| Unit name | Artificial Intelligence for Business |
|---|---|
| Unit code | MGRC30014 |
| Credit points | 20 |
| Level of study | H/6 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Dr. Bernardi |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
Machine Learning for Data-driven Business Decision-making (MGRC20007) or Advanced Quantitative Analysis in Management (EFIM20039) or Management Science (EFIM20005) or Applied Quantitative Research Methods (EFIM20010) |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | School of Management - Business School |
| Faculty | Faculty of Social Sciences and Law |
Why is this unit important
With the rise of Artificial Intelligence (AI) applications in business, many companies are increasingly looking for digitally savvy graduates with skills in AI. This unit provides you with skills in machine and deep learning methodologies (e.g. Artificial Neural Networks), employed for several business AI applications, such as recommendation systems, demand prediction, financial analysis, and supply chain management. Additionally, the unit provides you with the necessary critical understanding of how businesses can leverage machine and deep learning methodologies and, more broadly, AI to gain a competitive advantage, as well as the broader ethical and societal issues arising from their use.
How does this unit fit into your programme of study
The unit complements what you have learned in business analytics, marketing, international business, human resource management, digital innovation, economics, and sustainability across BSc Management degrees. It teaches how AI technologies and methods solve business problems, support automation, and improve business processes and decision making in organisations. You will learn how AI can drive strategic advantage and innovation, along with the impact of AI on the workforce and consumers. The unit equips you with AI skills to boost your employability and prepares you to become a digitally savvy manager who cares about the ethical and responsible use of AI.
An overview of content
The unit teaches machine and deep learning algorithms (e.g. Artificial Neural Networks) and how they are employed in various business applications (e.g. demand prediction, financial analysis, and supply chain management). In addition, the unit will provide you with an understanding of how machine deep learning methodologies are employed to solve business problems. You will learn about various areas of application of AI technologies and methods in a managerial context, including their strategic and ethics implications.
How will students, personally, be different as a result of the unit
At the end of this unit, you will develop practical skills and a deeper understanding of machine and deep learning and their role in driving business improvement, decision-making, competitiveness, and innovation. You will gain a critical perspective on integrating AI to innovate products and services while evaluating the broader benefits and risks of AI in business. This unit will give you a realistic view of AI’s capabilities and potential for businesses, along with a heightened awareness of its ethical implications for workers and consumers.
Learning Outcomes
On completion of this unit, you will be able to:
The unit will be taught in one hour lecture and 2-hour seminars. Students will practice exercises on machine and deep learning algorithms to solve a business problem in computer rooms. During seminars on the business and ethical implications of AI, students will discuss thought-provoking academic papers and practice-oriented cases about the use of AI in organizations. Students will be required to prepare some tasks and do some reading before each session. Students will be able to access and revise the unit contents on Blackboard.
Tasks which help you learn and prepare you for summative tasks (formative):
Seminars will be highly interactive sessions where students will have the opportunity to discuss the problems they are trying to solve and get verbal feedback from lecturers/tutors as well as peer-to-peer feedback. Students will practice and be given feedback on computer lab exercises on machine and deep learning algorithms (e.g. ANN) in the seminars. In the seminars about the business and ethical implications of AI, feedback will be based on student-led discussions of academic and practice-oriented reading that students will have to prepare before each session. Students will have to discuss ethical and societal issues about AI. The tasks that students will have to perform in class will be similar to what is expected from them in the assessment. In this way, students can receive formative feedback that will help their preparation for the assessment.
Tasks which count towards your unit mark (summative):
Group project proposal (500 words, 20%): students will outline a business problem, the AI solution they propose to solve it, and provide technical details of how their solution will work and be designed, how they plan to address its potential ethical implications, and the main tasks their team will execute to realise it (All ILOs).
Group project presentation (10 min., 30%): students will present their proposed AI solution and evaluate its effectiveness in addressing a business problem, along with its ethical implications (All ILOs).
Individual report (1,500 words, 50%): students will draw on theories taught in the course to evaluate and suggest improvements to the AI solution from their group project, provide a reasoned analysis of key ethical and societal issues that may arise from this solution, and recommend ways to mitigate them (All ILOs).
When assessment does not go to plan
Re-assessment of units within the final year of undergraduate modular programmes is not usually permitted.
When exceptional circumstances apply, failed components will be reassessed on a like-for-like basis.
Students who fail the Group project proposal will be reassessed by a 200-word individual reflective piece on their contribution to the original group project proposal (20%) (ALL ILOs).
Students who fail the 10-minute Group project Presentation will be reassessed by a 2-minute individual video reflective piece on their contribution to the original group presentation (30%) (All ILOs).
Students who fail the 1,500-word Individual report will be reassessed by submitting a new individual report that evaluates different limitations and ethical implications and offers alternative suggestions and recommendations to address these limitations and ethical implications, compared to the original report (50%) (All ILOs).
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. MGRC30014).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
