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MIS41490

Academic Year 2025/2026

AI & Business Analytics (MIS41490)

Subject:
Management Information Systems
College:
Business
School:
Business
Level:
4 (Masters)
Credits:
7.5
Module Coordinator:
Professor Michael O'Neill
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

The broad objective of this module is to give participants the knowledge, tools, and insights to demystify AI and analytics, and to start to explore their potential in organizations. We will explore what are the ingredients required to successfully leverage AI & Business Analytics. Along the journey we will explore concepts around Trustworthy AI and how these have provided foundations for the EU AI Act. We will examine limitations and risks of the underlying AI and analytics technologies and how they might be adopted responsibly. Real-world examples will be presented to demonstrate the capabilities of, the limitations of, and opportunities provided by these technologies. Importantly, participants will explore what it takes for success with AI and analytics and the critical role of information.

About this Module

Learning Outcomes:

• Understand the language of artificial intelligence, machine learning and analytics;
• Appreciate the fundamental concepts of analytics and AI;
• Recognise and analyse the capabilities and limitations of AI and analytics and explore their potential strategic and operational impact;
• Appreciate the potential opportunities through exploring a variety of application areas;
• Be aware of the benefits of a strategy-driven versus a data-driven approach;
• Appreciate the challenges organisations face in realising expected outcomes as they look to adopt AI and analytics.
• Adopt evidence-based decision making, and recognise cognitive biases that can impact on decision making;
• Identify potential application areas of AI and analytics in your organisation;
• Understand the capabilities organisations require to successfully leverage artificial intelligence and analytics;
• Understand ethics, and the regulatory frameworks governing the use of data and algorithms.

Student Effort Hours:
Student Effort Type Hours
Lectures

24
Specified Learning Activities

80
Autonomous Student Learning

80
Total

184

Approaches to Teaching and Learning:
The teaching style adopted will be participant-centered. There is a wealth of experience in the room and to capitalize on this, engagement will be highly interactive to ensure that participants learn from each other. The role of the faculty member is to provide a safe environment for sharing and to provide a structured approach to learning. The module is delivered through the use of materials (cases, readings, videos, podcasts, exercises, breakout discussions) and the orchestration of the conversation around these materials. Given the highly interactive nature of the session, it is essential that all required pre-module preparation is completed in advance of the classroom sessions.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (In-person): End of term exam End of trimester
Duration:
2 hr(s)
 Graded No
100
 No

Carry forward of passed components
Yes
 

Resit In Terminal Exam
Summer Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 

Not yet recorded

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 10:00 - 11:50
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