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SBUS46280

Academic Year 2024/2025

How to Leverage AI & Analytics (SBUS46280)

Subject:
Business
College:
Business
School:
Business
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Professor Joe Peppard
Trimester:
Autumn&Spring&Summer(separate)
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 explore how organizations can effectively and repeatedly leverage AI and analytics for success. We will explore what are the ingredients required to successfully leverage AI & Business Analytics. We will discuss and get hands on with how we might approach augmenting existing business processes with processes for AI & business analytics including alignment to strategy. We will explore how to apply analytics and AI to existing business processes. We will also get hands on and discuss the importance of communication through visual analytics and storytelling with data. In addition, we continue to give participants the knowledge, tools, and insights to demystify AI and analytics, and explore their potential in organizations.

About this Module

Learning Outcomes:

The broad learning outcomes from this module include:
•Be aware of the benefits of a strategy-driven versus a data-driven approach;
•Align opportunities presented by AI and analytics to strategic objectives;
•Apply frameworks to aid analysis and decision-making in order to understand the opportunities and implications of artificial intelligence and analytics technologies;
•Appreciate the capabilities organisations require to successfully leverage artificial intelligence and analytics;
•Appreciate the challenges organisations face in realising expected outcomes as they look to adopt AI and analytics;
•Appreciate the potential opportunities through a variety of application areas;
•Recognise and analyse the capabilities and limitations of AI and analytics and explore their potential strategic and operational impact;
•Understand the language of artificial intelligence, machine learning and analytics;
•Identify potential application areas of AI and analytics in your organization;
•Enhance competence in communicating about analytics and artificial intelligence to ensure that the organisation maximizes the opportunity from data and technology and realises real business value;
•Leverage techniques for more effective storytelling and communication with data.

Student Effort Hours:
Student Effort Type Hours
Specified Learning Activities

40
Autonomous Student Learning

60
Lectures

16
Total

116

Approaches to Teaching and Learning:
The module has a detailed study guide. Students are required to complete the module pre-reading or pre-work prior to attending the seminar sessions. The sessions themselves will be a combination of lectures, group discussion, in-class presentations, case study discussions and classroom exercises. A heavy emphasis in the seminars is on teasing out the implications of theory for practical application in a workplace context.

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
Assignment(Including Essay): Post-module assignment Week 15 Graded No
80
 Yes
Participation in Learning Activities: Grade for participation in class (as per rubrics provided in the Study Guide). Week 7 Graded No
20
 No

Carry forward of passed components
Yes
 

Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Written feedback to be provided within 20 days of the assignment deadline.

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