MSc Business Analytics: Operational Research and Risk Analysis

This course unit provides a rigorous examination of the intersection between cutting-edge AI technologies and organisational decision-making. Students will explore the technical and conceptual architecture of Decision Intelligence and AI-enabled decision systems, tracing the evolution of advanced analytics into the frontiers of Generative AI, Multimodal systems, and Agentic Autonomous AI. The curriculum is designed to address the complexities of deploying AI in uncertain, risky environments, balancing technical mastery with critical analysis of failure modes and misalignment. By integrating global regulatory frameworks with the technical pillars of Explainability, Transparency, and Accountability, the unit equips students to lead the responsible implementation of AI, ensuring that ethical governance is treated as a core component of modern business infrastructure rather than an afterthought.

In a global market increasingly defined by rapid technological shifts, this course unit offers a masterclass for the next generation of data-driven leaders. Move beyond traditional analytics to master the Large Language Models and Autonomous Agentic systems that are currently reshaping the corporate landscape. This course unit doesn't not just teach you how to build AI—it teaches you how to lead with it. You will gain a significant competitive advantage by learning to navigate the AI regulation and ethical governance, transforming technical risks into strategic business opportunities. This course unit provides the visionary mindset and the specialised technical depth required to build the future of responsible, AI-driven business.
 

BMAN71652 Programme Req: BMAN71652 is only available as an elective to students on MSc Business Analytics, MSc Data Science (Business and Management pathway) and MEng (Hons) Computer Science

BMAN71652 Programme Req: BMAN71652 is only available as an elective to students on MSc Business Analytics, MSc Data Science (Business and Management pathway) and MSc Digital Transformation

This unit examines state-of-the-art AI and advanced analytics techniques that shape modern decision-making in organisations, alongside the ethical, regulatory, and governance challenges arising from their deployment. Students will gain a deep understanding of advanced machine learning, generative AI, and autonomous AI systems, while developing the ability to critically assess issues of fairness, transparency, accountability, and responsible use of AI in business contexts.

AI systems and Decision Intelligence
Advanced Analytics as the Backbone of AI
AI Under Uncertainty and Risk
Generative AI and Large Language Models
Multimodal and Simulation-based AI
Agentic and Autonomous AI Systems
Failure Modes, Misalignment, and AI Risk
Ethical Foundations of AI and Analytics
Explainability, Transparency, and Accountability
AI Governance, Regulation, and Responsible Deploymen

Lecture: 30 hours
Guided independent study: 120 hours

Critically evaluate the theoretical foundations of AI-enabled decision making and explain how advanced analytics serves as the backbone for modern AI systems.

Develop an understanding of the architectural differences and emergent capabilities of AI technologies such as Generative AI, Multimodal AI, and Agentic Systems, including their specific applications in business automation.

Explain key concepts in ethical data governance, including legal and regulatory compliance, and the ethical implications of AI in decision-making.

Evaluate the transformative impact of AI trends on organisational decision-making under conditions of risk and uncertainty.

Critically evaluate the ethical, legal and societal implications of emerging AI systems.

Design and evaluate AI-enabled decision systems using business data for real-world managerial contexts.

Design AI governance strategies that ensure fairness, transparency, ethical practice, and legal compliance.

Communicate complex AI and data topics to non-technical audiences, highlighting ethical considerations, to support responsible decision-making.

Written assignment 70%
Oral assessment/presentation    30%

Written feedback, provided within the assessment and feedback policy

 

Optional reading list:
• The Analytics Edge, by Bertsimas, D., Allison, K.O. and Pulleyblank, W.R., 2016, Belmont, MA, USA: Dynamic Ideas LLC.
• Artificial Intelligence: A Modern Approach, by Russell, S.J. and Norvig, P., 2021, Global Edition 4e, Pearson.
• An introduction to statistical learning with applications in R/Python (ISLR/ISLP), by Gareth James, Daniela Witten, Trevor Hastie, and Rob Tibshirani, 2013, Springer.
• The Ethics of Artificial Intelligence: Principles, Challenges, and Opportunities, by Luciano Floridi, 2023, Oxford Academic.
• The Oxford handbook of ethics of AI by Dubber, Markus Dirk, Frank Pasquale, and Sunit Das, eds., 2020, Oxford University Press https://academic-oup-com.manchester.idm.oclc.org/edited-volume/34287