MIS780 – Advanced Artificial Intelligence for Business
Trimester 2, 2025
Assessment Task 3 – Business Report – Individual
DUE DATE: Thursday, 2nd October 2025, by 8:00 pm (Melbourne time)
PERCENTAGE OF FINAL GRADE: 35%
WORD COUNT: 3000 words ± 10% (Written report)
Word limit is applied for this assessment. Note that the cover page, references, table of
contents, tables and figures (if any) are not included in the word count.
Description
Purpose
This assessment aims for students to learn to:
Understand the applications of analytics and AI solutions in various business contexts.
Appraise the suitability of the solutions to solve business problems.
Critically evaluate the outcomes of the solutions and the practical implications of the findings.
Context/Scenario
You are appointed as an AI Business Solution Consultant, tasked with advising a chosen organisation on how
artificial intelligence can address key business challenges. Your objective is to produce a comprehensive
business report recommending three AI solutions tailored to the organisation’s specific needs. Your report
should address both mid-level managers and AI specialists. The AI solutions you propose must be customised
specifically to the context and challenges of the selected organisation.
You are required to choose one organisation from the three options based on the last digit of your student
ID, according to the following table.
Last Digit of the Student Number Assigned Scenario
0,3,6,9 Organisation 1: Southern Coast Retail Group
1,4,7 Organisation 2: Blue Ridge Healthcare
2,5,8 Organisation 3: Ever Green Energy
For example, if your student number ends with a “1”, then you should select Organisation 2: Blue Ridge
Healthcare.
Organisation 1: Southern Coast Retail Group
Southern Coast Retail Group (SCRG) is a major Australian retailer with over 200 stores, an e-commerce
platform, and a growing mobile app. The company aims to enhance customer engagement by leveraging data
from purchases and loyalty schemes to offer more personalised product recommendations and promotions.
Its stores face inventory imbalances and seasonal demand surges that are hard to predict, requiring smarter
optimisation of stock levels and supply chain routes. Back-office tasks such as invoicing, supplier
communication, and compliance reporting remain largely manual, which slows down operations. SCRG also
seeks to better understand customer communities, social media influencers, and referral networks to boost
marketing effectiveness. Demand can be unpredictable due to weather, tourism, and local events, so the
company needs systems capable of managing uncertainty in forecasting and store operations. Lastly, SCRG is
exploring AI-powered assistants to provide 24/7 customer support, answer product or delivery questions, and
assist managers in making better operational decisions while maintaining consistency.
Organisation 2: Blue Ridge Healthcare
Blue Ridge Healthcare is a regional provider managing several hospitals and clinics. The organisation aims to
improve patient outcomes by developing personalised treatment plans tailored to each patient’s needs. It also
faces long wait times caused by complex staff rosters and appointment scheduling, creating a need for smarter
resource optimisation. Administrative tasks, such as insurance claims, billing, and patient record entry, are still
mostly manual, which slows operations and increases costs. The organisation also seeks to better understand
referral networks and community health patterns to strengthen preventative care. In clinical decision-making,
doctors often work with uncertain or incomplete patient data, which calls for systems capable of handling
ambiguity in treatment support. Finally, the organisation is interested in exploring AI assistants for patient
engagement, scheduling, and compliance monitoring to support staff and ensure adherence to healthcare
regulations.
Organisation 3: Ever Green Energy
Ever Green Energy is a regional provider specialising in renewable energy integration and smart grid
technologies. A key challenge is enhancing demand forecasting, where uncertainty arises from weather
conditions, consumer behaviour, and renewable energy variability. The company also aims to find smarter
ways to optimise energy distribution across the grid, ensuring efficiency and stability while lowering costs.
Many operational processes, such as meter data validation, billing, and compliance reporting, remain manual
and time-consuming. Ever Green wants to better understand energy usage patterns and community-level
consumption networks to encourage sustainable practices. Customers increasingly expect personalised
energy plans and advice for efficiency upgrades, such as solar panels or smart home solutions. Finally, the
organisation is exploring cognitive AI systems to support operators with real-time decision-making, improve
system reliability, and ensure compliance with strict regulatory frameworks.
Specific Requirements and Report Guidelines
You need to pick three AI concepts from the list below, link them to the organisation’s main challenges, and
suggest AI solutions to address those challenges.
Social Network Analysis
Evolutionary Computation
Recommendation Systems
Fuzzy Control Systems
Cognitive Computing
Robotic Process Automation
Your report should include the following (suggested) sections.
Executive Summary: Clear, concise, and accessible executive summary for a mixed audience of mid-level
managers and AI specialists.
Introduction: Introduce the purpose of the report, explain the relevance of AI in the organisation’s
industry, and outline the scope of your analysis. Include references to support your discussion where
appropriate.
Organisation Profile: Provide a brief overview of the organisation’s operations, size, customer or
stakeholder base, and current challenges. Focus on issues that are relevant for mapping AI solutions,
ensuring clarity and context for the reader.
AI Solutions: For each AI solution, explain the concept clearly, describe how it addresses a specific
organisational problem, and support your arguments with academic research and industry evidence.
Critically evaluate each solution by discussing its feasibility, expected benefits, and potential limitations,
ensuring a clear connection between the AI technique and the organisation’s strategic objectives. Use
tables, diagrams, or visuals where appropriate to enhance clarity and understanding.
Challenges and Ethical Considerations: Briefly discuss potential challenges that may arise when applying
AI solutions, along with relevant ethical considerations.
Conclusion: Summarise the key findings and emphasise evidence-based recommendations.
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References: The preferred referencing style for this assessment is APA7. Other referencing styles are also
accepted. However, ensure that all references are consistently formatted following the same chosen
referencing style. Refer to Deakin Referencing Guide for detailed information
https://www.deakin.edu.au/students/studying/study-support/referencing.
GenAI Acknowledgement (Please refer to the “Use of Generative Artificial Intelligence (genAI) in this
assessment” section.
Appendix (Optional) – if necessary
Your report should be professionally formatted with a cover page, a table of contents, appropriate headings,
subheadings, labelling of figures, tables, and captions, etc.
The report should be written following the Academic writing style
https://www.deakin.edu.au/students/studying/study-support/academic-skills/academic-style
Scholarly articles can be accessed from Academic Databases through Deakin Library
https://www.deakin.edu.au/library/a-z/databases
Note: You are not required to implement any of the proposed AI solutions using real data or Python
coding.
Learning Outcomes
This task allows you to demonstrate your achievement towards the Unit Learning Outcomes (ULOs) which
have been aligned to the Deakin Graduate Learning Outcomes (GLOs). Deakin GLOs describe the knowledge
and capabilities graduates acquire and can demonstrate on completion of their course. This assessment task
is an important tool in determining your achievement of the ULOs. If you do not demonstrate achievement of
the ULOs you will not be successful in this unit. You are advised to familiarise yourself with these ULOs and
GLOs as they will inform you on what you are expected to demonstrate for successful completion of this unit.
The learning outcomes that are aligned to this assessment task are:
Unit Learning Outcomes (ULOs) Graduate Learning
Outcomes (GLOs)
ULO 1: Appraise the suitability of major artificial intelligence and
advanced machine learning concepts to solve business problems
ULO3: Critically evaluate and justify the feasibility and efficacy of artificial
intelligence solutions in addressing real-world business requirements
GLO1: Discipline-specific
knowledge and capabilities
GLO4: Critical thinking
GLO5: Problem solving
Submission
See CloudDeakin for more info about this assessment, especially the marking rubric.
The assessment must be prepared using Microsoft Word. There is no template provided for this assessment.
However, the report must be professionally formatted following the structure outlined above. Sections and
subsections must be named and numbered appropriately. Upon completion of the assessment, name your
file as your firstname_lastname_MIS780A3 (e.g. John_Smith_MIS780A3.docx).
You are to submit your assessment in the individual Assessment Dropbox in the MIS780 CloudDeakin unit site
on or before the due date. DO NOT zip the file. Any submission contained in a zip file will not be marked.
Do not upload files obtained from the Internet or Academic Databases to your dropbox as you do not own the
copyrights. Instead, provide references and in-text citations in your report where appropriate.
Submitting a hard copy of this assessment is not required. You must keep a backup copy of every assessment
you submit until the marked assessment has been returned to you. In the unlikely event that one of your
assessments is misplaced, you will need to submit your backup copy.
Any work you submit may be checked by electronic or other means for the purposes of detecting collusion
and/or plagiarism and for authenticating work.
When you submit an assessment through your CloudDeakin unit site, you will receive an email to your Deakin
email address confirming that it has been submitted. You should check that you can see your assessment in
the Submissions view of the Assessment Dropbox folder after uploading and check for, and keep, the email
receipt for the submission.
Marking and feedback
The marking rubric indicates the assessment criteria for this task. It is available in the CloudDeakin unit site in
the Assessment folder, under Assessment Resources. Criteria act as a boundary around the task and help
specify what assessors are looking for in your submission. The criteria are drawn from the ULOs and align with
the GLOs. You should familiarise yourself with the assessment criteria before completing and submitting this
task.
This is the final assessment task in MIS780. Scores and feedback will be returned to students after the unit
results are released.
Extensions
Extensions can only be granted for exceptional and/or unavoidable circumstances outside of your control.
Requests for extensions must be made by 12 noon on the submission date using the online Extension Request
form under the Assessment tab on the unit CloudDeakin site. All requests for extensions should be supported
by appropriate evidence (e.g., a medical certificate in the case of ill health).
Along with the evidence supporting your reason for the extension, a draft showing work in progress and a
detailed document outlining the remaining tasks and required refinements of the draft should be submitted.
Applications for extensions after 12 noon on the submission date require University-level special
consideration and these applications must be must be submitted via StudentConnect in your DeakinSync site.
Late submission penalties
If you submit an assessment task after the due date without an approved extension or special consideration,
5% will be deducted from the available marks for each day after the due date up to seven days*. Work
submitted more than seven days after the due date will not be marked and will receive 0% for the task. The
Unit Chair may refuse to accept a late submission where it is unreasonable or impracticable to assess the task
after the due date. *’Day’ means calendar day for electronic submissions and working day for paper
submissions.
An example of how the calculation of the late penalty based on an assessment being due on a Thursday at
8:00pm is as follows:
1 day late: submitted after Thursday 11:59pm and before Friday 11:59pm– 5% penalty.
2 days late: submitted after Friday 11:59pm and before Saturday 11:59pm – 10% penalty.
3 days late: submitted after Saturday 11:59pm and before Sunday 11:59pm – 15% penalty.
4 days late: submitted after Sunday 11:59pm and before Monday 11:59pm – 20% penalty.
5 days late: submitted after Monday 11:59pm and before Tuesday 11:59pm – 25% penalty.
6 days late: submitted after Tuesday 11:59pm and before Wednesday 11:59pm – 30% penalty.
4
7 days late: submitted after Wednesday 11:59pm and before Thursday 11:59pm – 35% penalty.
The Dropbox closes the Thursday after 11:59pm AEST time.
Support
The Division of Student Life provides a range of Study Support resources and services, available throughout
the academic year, including Writing Mentor and Maths Mentor online drop ins and the SmartThinking 24
hour writing feedback service at this link. If you would prefer some more in depth and tailored support, make
an appointment online with a Language and Learning Adviser.
Referencing and Academic Integrity
Deakin takes academic integrity very seriously. It is important that you (and if a group task, your group)
complete your own work in every assessment task Any material used in this assessment that is not your
original work must be acknowledged as such and appropriately referenced. You can find information about
referencing (and avoiding breaching academic integrity) and other study support resources at the following
website: http://www.deakin.edu.au/students/study-support
Use of Generative Artificial Intelligence (genAI) in this assessment
Deakin welcomes the opportunity to engage with emerging technologies in education and seeks to
build your capability in the ethical and responsible use of current and emergent technology. Deakin
also upholds a commitment to academic integrity and to ensuring high-quality educational outcomes
that prepare you for an AI-driven future.
The use of genAI for pre-task planning purposes is appropriate in this assessment task.
To support your learning in this assessment task, it is recommended that you limit genAl use to
planning, idea development, brainstorming, exploring and locating initial sources. Your final
submission should be your own work and show how you have developed and refined these ideas and
what you have learnt in this unit.
It is important that you take responsibility for your final submission, including:
Evaluating the accuracy and quality of any genAI generated material.
Acknowledging how you used genAI tools in this assessment to ensure you are making
informed decisions about your learning, demonstrating learning you have gained in the unit,
and acting with integrity.
Please use the Acknowledgement statements to guide how you acknowledge the use of genAI in this
assessment.
Your rights and responsibilities as a student
As a student you have both rights and responsibilities. Please refer to the document Your rights and
responsibilities as a student in the Unit Guide & Information section in the Content area in the CloudDeakin
