Course Structure

The MSc AI in Business is a one-year programme structured across 3 semesters.
Students who successfully complete eight taught modules (120 CATS) and an AI in Business Capstone module (60 CATS) in semester 3 are awarded an MSc in AI in Business.

Exit qualifications are also available: students may exit with a Postgraduate Diploma by successfully completing 120 CATS from taught modules or a Postgraduate Certificate by successfully completing 60 CATS from taught modules.

Introduction

The MSc AI in Business is a one-year full-time programme designed to equip students with the knowledge and skills needed to leverage AI technology in business contexts, enabling them to drive innovation, informed decision-making, and responsible AI adoption across various sectors. The programme covers core areas such as AI integration, ethical considerations, data-driven strategy, and innovative business solutions. These areas are crucial for addressing the evolving challenges and opportunities presented by AI in today’s business landscape.

Semester 1 Modules:

AI for Business and Society (15 credits)
OVERVIEW
Artificial intelligence (AI) has already had a substantial impact on business and society, such as data driven business strategies, changes to the nature of work, the development of innovations which shape the behaviour of individuals and society, privacy and surveillance concerns, and recent ethical crises in the use of data. With the fast pace of AI development, these trends seem likely to continue, making it essential to consider the wider implications of AI on business and society. This module will encourage students to engage with these issues, building a deeper understanding of the wider implications of AI, and how students can contribute to responsible development and use of AI in their future career.

Course content may include, but is not limited to:
• The strategic implications of AI innovations for business
• The wider economic and societal consequences of AI
• Changes in the nature of work due to AI
• Ethical use of data
• Surveillance and privacy considerations in the use of data
• Legal consideration in the use of data

LEARNING OUTCOMES:
On successful completion of this module, students will be able to:
1. Critically evaluate the implications of AI for society
2. Critically evaluate the implications of AI for businesses
3. Explain the legal and ethical considerations of AI

SKILLS
This module provides opportunities for students to acquire or enhance the following skills:
Cognitive
• Problem solving
• Logical reasoning
• Independent enquiry
• Critical evaluation and interpretation
• Self-assessment and reflection

Knowledge and Understanding
• Critically assess the transformative role of AI in business strategy and societal impact
• Critically analyse and evaluate the ethical, legal, and cross- cultural implications of AI use

Subject-specific
• Critical evaluation of the wider business, and societal consequences of AI

Transferable
• Synthesise and evaluate information/data from a variety of sources
• The preparation and communication of ideas in written form
• Work both independently and in groups
• Organisation and time management
• Problem solving and critical analysis


Technology for Good (15 credits)
OVERVIEW
The emergence of advanced digital technologies, including Digital Platforms, Accelerated Computing, Artificial Intelligence (AI), Robotics, Fintech, and Virtual/Augmented Reality (VR/AR), offers unprecedented opportunities for innovation and societal progress. These technologies enable organisations to greatly improve efficiency, enhance decision- making, create immersive experiences, and potentially address complex social challenges. Shaped by the values and vision of their creators, advanced digital technologies can drive positive impact across various dimensions, contributing to the achievement of Environmental, Social, and Governance (ESG) goals. However, the adoption of these technologies also disrupts traditional organisational structures, raises ethical issues, and introduces new challenges to environmental and social sustainability.

In this context, the module is positioned at the intersection of technology development, digital strategies, and social innovation and explores how firms can organise and develop strategies that leverage innovative technologies to synergistically create economic, social, and environmental value.

Throughout the module, students will engage with cutting-edge research, real-world case studies, and practical exercises that challenge them to think critically about the role of technology in creating a more sustainable and equitable future.

LEARNING OUTCOMES
On successful completion of this module, students will be able to:
1. Critically evaluate the multifaceted nature of advanced digital technologies, understanding their potential to create and sustain economic, social, and environmental value.
2. Critically examine the positive and negative consequences of deploying advanced digital technologies, considering ethical implications and sustainability concerns.
3. Synthesise insights from current practice and real-world case studies to propose innovative applications of advanced digital technologies for the greater good.

SKILLS
This module provides opportunities for students to acquire or enhance the following skills:
Knowledge and Understanding
• Critically analyse and assess the potential and limitations of digital technologies in driving multifaceted value creation
• Recognise the complex relationship between technological advancement, social impact, and environmental sustainability

Subject-specific
• Analyse and assess digital technology applications for integrated economic, social, and environmental value creation and addressing complex societal challenges
• Assess strategic design thinking in the development and implementation of technology-driven social impact projects

Transferable
• Critical thinking and problem-solving in complex, multidimensional scenarios
• Competence to conduct a significant piece of independent enquiry
• Ethical reasoning towards the application of digital technologies



Design Thinking with AI (15 credits)
OVERVIEW
Organisations across the public, private, and third sectors are increasingly adopting Design Thinking (DT) methodologies to drive innovation, enhance user experience, and address complex challenges. DT is a user-centred, creative problem-solving method that emphasises understanding the needs and experiences of users and stakeholders to develop innovative solutions. It involves a flexible, iterative process that typically includes five key phases: (1) empathising, (2) defining, (3) ideating, (4) prototyping, and (5) testing.

Recent advancements in Artificial Intelligence (AI) technologies, such as machine learning, Natural Language Processing (NLP), computer vision, and generative AI, have created new opportunities to enhance the DT process. AI can analyse vast quantities of data, generate user insights, predict trends, automate tasks, and create innovative solutions through generative design, speeding up prototyping and testing for faster iteration. However, AI technologies also introduce novel challenges for DT including algorithmic bias, privacy and data security concerns, lack of transparency in decision-making, and the potential over-reliance on automation. Navigating these issues requires a balanced, informed approach that considers both the strengths and limitations of AI-assisted design.

Against this backdrop, the module explores the intersection of DT and AI, examining how AI technologies can be integrated into the design process to potentially create more innovative solutions for complex problems. The main aim of the module is to equip students with the knowledge and skills to apply DT methodologies to address challenging problems while learning to leverage AI technologies to improve the DT process in an informed and ethically responsible manner.

LEARNING OUTCOMES
On successful completion of this module, students will be able to:
1. Explain foundational concepts and theories of Design Thinking in the context of AI-driven problem-solving by analysing, articulating, and presenting solutions to real-world challenges.
2. Effectively integrate AI tools into the Design Thinking process
3. Critically assess the strengths and limitations of AI-assisted design
4. Critically discuss the ethical implications of AI-assisted design

SKILLS
This module provides opportunities for students to acquire or enhance the following skills:
Cognitive
• Develop and articulate structured approaches for integrating DT methodologies to address challenging,
user-centred problems in business contexts.
• Critically evaluate and synthesise AI and DT theories to derive insights for addressing business problems, addressing ethical considerations such as algorithmic bias and data privacy within the DT process.

Knowledge and Understanding
• Critically evaluate the application of AI technologies within DT to inform strategic decision-making in challenging business scenarios.

Subject-specific
• Integrate AI-driven models within the DT process to develop innovative, user-centred solutions for challenging business problems

Transferable
• Develop and demonstrate effective organisation and time management skills by planning, executing, and iterating through the DT process in a structured, timely manner.
• Communicate insights from AI-enhanced DT analyses effectively to diverse audiences, including non-specialists.


Generative AI and Business Intelligence (15 credits)
OVERVIEW
Integration of Generative AI (Gen AI) with Business Intelligence (BI) is transforming data-driven decision-making and strategic planning. Gen AI technologies such as Large Language Models (LLMs), Generative Adversarial Networks (GANs), and data synthesis tools empower organisations to generate valuable insights, refine data analysis, and predict trends. Gen AI’s ability to produce synthetic data, simulate scenarios, and automate reporting helps businesses navigate dynamic markets and make proactive adjustments. However, the application of Gen AI in BI requires a careful approach to ethical considerations, including data privacy, transparency, and potential biases in AI outputs.

This module equips students with foundational skills in data analysis, modelling, and prompt engineering, essential for deriving actionable insights from Gen AI. By covering practical applications (e.g., A/B testing/scenario simulations), students will gain the tools to enhance decision-making, communicate insights clearly, and critically evaluate Gen AI’s impact on BI processes. These competencies support students in mastering Gen AI fundamentals, applying analytical techniques, and understanding the transformative potential and responsibilities that Gen AI brings to BI.

LEARNING OUTCOMES
On successful completion of this module, students will be able to:
1. Evaluate Gen AI fundamentals and relevant business applications
2. Assess data analysis and modelling techniques to derive business insights
3. Implement prompt engineering strategies
4. Evaluate the impact of Gen AI on BI processes

SKILLS
This module provides opportunities for students to acquire or enhance the following skills:
Cognitive
• Develop and articulate data-driven strategies using Gen AI and BI tools to address challenging business problems.
• Critically evaluate and synthesise relevant theories and practical applications of Gen AI technologies in BI.

Knowledge and Understanding
• Critically evaluate analytical and prompt engineering techniques to harness Gen AI capabilities in BI solutions for strategic decision-making.
Subject-specific
• Leverage Gen AI tools to develop actionable, data-driven insights and support strategic decision-making.

Transferable
• Critically assess Gen AI and BI techniques to develop sustainable solutions that solve real-world business problems and drive positive impact.
• Communicate Gen AI-driven insights clearly to diverse stakeholders, including non-specialist audiences.

Semester 2 Modules:

Strategy, Change, and Analytics (15 credits)
OVERVIEW
This module provides students with a comprehensive understanding of how strategic decision-making, change management, and data analytics intersect to drive competitive advantage in a rapidly evolving business environment. The module integrates a range of analytical techniques that support organisational strategy development and implementation, drive change, and enable business transformation. It focuses on data-driven insights for formulating and implementing strategies while addressing the challenges of managing change. The module covers structured approaches to evaluating strategic options and projecting scenarios and outcomes. Students will utilise a variety of tools to examine data across key functions such as finance, HR, and operations, and interpret it effectively to better manage organisational change and create value.

This module provides students with the capability to understand data, and how to analyse, and interpret it strategically to drive business change. In addition, students will engage with recent developments in strategic data analytics such as text analytics and data visualisation. Throughout the module, students will engage with cutting-edge research, real-world case studies, and practical exercises that challenge them to think critically about the strategic use of data analytics to drive business change.

LEARNING OUTCOMES
On successful completion of this module, students will be able to:
1. Develop data-driven business strategies.
2. Critically assess appropriate datasets and leverage analytics tools to drive change and generate value.
3. Communicate strategic insights and data-driven analyses effectively to support organisational change.
4. Evaluate the legal, social, and ethical concerns involved in implementing strategy, managing change, and using data analytics.

SKILLS
This module provides opportunities for students to acquire or enhance the following skills:
Knowledge and Understanding
• Scope and deliver data-driven strategic analysis projects in response to the need for business change, creating business opportunity reports suitable for a variety of stakeholders, including senior management and clients.
• Recognise the complex relationship between capturing value through data analytics, and the responsible use of data from an ethical perspective.

Subject-specific
• Critically assess suitable data analytics technologies and business analytics approaches.
• Formulate solutions for driving strategic change and capturing value through data analytics and the use of technologies.

Transferable
• Critical thinking and problem-solving in real-world scenarios.
• Competence in balancing theory-driven enquiry with appropriate available data.
• Ethical reasoning regarding the use of data analytics tools.


AI Entrepreneurship and Consulting (15 credits)
OVERVIEW
The AI Entrepreneurship and Consulting module explores the dynamic intersection of artificial intelligence (AI) and business innovation, equipping students with the expertise to tackle challenges in AI-driven entrepreneurship and consulting. Students will study strategies for developing AI start-ups, from organisational planning to product strategy design, while examining real-world examples of both successful and struggling ventures. Key topics include building a compelling AI business case, navigating funding and scalability issues, and understanding drivers of start-up profitability. Students will gain insights into the entire lifecycle of AI entrepreneurship, from ideation and funding to product- market fit and operational resilience. The module also covers essential consulting frameworks and methodologies, with case-based exercises to develop problem-solving and communication skills that address ethical and practical considerations in the AI landscape. By the end of the module, students will be able to generate and assess AI-driven entrepreneurial ideas, apply consulting frameworks to AI-related business challenges, and effectively convey their consulting recommendations to varied stakeholders, preparing them to drive AI innovation and strategic solutions in a rapidly evolving tech landscape.

LEARNING OUTCOMES
On successful completion of this module, students will be able to:
1. Evaluate how AI can be leveraged to develop new entrepreneurial ideas
2. Examine the challenges involved in translating AI ideas into successful ventures
3. Develop and assess consulting frameworks and methodologies
4. Effectively communicate consulting findings and recommendations

SKILLS
This module provides opportunities for students to acquire or enhance the following skills:
Cognitive
• Develop and articulate strategies leveraging both technological and non-technological solutions for AI-driven business challenges.
• Critically evaluate and synthesise relevant theories and solutions related to AI start-up development and consulting frameworks to derive well-founded business insights.

Knowledge and Understanding
• Critically evaluate AI technologies and innovative approaches to inform strategic decision-making and create effective solutions for entrepreneurial and consulting challenges.

Subject-specific
• Integrate innovative approaches and AI-driven models to develop solutions tailored to the unique challenges faced by AI start-ups and consulting projects.

Transferable
• Demonstrate effective organisation and time management in project-based and case-driven consulting assignments.
• Synthesise diverse sources of information, including academic literature, data sets, and industry reports, to build well-informed business strategies.
• Communicate AI concepts and consulting recommendations clearly and effectively to a range of stakeholders, including non- specialist audiences.


AI Frontiers (15 credits)
OVERVIEW
This module explores the latest advancements in artificial intelligence (AI) and their implications for businesses and society. By focusing on cutting- edge innovations, including developments in machine learning, deep learning, and autonomous systems, this module provides students with the tools to understand how these technologies are reshaping industries and augmenting human capabilities.
The module emphasises the importance of ethical considerations and regulatory frameworks that govern AI advancements. Students will critically evaluate the societal impacts of AI, addressing issues such as data privacy, algorithmic bias, and the need for responsible AI deployment.

In addition, students will engage with horizon scanning approaches to identify and assess emerging AI trends and technologies. Through a blend of theoretical knowledge and practical exercises, including case studies and trend analysis, students will develop the skills to anticipate future developments in AI and understand their potential consequences. By the end of the module, students will be well-equipped to understand AI’s transformative role in human augmentation, assess its impact on various sectors, and navigate the complexities of ethical and regulatory challenges in the rapidly evolving landscape of AI.

LEARNING OUTCOMES
On successful completion of this module, students will be able to:
1. Critically analyse the latest advancements in AI innovations
2. Critically evaluate the ethical and regulatory implications of AI advancements
3. Critically examine AI’s impact on human augmentation and interaction
4. Assess horizon scanning approaches to forecast and assess AI trends

SKILLS
This module provides opportunities for students to acquire or enhance the following skills:
Cognitive
• Develop and propose AI approaches to addressing challenging business and societal problems.
• Conduct independent research and inquiry to identify, assess, and forecast emerging AI trends and their implications in diverse business and societal contexts.
• Critically evaluate and synthesise relevant theories, case studies, and practical solutions to derive insights into AI’s impact on business and human augmentation.

Knowledge and Understanding
• Critically assess the transformative role of AI innovations in business practices and their broader societal implications.
• Critically analyse and evaluate the global and cross-cultural impacts of AI technologies on business operations and societal structures.

Subject-specific
• Leverage AI/ML and deep learning tools to develop actionable insights from data and address challenging business problems.
• Critically evaluate the ethical and responsible management of AI, considering data privacy, algorithmic bias, and regulatory frameworks.

Transferable
• Synthesise information from diverse sources, including academic journals, case studies, and business reports, to inform strategic decision-making.
• Effectively communicate AI concepts and insights from AI-driven analyses to diverse audiences, including non-specialist stakeholders.


OPTIONAL MODULES

Emerging Technologies for Business (15 credits)
OVERVIEW
This module examines a range of breakthrough technologies reshaping the business landscape, including blockchain, metaverse, and quantum computing. Focusing on the practical applications and strategic potential of these technologies, this module equips students with the knowledge to understand how emerging innovations can drive business performance and fuel future growth.
Throughout the module, students will explore how these technologies create new opportunities for business innovation and operational enhancement. They will examine blockchain’s potential to revolutionise transactions, metaverse’s ability to create immersive customer experiences, and quantum computing’s promise for solving computational challenges. The module also emphasises the role of artificial intelligence in the ecosystem of emerging technologies, highlighting its importance in facilitating new applications and strategic insights.

Ethical, legal, and social considerations play a central role, as students critically evaluate the implications of deploying these technologies responsibly. Additionally, students will engage in case studies and workshops that provide hands-on experience, enabling them to assess the feasibility, risks, and benefits of implementing these technologies in various business contexts.
By the end of the module, students will be equipped to critically evaluate emerging technologies, apply practical skills to integrate them, and analyse regulatory frameworks. They will also be able to effectively communicate the strategic business implications of these technologies to diverse stakeholders, preparing them to navigate the complexities of innovation in a rapidly evolving digital landscape.

LEARNING OUTCOMES
On successful completion of this module, students will be able to:
1. Critically evaluate emerging technologies
2. Evaluate practical skills in integrating emerging technologies
3. Analyse global trends and regulatory frameworks
4. Effectively communicate the business implications of emerging technologies to diverse stakeholders

SKILLS
This module provides opportunities for students to acquire or enhance the following skills:
Cognitive
• Develop and articulate strategic approaches for integrating emerging technologies to solve challenging business problems.
• Critically evaluate and synthesise relevant theories and practical solutions associated with emerging technologies and their business applications.

Knowledge and Understanding
• Critically assess the transformative role of emerging technologies in driving business performance, innovation, and their broader societal impacts.
• Critically analyse and evaluate the global and cross-cultural implications of deploying emerging technologies in various business contexts.

Subject-specific
• Critically evaluate the ethical and responsible management of emerging technologies in a business environment, considering their implications for business strategy and stakeholder impact.

Transferable
• Synthesise information from academic research, industry reports, and case studies to inform strategic decisions.
• Communicate the strategic and operational implications of emerging technologies clearly to stakeholders, including non- specialist audiences.


Making Ethical Business Decisions (15 Credits)
OVERVIEW
This module discusses and evaluates the ethical responsibilities of managers and organisations. It will explore themes/issues from international, multi-disciplinary and managerial perspectives, and analyse tensions, conflicts, contradictions and dilemmas via case studies and class discussion. Topics that may be developed include the role of ethical theories, the internal ethical environment (why and how do ethical dilemmas arise and how can they be resolved?); ethical issues regarding employees (including whistleblowing, Codes of Ethics, corporate governance), ethics and consumers, ethics and social responsibility, and ethical issues arising from using artificial intelligence and machine learning.

LEARNING OUTCOMES
On successful completion of this module, students will be able to:
1. Evaluate the ethical responsibilities of organisations.
2. Evaluate the role of ethics in business, in local, national and international contexts.
3. Analyse different ethical perspectives that are used to guide decision making.
4. Evaluate the rights, duties and responsibilities of, and relationships between, organisations and their internal (employee) and external stakeholders (customers, local and wider communities).
5. Discuss and critically review new developments in governance and business ethics.

SKILLS
This module provides opportunities for students to acquire or enhance the following skills:
Cognitive
• Generate creative and innovative solutions to complex managerial challenges in a local and global environment
• Critically assess how managers and firms generally behave and strategise in a complex global business context

Knowledge and Understanding
• Develop a global mindset with cross cultural intelligence

Subject-specific
• Evaluate how managers and firms engage with and influence key stakeholders

Transferable
• Synthesise information from diverse sources, including academic journals, case studies, business reports, and ethical frameworks


Marketing Analytics (15 credits)
OVERVIEW
This module focuses on a new and exciting development in marketing theory and practice. The use of data, ‘big data’, to assist in marketing decision making and accountability continues to grow in importance, particularly in the current age of austerity and resource scarcity. The module takes both a theoretical and practical approach to the use of marketing analytics in practice.

A highlight of the module is the use of SAS or SPSS software to analyse data for marketing-related decision making and evaluative purposes. Students who successfully complete the module will be able to signal to potential employers that they have the theoretical, practical plus industry-standard software skills to compete.

The module is taught in two blocks. Block 1 takes a holistic approach to understanding marketing analytics - the maturity of data analytics in the marketing profession; uncover where, when and how it is being used; and identify whether or not its use results in greater effectiveness, efficiency and performance returns.

LEARNING OUTCOMES
On successful completion of this module, students will be able to:
1. Have a holistic understanding of the use, value or otherwise of marketing analytics – theoretical and practical applications.
2. Understand the antecedents and consequences of building a marketing analytics culture.
3. Appreciate the complexity of capturing and using data to aid marketing-related decision making, including caveats, as appropriate.
4. Have practical knowledge of SAS or SPSS software tools relating to the application of marketing analytics.
5. Have worked through and run appropriate models relating to descriptive and predictive analytics for marketing.

SKILLS
This module provides opportunities for students to acquire or enhance the following skills:
Cognitive
• Problem solving
• Logical reasoning
• Independent enquiry
• Critical evaluation and interpretation
• Self-assessment and reflection

Knowledge and Understanding
• Critically evaluate AI-driven and analytics methodologies within marketing to support strategic decision-making

Subject-specific
• Marketing analytics for managers
• Marketing analytics in practice
• Software and data mining applications

Transferable
• Synthesise and evaluate information/data from a variety of sources
• The preparation and communication of ideas in written form
• Work both independently and in groups
• Organisation and time management
• Problem solving and critical analysis

Semester 3 Modules:

AI in Business Capstone
OVERVIEW
The AI in Business Capstone module is the culmination of the MSc in AI in Business programme, offering students a unique opportunity to integrate their expertise in business strategy with advanced AI and machine learning techniques. This individual capstone allows students to apply skills and knowledge from the taught modules to real-world business scenarios, synthesising insights from business models, AI, machine learning, and data analytics to propose viable and realistic solutions to an organisational or societal challenge. By undertaking this capstone, students demonstrate their ability to independently conduct rigorous research, critically evaluate academic and industry literature, and propose data-driven, AI-powered solutions for strategic value, operational efficiency, and sustainable impact in diverse industry contexts.

This module offers three alternative pathways for the capstone:
• Consultancy-Based Capstone: This pathway involves engaging directly with an organisation to identify opportunities where AI, machine learning, or deep learning can be strategically implemented. Students will explore potential areas for improving processes, developing innovative ML approaches, and gaining a competitive edge through AI solutions.
• Analytical Problem-Solving Capstone: For students selecting this pathway, the focus is on addressing a clearly defined organisational or societal problem through data analytics and AI techniques. Leveraging design thinking and AI-driven insights, students will propose actionable analytics strategies that apply advanced AI or machine learning models to tackle the identified business or societal problem in a sustainable and impactful way.
• Internship-Based Capstone: This pathway is designed for students undertaking an internship, where they will critically analyse the ways in which the host organisation deploys AI/machine learning solutions to enhance its competitive position or resolve challenges. Students will produce a reflective analysis and propose additional AI-powered strategies that could support the organisation’s goals.

A designated pathway coordinator will provide workshops throughout the summer, offering guidance and support tailored to each pathway. Students working on a consultancy or internship capstone will also be paired with a company mentor who will offer insights from the organisation’s perspective. Additionally, internship students will receive mid-point pastoral visits from placement officers to support their experience in alignment with the programme’s practices.

To further support students’ research skills, a series of Research Skills workshops will be delivered early in Semester 3. These sessions will cover essential topics including literature review techniques, data synthesis, and advanced analytical methods to equip students for their capstones, complementing the foundation provided in previous modules.

LEARNING OUTCOMES
On successful completion of this module, students will be able to:
1. Conduct independent, rigorous research in AI, business strategy, and management, producing original insights relevant to academia, industry, and society.
2. Critically integrate AI methodologies and business frameworks to address real-world challenges, demonstrating an understanding of AI’s role in driving innovation, strategic decision-making, and sustainability.
3. Propose and communicate viable, data-driven solutions aligned with organisational and/or societal goals, exhibiting professionalism and ethical responsibility in AI applications.

SKILLS
This module provides opportunities for students to acquire or enhance the following skills:
Cognitive
• Develop and articulate strategic, AI-powered solutions to challenging business or societal problems.
• Conduct rigorous, independent research and inquiry to explore and appraise AI concepts in diverse real-world scenarios.
• Critically evaluate and synthesise relevant theories, industry practices, and data-driven solutions to support well-founded strategic proposals.

Knowledge and Understanding
• Critically assess the transformative role of AI and machine learning in business strategy and operational efficiency, and its broader societal impact.
• Critically evaluate AI, machine learning, and design thinking principles to formulate and propose viable solutions for identified challenges.
• Critically analyse the global and cross-cultural implications of AI- driven strategies in addressing business and societal challenges.

Subject-specific
• Leverage AI and machine learning techniques to develop actionable insights and propose data-driven solutions that enhance strategic value and sustainability.
• Integrate design thinking and AI-driven models to develop innovative AI-driven solutions tailored to specific business or societal challenges.
• Critically evaluate the ethical and responsible management of AI/ML technologies, considering implications for business strategy and stakeholder impact.

Transferable
• Critically evaluate innovative AI and data analytics techniques to sustainably address and drive impactful solutions for real-world problems in consultancy, analytical, or internship-based projects.
• Demonstrate organisation and time management skills throughout the capstone research process.
• Synthesise literature, data, and industry insights to inform and develop robust business proposals.

Teaching Times

For semesters 1 and 2, students will have 4 classes per week, all 3 hours long.

Learning and Teaching

Learning opportunities associated with this course are outlined below:

Teaching Methods

Classes will be delivered as a mix of seminars, lectures, and workshops and they will be interactive and discussion-based blending theory with practical examples. Learning and engagement approaches will involve a range of styles such as interactive case studies, live AI software and tools demonstrations, practical workshops, role plays, groupwork, and scenario discussions.
Semester 3 is structured around independent and applied learning through the capstone where students choose between different pathways. Students are assigned dedicated academic support for these projects and company mentors where relevant.

Assessment

Assessments associated with the course are outlined below:

• There is an emphasis on assessments that are applied and practical in focus where students are encouraged and supported to produce actionable recommendations to address real-world scenarios and problems.
  More specifically, the programme includes a range of assessment methods tailored to develop and evaluate students’ knowledge and skills in applying AI to business challenges. These assessments involve individual and group projects, oral presentations, case study investigations, reports, computer software simulations, portfolios, and projects. Formative assessments provide students with developmental feedback, supporting their progression. The capstone serves as a culminating independent piece of research, allowing students to demonstrate independent research skills, data synthesis, and the integration of AI and business strategy in addressing a real-world organisational or societal challenge.

Modules

Career Prospects

Introduction

The programme provides a wide range of opportunities for graduates to work in roles such as Business Transformation Consultant, Strategic Innovation Advisor, Customer Insights Specialist, and Business Intelligence Analyst, among others. With skills in AI-driven solutions, strategic decision-making, and ethical AI applications, graduates will excel in diverse industries, driving innovation, efficiency, and growth in an AI-powered business environment.

Employment after the Course

The MSc AI in Business programme uniquely combines advanced AI expertise with strategic business applications, preparing graduates for in-demand roles such as Business Transformation Consultant, Customer Insights Specialist, and Strategic Innovation Advisor in a rapidly evolving, AI-driven economy.

Prizes and Awards

Graduate Plus/Future Ready Award for extra-curricular skills

In addition to your degree programme, at Queen's you can have the opportunity to gain wider life, academic and employability skills. For example, placements, voluntary work, clubs, societies, sports and lots more. So not only do you graduate with a degree recognised from a world leading university, you'll have practical national and international experience plus a wider exposure to life overall. We call this Graduate Plus/Future Ready Award. It's what makes studying at Queen's University Belfast special.

Tuition Fees

Northern Ireland (NI) 1	£8,800
Republic of Ireland (ROI) 2	£8,800
England, Scotland or Wales (GB) 1	£9,250
EU Other 3	£25,800 (£6,000 discount, see T&Cs link below)
International	£25,800 (£6,000 discount, see T&Cs link below)

£6,000 Scholarship available for 2025 entry. Click this link to view the Terms and Conditions.

¹EU citizens in the EU Settlement Scheme, with settled status, will be charged the NI or GB tuition fee based on where they are ordinarily resident. Students who are ROI nationals resident in GB will be charged the GB fee.

² EU students who are ROI nationals resident in ROI are eligible for NI tuition fees.

³ EU Other students (excludes Republic of Ireland nationals living in GB, NI or ROI) are charged tuition fees in line with international fees.

All tuition fees quoted relate to a single year of study unless stated otherwise. Tuition fees will be subject to an annual inflationary increase, unless explicitly stated otherwise.

More information on postgraduate tuition fees.

Additional course costs

Students have the option to undertake a consultancy project for their dissertation and are responsible for funding any travel, accommodation and subsistence costs.

Terms and Conditions for Postgraduate applications:

1.1  Due to high demand, there is a deadline for applications. 

1.2  International applicants will be required to pay a deposit to secure their place on the course. The current mandatory tuition fee deposit payment is £1000 International (Non- EU & EU except ROI). 

1.3  This condition of offer is in addition to any academic or English language requirements.

Read the full terms and conditions at the link below:
https://www.qub.ac.uk/Study/postgraduate/tuition-fees/deposit-refunds-policy/

All Students

Depending on the programme of study, there may be extra costs which are not covered by tuition fees, which students will need to consider when planning their studies.

Students can borrow books and access online learning resources from any Queen's library. If students wish to purchase recommended texts, rather than borrow them from the University Library, prices per text can range from £30 to £100. Students should also budget between £30 to £75 per year for photocopying, memory sticks and printing charges.

Students undertaking a period of work placement or study abroad, as either a compulsory or optional part of their programme, should be aware that they will have to fund additional travel and living costs.

If a programme includes a major project or dissertation, there may be costs associated with transport, accommodation and/or materials. The amount will depend on the project chosen. There may also be additional costs for printing and binding.

Students may wish to consider purchasing an electronic device; costs will vary depending on the specification of the model chosen.

There are also additional charges for graduation ceremonies, examination resits and library fines.

How to Apply

Apply using our online Queen's Portal and follow the step-by-step instructions on how to apply.

Apply now

Terms and Conditions

The terms and conditions that apply when you accept an offer of a place at the University on a taught programme of study.
Queen's University Belfast Terms and Conditions.

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