BAN312 - Artificial Intelligence for Enterprises

Credit Points: 15 credit points

Workload: 36 hours

Prerequisite: N/A

Co-requisite: N/A

Aims & Objectives

This is a third-year Core Unit in the Bachelor of Business, Major in Business Analytics, and offered as a recommended Elective Unit in the Bachelor of Business, Major in Marketing and Digital Communications, and Bachelor of Business, Major in Management. For Course Learning Outcomes and further information relating to Bachelor of Business programs, please visit our website: http://www.mit.edu.au/study-with-us/programs/bachelor-business.  

This unit introduces advanced theories, concepts, practices, and techniques of Artificial Intelligence that are currently practised in enterprises. Machine learning, specifically Artificial intelligence (AI), has provided organisations with algorithms, tools, and an expert base to analyse data to provide business insights and strategies, including business problem-solving skills to deliver better data-determined decisions. This unit is aimed at positioning and equipping students within the business sector with fundamental AI knowledge and competencies by applying the principles of AI based on data. Students will gain skills in analysing structured and unstructured data within the business context to derive strategies and enhance business insights. On successful completion of this course, students will obtain professional perspectives and technical confidence regarding the use of AI for business analytics. 

Unit topics include: 

  • Understanding AI in Business Applications 
  • Digital Revolution and Sustainability 
  • Human Apprehension and AI 
  • AI in Human Resources, Marketing, Tourism and Advertising 
  • AI in Retail Marketing, Insurance and Finance 
  • Innovative Recruitment Strategies Using Knowledge Management Systems 
  • Evolution of Chatbot in Human Resource Management 
  • Adoption of AI in Small and Medium sized Enterprises (SMEs) 

Learning Outcomes

  1. Course learning outcomes 

  • The Course learning outcomes applicable to this unit are listed on the Melbourne Institute of Technology's website: www.mit.edu.au   

  1. Unit learning outcomes  

  • Apply critical thinking and AI tools in solving business problems. 
  • Develop technical confidence in the application of AI for enterprises based on the professional code of ethics. 
  • Evaluate and synthesise business solutions and scenarios using machine learning methods and secure data visualisation techniques. 
  • Effectively communicate business strategies and insights derived from AI enablers. 

Assessment

Assessment Task Due Date A B Learning Outcomes Assessed 
1. Assessment 1: (Individual) – Opinion Paper Week 3 - 10%  a
2. Assessment 2: (individual) – Data Analysis Activity  Week 8    30%  b-d 
3. Assessment 3: Contribution and Participation Weeks 1 to 12  - 10% a-d 
4. Assessment 4: (Group) Project Report (3,000 words)  Week 12  50%  - a-d 
TOTALS   50%   50%   

Task Type: Type A: unsupervised, Type B: supervised.

Contribution and Participation (10%)

This unit has class participation as an assessment. The assessment task and marking rubric will follow the Guidelines on Assessing Class Participation (https://www.mit.edu.au/about-us/governance/institute-rules- policies-and-plans/policies-procedures-and-guidelines/Guidelines_on_Assessing_Class_Participation). 

Further details on the type of assessment tasks and the marking rubrics will be provided in the assessment specification. 

Textbook and Reference Materials

Note: Students are required to purchase the prescribed textbook and have it available each week in class. 

Prescribed Textbooks

  • Hemachandran K, Raul V. Rodriguez (2024), Artificial Intelligence for Business An Implementation Guide Containing Practical and Industry-Specific Case Studies, Routledge. 

Recommended Texts

  • Akerkar, R. (2019), Artificial Intelligence for Business. Western Norway Research Institute, Sogndat, Norway: Springer. 
  • Haq, R. (2020), Enterprise Artificial Intelligence Transformation. Hoboken, New Jersey: John Wiley & Sons, Inc. 
  • Agbinya, J.I. (2020), Applied Data Analytics–Principles and Applications. Denmark: River Publishers. ISBN 978-87-7022-096-5 (Hardback), ISBN 978-87-7022-095-8 (eBook).  

Journal articles

  • AI Open (Q1) 
  • Artificial Intelligence (Q1) 
  • Big Data Mining and Analytics (Q1) 
  • International Journal of Information Management (Q1) 
  • IEEE Transactions on Pattern Analysis and Machine Intelligence (Q1) 
  • IEEE Transactions on Neural Networks and Learning Systems (Q1) 
  • IEEE Transactions on Cognitive Communications and Networking (Q1) 

The Referencing style for this unit is APA:  

See the MIT Library Referencing webpage: https://library.mit.edu.au/referencing/APA and the Unit Moodle page for additional referencing support material and web links. 

Graduate Attributes

MIT is committed to ensure the course is current, practical and relevant so that graduates are “work ready” and equipped for life-long learning. In order to accomplish this, the MIT Graduate Attributes identify the required knowledge, skills and attributes that prepare students for the industry.
The level to which Graduate Attributes covered in this unit are as follows:

Ability to communicate Independent and Lifelong Learning Ethics Analytical and Problem Solving Cultural and Global Awareness Team work Specialist knowledge of a field of study

Legend

Levels of attainment Extent covered
The attribute is covered by theory and practice, and addressed by assessed activities in which the students always play an active role, e.g. workshops, lab submissions, assignments, demonstrations, tests, examinations.
The attribute is covered by theory or practice, and addressed by assessed activities in which the students mostly play an active role, e.g. discussions, reading, intepreting documents, tests, examinations.
The attribute is discussed in theory or practice; it is addressed by assessed activities in which the students may play an active role, e.g. lectures and discussions, reading, interpretation, workshops, presentations.
The attribute is presented as a side issue in theory or practice; it is not specifically assessed, but it is addressed by activities such as lectures or tutorials.
The attribute is not considered, there is no theory or practice or activities associated with this attribute.