| Unit name | Interdisciplinary Perspectives on AI for Business |
|---|---|
| Unit code | MGRCM0065 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| 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?
Artificial intelligence is fundamentally reshaping value creation across sectors such as healthcare, finance, retail, and manufacturing. However, the effectiveness of AI deployment depends not only on the technology itself but also on how it is governed, managed, and aligned with industry-specific constraints. This unit focuses on the interdisciplinary integration and governance differences of AI across industries, equipping students with the ability to design AI strategies that account for regulatory complexity, public sensitivity, and commercial priorities. Emphasising the practical application of interdisciplinary knowledge in real-world sectoral contexts, the unit serves as a critical starting point for students aiming to develop comprehensive strategic thinking and pursue cross-sectoral dissertation or project work. The unit will provide the students with the interdisciplinary skills to connect their learning in the Business School and other Schools across the University.
How does this unit fit into your programme of study
This unit acts as the theoretical backbone of the programme, guiding students to construct a comprehensive understanding of AI-business interactions from interdisciplinary perspectives. It focuses on sectoral differences in AI adoption, governance, regulation, and strategic alignment, drawing on insights from technology, management, and the social sciences. This unit complements, but is distinct from, AI in Business Practice. Whereas AI in Business Practice in TB2 emphasises the application of AI in organisational settings, Interdisciplinary Perspectives on AI for Business in TB1 provides the conceptual and sector-specific frameworks that underpin those applications. Together, these units ensure students have both the theoretical grounding and applied skills necessary for effective AI deployment in diverse business environments.
An overview of content
The unit explores how AI generates value and drives systemic transformation across industries, drawing on key business and management theories and applied learning. Key topics include comparative adoption pathways of AI across sectors, the logic of industry-specific strategy design, regulatory and ethical adaptation frameworks, evolving governance models, and the cultural differences in social acceptance of AI. Through cross-industry case studies, collaborative simulations, and strategic planning workshops, you will gain a deep understanding of how AI operates differently in finance, healthcare, retail, manufacturing, and beyond. The final project involves producing an AI strategy and governance proposal tailored to a specific sector, laying a structured foundation for comparative thinking in future research or professional contexts.
How will students, personally, be different as a result of the unit
You will evolve from having narrow technical or managerial perspectives to becoming integrative AI strategists capable of combining industry context, technical insight, and social judgement. You will learn to compare sectoral needs, constraints, and governance frameworks and translate interdisciplinary knowledge into actionable business strategies. This skillset will directly support your ability to contribute to cross-industry consulting, policy research, and innovation leadership. This will be underpinned by theories of business and management.
Learning Outcomes
By the end of the unit, you will be able to:
This unit adopts an interdisciplinary, student-centred and practice-oriented teaching approach, combining conceptual learning with applied analysis. Through a blend of interactive lectures, comparative case studies, cross-sectoral debates, and strategic planning workshops, you will explore how AI is deployed and governed across diverse industry contexts. Emphasis is placed on collaborative learning, with students working in interdisciplinary teams to tackle real-world challenges and generate policy-oriented solutions. Formative activities provide structured opportunities to test ideas and receive feedback. These experiences prepare you for summative assessments that require both individual analytical depth and collective strategic thinking.
Tasks which help you learn and prepare you for summative tasks (formative):
To support your engagement with cross-industry analysis and interdisciplinary strategy building, the unit incorporates structured formative activities:
Tasks which count towards your unit mark (summative):
You will submit an individual analysis report critically evaluating an AI strategy within a selected industry. The analysis must address sector-specific opportunities, constraints, ethical considerations, and strategic alignment across technical and organisational dimensions. Covers ILOs 1, 2, 3, 4.
You will work in teams with interdisciplinary backgrounds to produce a professional policy report offering actionable recommendations for responsible AI adoption in a given sector. A 10-minute group presentation will accompany the written submission. To ensure fairness, each student's final grade for the group component may be adjusted based on a peer evaluation process. This peer assessment will involve team members confidentially rating each individual's contribution using clear criteria, enabling tutors to allocate marks equitably according to each student's demonstrated effort and input. Covers ILOs 1, 2, 3, 4, 5.
When assessment does not go to plan
If you are unable to complete or pass any assessment, a reassessment will be offered.
In all cases, reassessments will test the same intended learning outcomes as the original assessment. Reassessment deadlines will be set in line with university policy and support will be available from academic advisors.
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. MGRCM0065).
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.
