MN6078 - Artificial Intelligence and Big Data in Business (2026/27)
| Module specification | Module approved to run in 2026/27 | ||||||||
| Module title | Artificial Intelligence and Big Data in Business | ||||||||
| Module level | Honours (06) | ||||||||
| Credit rating for module | 15 | ||||||||
| School | Guildhall School of Business and Law | ||||||||
| Total study hours | 150 | ||||||||
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| Assessment components |
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| Running in 2026/27(Please note that module timeslots are subject to change) | No instances running in the year |
Module summary
The business world is currently undergoing profound technological change. Digitalisation has reached new heights, and new technologies are helping tackle ever more tasks that are complex. This trend is driven by the availability of large quantities of data – big data (BD) – and by the improved opportunities for using this data through artificial intelligence (AI). The relevance of AI is growing as technology, companies, and consumers interact. First, current technological progress facilitates the extensive and practical use of AI. Second, companies are increasingly relying on data and the value they extract from it to optimise their business models and processes.
Third, consumer behaviour is increasingly shaped by digital applications, which in turn boosts the generation and availability of data. As such this module focuses on the relevance and management of AI in the business context. This module also focuses on how to lead successful AI initiatives by prioritising the right opportunities, building a diverse team, shaping the strategies and strategic experiments and continuously managing business solutions to benefit the organisations.
Prior learning requirements
Standard university requirements for Level 4 entry
Available for Study Abroad? YES.
Syllabus
• Introduction to AI
• AI and BD in organisational context
• AI influence on competitiveness and market
• Decision making and AI
• Problem solving - Understand if a problem can be solved and how do you solve the problem • Challenges of AI - The disruptive power of Digital Transformation
• Developing an enterprise strategy
• Ethics, Law and GDPR
• AI in practice
Balance of independent study and scheduled teaching activity
Delivery of the module will consist of Lecture /Seminars. Students will be encouraged to research AI management issues from a variety of sources in addition to module materials including newspapers, textbooks and on-line sources to identify aspects relevant to their areas of study. Seminars will provide the opportunity for the whole class to come together and to interact with the tutor who will provide the basic AI management theories and present a set of examples and real-world cases.
The assessment strategy for MN6078 uses a two-part integrated assessment consisting of:
1. Group Video Presentation (15 minutes ±10%)
Students collaborate on a live Riipen industry project and deliver a recorded group video presentation. Although the work is completed as a group, each student submits their own copy individually.
2. Individual Reflective Commentary (500 words)
Each student submits a personal reflection on their contribution, learning, and development throughout the project.
Overall Strategy Features
• Combination of collaborative and individual assessment to balance teamwork with personal accountability.
• Authentic, project-based assessment, directly aligned to real-world business consulting tasks via the Riipen partnership.
• Digital delivery (video format) supports development of digital communication and presentation skills required in contemporary workplaces.
• Two-stage assessment requirement ensures students must complete both components to pass — maintaining academic integrity and individual responsibility.
This strategy aligns with constructive alignment principles (Biggs & Tang, 2011):
• The group presentation assesses the application of theory to an industry brief, teamwork, research, analytical skills, and professional communication.
• The individual reflection assesses personal insight, critical evaluation of learning, and evidence of employability skills development.
2. Purpose of the Assessment
The purpose of this assessment is to ensure students achieve the learning outcomes through an authentic, industry-aligned task that develops both academic and professional competencies. Specifically:
a. Application of Theory to Real-World Practice
Working on a live Riipen project enables students to:
• Apply strategic, managerial, and analytical frameworks to solve industry problems.
• Produce evidence-based recommendations for a real client.
This develops problem-solving and consultancy skills valued by employers.
b. Development of Professional Digital Communication Skills
The video format ensures students practise:
• Structuring professional presentations
• Communicating complex ideas clearly
• Using digital tools effectively
• Presenting collaboratively as a team
These are essential graduate attributes in a digitally enabled workplace.
c. Enhancement of Reflective and Critical Thinking Skills
The individual reflection supports:
• Self-evaluation of teamwork, time management, and project coordination
• Identification of strengths, weaknesses, and areas for improvement
• Linking personal learning to theoretical concepts and employability
Reflective practice is a key expectation of postgraduate and professional learners.
d. Fairness, Inclusivity, and Academic Integrity
• Individual submission prevents free-riding and ensures each student is assessed equitably.
• The reflective element verifies the student's ownership of their contribution.
3. Use of Assessment for Learning
The assessment is intentionally designed to promote continuous learning, formative development, and feedback-rich engagement, rather than being solely summative.
a. Continuous Formative Feedback During the Riipen Project
Throughout the module:
• Tutors guide students during project workshops.
• Riipen supervisors may provide industry feedback.
• Peer feedback naturally occurs during group task coordination.
These interactions reinforce learning iteratively — a key principle of AfL (Black & Wiliam, 2009).
b. Scaffolded Weekly Learning
Weekly teaching links directly to components of the assessment:
• Project scoping
• Research and analysis
• Presentation design
• Recommendations formulation
• Reflection and evaluation
This structured progression gradually builds the knowledge and skills needed for successful completion.
c. Opportunities for Practice
Students engage in:
• In-class discussions
• Practical group activities
• Drafting and rehearsing presentations
d. Reflective Element Strengthens Metacognition
The reflection encourages students to:
• Think about how they learned
• Analyse what worked and what did not
• Understand the development of transferable skills
This deepens learning and enhances future performance.
e. Clear Assessment Criteria and Expectations
The assessment brief includes:
• A structured guide
• Clear components
• Submission instructions
• Required academic standards (Harvard referencing)
Learning outcomes
On successful completion of the module, students will be able to:
LO1. Critically evaluate managing success factors of AI
LO2. Critically evaluate the implementation of the strategies for the use of BDAI in the business context.
LO3. Apply the assessment of the opportunities as well as sustainability risks of an increased use of artificial intelligence within the business context.
Bibliography
Textbooks:
Core Text:
Wodecki, A. (2018) Artificial Intelligence in Value Creation: Improving Competitive Advantage. Switzerland: Palgrave McMillan.
Yao, M., Jia, M. and Zhou, A. (2018) Applied Artificial Intelligence: A Handbook For Business Leaders. USA: Topbots.
Other Texts:
Davenport, T. (2018) The AI Advantage: How to Put the Artificial Intelligence Revolution to Work (Management on the Cutting Edge). Cambridge: MIT.
Donald, M. (2019) Leading and Managing Change in the Age of Disruption and Artificial Intelligence. UK: Emerald.
Kampakis, S. (2018) The Decision Maker’s Handbook to Data Science: A guide for non-technical executives, managers and founders. USA: Creative Common.
King, K. (2019) Using Artificial Intelligence in Marketing: How to Harness AI and Maintain the Competitive Edge. UK: Kogan Page.
Marr, B. (2016) Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results. UK: Wiley.
Tegmark, M. (2018) Life 3.0: Being Human in the Age of Artificial Intelligence. UK: Penguine.
Journals:
Journal of Management Information Systems (JMIS)
European Journal of Information Systems (EJIS)
Information and Management
Management Information Systems Quarterly (MIS Quarterly)
Journal of Strategic Information Systems
International Journal of Information Management (IJIM)
Journal of Information Technology (JIT)
Journal of Information Science Behaviour & Information Technology
Journal of Organizational and End User Computing
Information Systems Journal
Journal of Knowledge Management
Knowledge management Research and Practice
Harvard Business Review
Websites: to be provided in class/Weblearn
Electronic Databases: to be provided in class/Weblearn
Social Media Source: to be provided in class/Weblearn