| Unit name | Applied Research Project or Dissertation |
|---|---|
| Unit code | MGRCM0063 |
| Credit points | 60 |
| Level of study | M/7 |
| Teaching block(s) |
Academic Year (weeks 1 - 52) |
| Unit director | Dr. Cheng |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
None |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | University of Bristol Business School |
| Faculty | Faculty of Arts, Law and Social Sciences |
Why is this unit important?
This unit represents the culmination of the AI for Business programme, providing students with the opportunity to apply interdisciplinary knowledge to a high-impact, real-world problem. What makes it particularly valuable—and distinct from similar offerings elsewhere—is its deep integration with industry partners. Students will work directly with external organisations, including businesses, start-ups, and public-sector entities, to explore how AI can be applied, governed, and scaled across different functional and sectoral contexts. Whether the focus is on AI-enhanced operations, ethical governance, or customer-facing innovation, students are supported by both academic supervisors and industry mentors. These collaborations ensure that the final dissertation or applied project is not only academically rigorous but also practically meaningful—delivering outcomes that are aligned with real business needs and societal impact. By doing so, the unit equips students with strategic foresight, technical credibility, and the collaborative skills needed to lead AI transformation initiatives across industries.
How does this unit fit into your programme of study
This capstone unit builds on the theoretical and applied foundations developed across earlier units, enabling students to pursue an in-depth project that bridges academic understanding with organisational relevance. Through the dissertation or applied research project—often in collaboration with an external industry or public-sector partner—students learn to translate AI knowledge into strategic, sector-specific solutions. This final unit empowers students to become reflective practitioners and capable leaders at the frontier of AI deployment across business functions and industries.
An overview of content
This capstone unit enables you to apply the theoretical understanding and applied skills developed in the first two teaching blocks to a dissertation or applied project. Dissertations/Projects synthesise concepts, methods, and perspectives from previous units and address real-world AI challenges in business contexts. A distinctive feature is its strong industry engagement. You may work with external partners—including businesses, start-ups, or public-sector organisations. Academic supervision from the Business School, supported by the Faculty of Science and Engineering, ensures projects are academically rigorous while meeting organisational needs. Through this integration of academic guidance and practical collaboration, you will produce work that bridges technical AI expertise with business value, preparing them to lead AI-driven initiatives in professional settings.
This unit is developed in accordance with the current programme structure for MSc Artificial Intelligence for Business. In line with the Business School’s ongoing curriculum review, it is anticipated that future iterations may align with a cross-programme, generic dissertation or AEP unit to ensure efficiency and consistency across taught postgraduate provision. The specific strengths of this programme—such as industry collaboration and interdisciplinary supervision—will be preserved and integrated as thematic or organisational features within the consolidated AEP unit as appropriate.
How will students, personally, be different as a result of the unit
Through this unit, you will become interdisciplinary AI practitioners, capable of addressing complex challenges at the intersection of technology, business strategy, and societal impact. You will gain direct experience working with non-academic stakeholders—including industry mentors or organisational sponsors—and develop the ability to manage and deliver high-value, real-world projects.
Beyond academic excellence, you will strengthen the critical analysis, stakeholder communication, and ethical reasoning skills. You will also develop the confidence to apply AI technologies across a range of business contexts, and the leadership potential to guide AI-driven transformation initiatives in both commercial and public sector environments.
Learning outcomes
Upon completing this unit, you will be able to:
This unit adopts a student-centred, inquiry-based, and industry-embedded learning approach, designed to prepare you for independent and impactful research that addresses real-world AI and business challenges.
The primary mode of learning is through supervision, combining academic and (where applicable) industry-based support. Each student is assigned a supervisor from the Business School to guide the planning and execution of your dissertation or applied project. These sessions—held individually or in small groups—provide structured feedback on research framing, literature synthesis, methodology, and analysis.
A distinctive feature of this unit is the opportunity to work directly with an external organisation, such as a business enterprise, start-up, or public-sector body. These partners not only offer access to live data and organisational challenges, but may also act as informal co-supervisors, offering domain expertise, commercial insight, and feedback throughout the research process. In some cases, research questions may even be co-developed with these partners to ensure mutual relevance.
To complement this, you may attend interdisciplinary consultations with staff from the Faculty of Science and Engineering, who provide guidance on AI models, technical tools, and research feasibility—ensuring projects are both methodologically sound and technically credible.
A series of workshops (e.g., on research ethics, stakeholder engagement, or dissertation writing) will be offered to support your progress. While not assessed, these formative activities are designed to foster peer exchange, academic development, and industry alignment.
Through this integrated model of academic guidance, real-world collaboration, and interdisciplinary support, you will:
Tasks which help you learn and prepare you for summative tasks (formative):
During this unit, you will benefit from continuous formative support designed to guide the development of your dissertation or applied project. Specifically:
Tasks which count towards your unit mark (summative):
To successfully complete this unit, you are required to submit a dissertation (12,000 words) or applied project report (15,000 words), which accounts for 100% of the final mark. The project may involve research into AI deployment, cloud-based solutions, industry-specific integration strategies, innovation management, or the societal implications of AI adoption. You must clearly define your research objectives or applied problem in relation to a selected topic in AI and business (ILO1), and justify its significance through a comprehensive and critical review of both academic literature and industry resources (ILOs 1 and 2).
You are expected to select and apply appropriate interdisciplinary research methods and conduct data collection or case analysis in accordance with ethical standards and GDPR requirements (ILO3). Findings should be analysed and contextualised within relevant theoretical and practical frameworks, leading to actionable insights or scholarly contributions (ILOs 2 and 4). The final dissertation should demonstrate strong academic writing, structural clarity, and analytical rigour (ILO5), while also reflecting effective time and project management (ILO5).
The report must include, at minimum, the following sections: Introduction, Literature Review, Methodology, Findings, Discussion, and Conclusion.
When assessment does not go to plan
If you fail the unit but is eligible for reassessment, the failed component will be reassessed on a like-for-like basis. This means you will be required to revise and resubmit your dissertation or applied project report, addressing the original assessment criteria and covering all intended learning outcomes (ILOs 1–5). If your initial attempt was a group applied project, then the word count will be 1 / [no. Of students in the group] x 15,000 words; if your initial attempt was an individual dissertation, then you should rework your dissertation with a length of 12,000 words.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. MGRCM0063).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
