| Unit name | AI in Business Practice |
|---|---|
| Unit code | MGRCM0062 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| 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?
As AI technologies continue to mature, organisations must grapple with complex decision-making related to when and how to integrate them into core operations. This unit aims to develop strategically minded AI managers who can align technological innovation with organisational goals, drive transformation, and ensure ethical and compliant implementation. Unlike entrepreneurship-focused modules, this unit concentrates on AI adoption in large or traditional organisations, addressing practical challenges across key business functions—including strategy, marketing, finance, operations, and human resources—as well as governance structures, organisational culture, data infrastructure, and stakeholder resistance. You will learn how to lead responsible organisational change and translate AI from a conceptual promise into operational reality.
How does this unit fit into your programme of study
This unit serves as a critical bridge between technological strategy and organisational management. It is particularly suited for students aiming to explore digital transformation, AI deployment strategies, or organisational governance in their dissertations or projects. The unit provides a systematic approach to navigating complex organisational structures, managing stakeholders, and mitigating ethical risks—equipping students to lead projects from “proof of concept” to full “enterprise integration” and delivering measurable business value. This unit includes research training to support the Applied Research Project or Dissertation, exploring methodologies in business and management.
An overview of content
The course focuses on the real-world deployment of AI in mid-to-large enterprises, covering themes such as AI adoption pathways, organisational restructuring, data governance, ethical review processes, and managing internal resistance. You will engage with real business cases to analyse cross-functional challenges, compliance issues, and employee retraining strategies related to AI deployment. Teaching methods include case discussions, role-play exercises, and focused seminars, equipping you to develop practical, organisation-wide AI development and execution plans. The final output centres on designing a strategic and risk-aware framework for organisational AI deployment, serving as a structured reference for dissertation research.
How will students, personally, be different as a result of the unit
You will gain the ability to identify both opportunities and barriers to AI adoption within complex organisations. You will develop a strategic mindset to align AI implementation with long-term organisational goals, strengthen your communication and leadership skills in cross-departmental contexts, and acquire the capability to design trustworthy, compliant, and scalable AI deployment strategies. These competencies will directly support your capacity to lead AI transformation initiatives in research and professional practice.
Learning Outcomes
By the end of this unit, you will be able to:
This unit adopts an applied, strategic approach to learning that mirrors the complexity of AI practices in mid-to-large organisations. Through interactive seminars, real-world case studies, and role-play simulations, you will explore how organisational structures, stakeholder dynamics, and regulatory concerns shape AI implementation. Group-based activities emphasise collaboration across functional perspectives, while individual tasks foster independent critical analysis. Formative exercises—including project proposal drafts and in-class scenario responses—allow you to test ideas and receive feedback in a low-stakes environment. Summative coursework combines a group-based organisational AI strategy report and an individual case analysis, encouraging you to bridge theory and practice. This structure equips you with the communication, leadership, and governance skills needed to lead responsible AI transformation initiatives within complex organisational settings.
Tasks which help you learn and prepare you for summative tasks (formative):
To support the development of strategic thinking and applied analysis, this unit includes two formative learning activities:
Tasks which count towards your unit mark (summative):
This unit adopts a diversified assessment structure to reflect both individual understanding and collaborative application of AI adoption strategies in real-world business settings. The summative assessments include:
When assessment does not go to plan
If you are unable to complete or pass a summative assessment, you will be offered a re-assessment opportunity that aligns with the original learning outcomes. Re-assessment arrangements are as follows:
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. MGRCM0062).
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.
