module 5.1: This sub-module focuses on the key roles of technology in service interactions, and the benefits of a holistic framework to guide an organisation's digital maturity journey. The Certis case study is used as a key learning tool. Learning outcomes: L01, L02, L03 |
Case-based learning |
Live Session This session focuses on the Certis Case Study Learning outcomes: L01, L02, L03 |
|
Week 13 |
Not Timetabled |
Module 5: Integration - Bringing it all together Self-Directed Learning Sub-module 5.2: This sub module includes a discussion on emerging trends and predictions influencing the future evolution of technology and business. Learning outcomes: L01, L02, L03 |
General contact hours |
Live Session This is the last live session of the course. It will include a summary discussion and Q&A. Learning outcomes: L01, L02, L03 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.