Module Specification

AI in Business: Strategies and Implementation

Module Specification

AI in Business: Strategies and Implementation: Transform Innovation with Strategic AI Insight



Artificial intelligence stands at the forefront of modern business transformation, shaping how companies innovate and compete. While AI promises significant advancements, it also challenges traditional business practices, demanding a nuanced understanding of when and how to integrate these technologies. This module provides a deep dive into the strategic facets of AI, setting the stage for a comprehensive exploration of its role in contemporary business.

This module covers the fundamental and nuanced aspects of AI as applied to business strategies. You will explore the operational autonomy of AI, areas requiring human intervention, and the ethical considerations that arise. Through diverse case studies, you'll analyse real-world scenarios—addressing workflow integration, risk management, and adaptability challenges in various sectors. These insights help pinpoint where AI excels, supports, or needs careful oversight in business applications.

Armed with strategic insight and practical understanding, you'll be prepared to harness AI’s full potential responsibly. As you discover how AI can enhance, rather than merely replace, human capabilities, you'll gain the foresight needed to anticipate emerging trends and challenges. This module equips you with the skills to critically evaluate AI’s strengths, navigate ethical complexities, and implement technologies that align with business goals while enhancing collaborative possibilities.


Mode(s) of Study Code CATS Credits ECTS Credits Framework HECoS code
Full-time Blended Learning
Part-time Blended Learning
MA71 15 7 FHEQ - L7 artificial intelligence (100359)

Prerequisites and Co-requisites

None

Learning Outcomes

Code Attributes developed Outcomes
LO1 Knowledge and Understanding Demonstrate a comprehensive understanding of AI's transformative impact across diverse business sectors.
LO2 Intellectual Skills Analyse the strengths and limitations of AI applications, distinguishing areas of autonomous operation, human oversight, and constraints.
LO3 Technical/Practical Skills Design innovative models of human-AI collaboration, enhancing human capabilities while considering technological constraints.
LO4 Technical/Practical Skills Develop strategic plans for AI integration, addressing workflow integration, risk management, and organisational adaptability.
LO5 Professional/Transferable Skills Lead ethical decision-making processes related to AI, adhering to professional standards and fostering responsible innovation.

Content Structure

Week Topic
Week 1 Introductory lecture
Introduces the module, outlining its relevance to the field and connections to other topics. It provides an overview of the content structure, key references, and assessment details.
Week 2 AI Fundamentals
Understand the essential principles and capabilities of AI, focusing on how it enables business innovation. Gain clarity on why AI matters, its value in decision-making, and its transformative potential. Discover the core concepts that underpin AI technology, its key types, and the varied applications in business today, setting a foundation for the deeper exploration of AI strategies in the following chapters.
Week 3 AI Limitations and Boundaries
Delve into the current limitations of AI, understanding where and why it struggles to perform certain tasks. Learn about the constraints of AI technology and the boundaries that shape its applications. This chapter focuses on recognising AI’s shortcomings and the situations that still require human intervention, providing a realistic view of what AI can and cannot achieve in business contexts.
Week 4 AI in Strategic Decision-Making
Explore how AI informs business strategy, helping organisations predict trends, analyse data, and make strategic choices. Focus on why AI-driven insights are pivotal for competitive advantage and learn how AI aids in enhancing decision quality across departments. This chapter reveals the strategic role of AI in transforming traditional approaches to decision-making and highlights the potential business impacts.
Week 5 AI Deployment Challenges
Examine the practical hurdles businesses face in AI implementation, including organisational, technical, and resource-based challenges. Discover why these obstacles arise and the significance of addressing them to ensure effective AI deployment. This chapter outlines the key issues that can hinder success, from data quality and integration to staff adaptability, and sets the stage for realistic deployment expectations.
Week 6 AI and Human Collaboration
Learn about the collaborative dynamics between AI and human workers and how these partnerships enhance productivity and innovation. This chapter explains why blending AI with human expertise can be more effective than AI alone, highlighting the strengths each brings to tasks. Understand the evolving nature of human-AI interaction and how to structure roles to maximise efficiency and satisfaction.
Week 7 Organisational Transformation
Explore the broader impact of AI on organisational structure, culture, and workflow. Understand why AI often requires rethinking traditional roles and processes and how companies can evolve to support AI-driven change. This chapter covers the shift in organisational mindsets and operational frameworks necessary for AI to add real value, highlighting the human elements essential for transformation.
Week 8 Risk Management in AI
Investigate the risks inherent in AI implementation, from technological to ethical. Learn why identifying and managing these risks is crucial for sustainable AI deployment. This chapter discusses strategies to mitigate issues related to data privacy, security, and potential bias, enabling businesses to build trust and ensure AI systems align with organisational values and compliance standards.
Week 9 Ethical AI and Social Responsibility
Understand the ethical dimensions of AI, focusing on why responsible AI usage matters to society and business reputation. Cover key ethical challenges such as algorithmic bias, data privacy, and the socio-economic effects of automation. This chapter encourages a critical mindset towards AI ethics, equipping students to navigate these issues responsibly and align AI practices with societal expectations.
Week 10 Future AI Trends
Explore emerging AI trends and their potential to shape the business landscape. Discover why staying informed on AI’s evolution is essential for anticipating market shifts and innovation opportunities. This chapter examines technological advancements, regulatory changes, and societal impacts to help students prepare for AI’s future role in business strategy, enhancing their foresight and adaptability.

References/Indicative Reading List

Importance ISBN Description
Core Textbook 9781800563469 Chojecki, Przemek. Artificial Intelligence Business: How you can profit from AI. Packt Publishing, 2020
Core Textbook 9781948198998 Munoz, J. Mark, and Al Naqvi. Business strategy in the artificial intelligence economy. Business Expert Press, 2018.
Supplementary Reading 9781800438811 Syam, Niladri and Kaul, Rajeeve. Machine Learning and Artificial Intelligence in Marketing and Sales: Essential Reference for Practitioners and Data Scientists. Emerald Publishing, 2021
Supplementary Reading 9781032028866 Unhelkar, Bhuvan, and Tad Gonsalves. Artificial intelligence for business optimization: research and applications. CRC Press, 2021.
Supplementary Reading 9781119651802 Anderson, Jason L., and Jeffrey L. Coveyduc. Artificial intelligence for business: A roadmap for getting started with AI. John Wiley & Sons, 2020.
Supplementary Reading 9783319974354 Akerkar, Rajendra. Artificial intelligence for business. Springer, 2019.
Supplementary Reading 9783319772516 Corea, Francesco. Applied artificial intelligence: Where AI can be used in business. Vol. 1. Springer International Publishing, 2019.
Supplementary Reading 9781617296932 Krunic, Veljko. Succeeding with AI: How to make AI work for your business. Simon and Schuster, 2020.
Supplementary Reading 9781492036579 Castrounis, Alex. AI for people and business: A framework for better human experiences and business success. O'Reilly Media, 2019.

Student Workload

The methods of teaching and learning for this module are based on the School's Professional 15 teaching system, consisting of the following activities.

Activity Total hours
Introductory lecture

This is the first weekly session, dedicated to providing a comprehensive introduction to the module. The module leader will present an overview of the subject, elucidating its importance within various digital engineering professions and its interrelation with other modules. Students will need no preparation ahead of attending this session.

The module leader will provide a structured breakdown of the content to be covered in the subsequent 9 sessions. Students will also receive an outline of the essential reference materials, alongside suggestions for supplementary reading. The format and criteria for the summative assessment will be delineated, followed by a dedicated period for questions and answers.

A recording of the session will be available to facilitate async engagement for any other student who missed the class, also offering an opportunity to review the content again.

1.50
Concept learning (knowledge graph)

Our institution's approach to teaching is primarily based on flipped learning. Ahead of each weekly session (Workshop/Lab), students will be required to study the essential concepts that are used in the coming session so they are familiar with the theories and ideas related to that session. The study material will be in the form of written content, illustrations, pre-recorded lectures and tutorials, and other forms of content provided through the AGS.

This content is self-navigated by the students, accommodating different learning styles and schedules, allowing students to watch or listen to them at their own pace and review them as needed.

18.00
AI formative assessment

Once each concept of the theory is studied, students will be prompted to engage in formative assessment with instant AI feedback. They include multiple-choice questions, socratic questions and answers, written questions, role-play and other AI-assisted practice scenarios.

The purpose of this automated formative assessment is to provide students with immediate feedback on their understanding of module material and highlight any areas that need support or further study. They are also used to track student progress, boost motivation and promote accountability.

9.00
Case Study Review

In this learning activity, students explore recent real-world case studies relevant to their course topic. The case studies will have been selected and curated by the module leader to represent up-to-date examples. They guide students through key details, contextual factors, and outcomes. This approach enhances students' understanding of current industry trends, challenges, and solutions, preparing them for real-world scenarios they may encounter in their future careers.

The learning experienced will be augmented by AI (virtual private tutor) allowing the students to critically engage with the content and discuss the case studies.

9.00
AI Roleplay

AI Roleplay is an innovative educational approach that leverages artificial intelligence to create immersive, interactive learning experiences for university students. In this activity, students are presented with a professional challenge or scenario relevant to their course. They then engage in a simulated interaction with one or more AI-powered characters, each programmed to embody specific roles, personalities, and expertise.

These AI characters respond dynamically to the student's inputs, creating a realistic and adaptive roleplay environment. Students can practice their communication skills, decision-making, problem-solving, and other professional competencies in a safe, low-stakes setting. After the session, the AI system provides detailed feedback on the student's performance, highlighting strengths and areas for improvement. This personalised guidance helps students refine their skills and gain confidence in handling real-world professional situations.

13.50
Workshop/Lab Sessions

Those studying in the blended learning mode will attend these 9 weekly classes (in person or remotely) during weeks 2 to 10. These sessions will complement the theory already studied during the preceding week (in our flipped-learning model), with discussions, analysis, practice or experience . They will be interactive and participatory, rather than one-way lectures. There will also be an opportunity for Q&A in every session. Depending on the nature of the content, challenges and learning activities will be pre-designed to apply flipped learning. They may include hands-on project work, group discussions or debates, roleplay, simulation, case studies, presentations, and other learning activities and opportunities. These workshops present an opportunity to apply critical thinking and problem-solving skills. They also encourage collaboration and foster a sense of community among students.

13.50
Independent reading, exploration and practice

This activity challenges students to engage with the reference material and independently explore and analyse academic literature related to the course topic. Students are expected to select relevant sources, practice critical reading skills, and where applicable technical skills, and synthesise information from multiple references. This is an opportunity to enhance research abilities, critical thinking, and self-directed learning skills while broadening and deepening subject knowledge.

61.50
Summative assessment

Summative assessments are used to evaluate student learning at the end of a module. These assessments can take many forms, including exams, papers, or presentations. Instructors can use summative assessments to measure whether students have achieved the learning outcomes for the module and provide them with a sense of their overall progress. Summative assessments can also be used to evaluate the effectiveness of the teaching methods used in the module.

24.00
150.00

Assessment Patterns

Weighting Format Outcomes assessed
50% Simulation and Role Playing Assessment
This assessment requires students to engage in AI-assisted simulations or role-playing scenarios that mirror real-world professional situations. It evaluates their practical knowledge, decision-making, and adaptability.

Students are given a detailed brief outlining a dynamic, evolving problem involving various issues like business, legal, professional, and ethical considerations. They must interpret the situation, consult relevant sources, and present a solution based on their knowledge from the module.

At the start of the module, students attend a workshop on effective participation in simulations aligned with the learning outcomes. Throughout the term, they practice through formative simulations, receiving feedback from AI, peers, staff, and their module leader.
K LO1
I LO2
T LO3
T LO4
P LO5
50% Invigilated Exam
This is a time-limited and closed-book exam with a mix of multiple-choice and analytical written questions that students undertake during the summative assessment period as scheduled under the School’s remote invigilation conditions to ensure quality and academic integrity.

The exam enables the students to demonstrate their successful attainment of the module learning outcomes, primarily related to knowledge and understanding, and secondarily related to Professional/Transferable Skills.

The analytical written questions will consist of problem questions representing issues and dilemmas students are likely to encounter in professional life and students have to synthesise and apply what they have learnt on the module in order to produce sound and reasoned judgements with respect to the problem.

To enable the students to practice and prepare, various formative assessment activies, including quizzes and a AI-augmented assignments and mock exams are built into the module. Additionally, throughout the course, students will regularly receive feedback on their knowledge and assignments from AI as well as peers and staff to indicate how to improve future work and how to give constructive feedback to others.
K LO1
I LO2
P LO5

Programmes this module appears on

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change.
Module Spec: AI in Business: Strategies and Implementation: Transform Innovation with Strategic AI Insight (MA71)