Module Specification

AI in Business: Strategies and Implementation

London school of INNOVATION

Module Specification

AI in Business: Strategies and Implementation: Practical insights for driving innovation across industries.



This course will immerse you in the practical applications of AI across business functions and industries. You'll learn about foundational AI technologies such as machine learning, natural language processing, and computer vision and gain a solid understanding of the AI landscape. You will then explore their practical use cases in everyday business functions, with plenty of examples across various industries.

The course also examines AI's limitations and ethical considerations, from data privacy issues and bias to the socio-economic impact of mass-scale automation. These discussions aim to give you a balanced view of AI's role in society, preparing you to think critically about technology implementation in your career.

You will leave this course inspired by AI's potential, aware of its constraints, and ready to use AI technology responsibly and effectively.


Code Number of Credits ECTS Credits Framework HECoS code
MA71 15 7 FHEQ - L7 artificial intelligence (100359)

Learning outcomes

Code Attributes developed Outcomes
LO1 Knowledge and Understanding Critical awareness of use cases in AI and emerging insights in the field.
LO2 Intellectual Skills Critically analyse current scholarship and research in the field of AI.
LO3 Technical/Practical Skills Appraise AI-driven projects to maximise the value of AI.
LO4 Technical/Practical Skills Evaluate AI techniques for interpreting and creating insights and knowledge.
LO5 Professional/Transferable Skills Communicate AI concepts effectively to both technical and non-technical audiences.
LO6 Professional/Transferable Skills Develop professional growth plans for advancing AI knowledge and skills.

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
P LO6
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
P LO6

Student workload

Activity Total hours
Introductory lecture 1.50
Concept learning (knowledge graph) 18.00
AI formative assessment 9.00
Case Study Review 9.00
AI Roleplay 13.50
Workshop/Lab Sessions 13.50
Independent reading, exploration and practice 61.50
Summative assessment 24.00
150.00

Content Structure

Week Chapter Name Chapter Description
Week 1 Understanding AI This chapter provides an overview of AI technologies, such as algorithms, deep learning, and neural networks. It will introduce you to the opportunities that AI provides and also AI roadmaps. Practical examples will demonstrate how AI can contribute value.
Week 2 AI in Manufacturing, Business Process Design, and Automation Explore the transformative impact of AI in manufacturing and business process automation. Learn about smart factories, supply chain optimisation, and lean manufacturing. This chapter will also cover how AI tools can automate tasks, improve accuracy, and streamline workflows in business settings. Case studies will highlight enterprise resource planning.
Week 3 AI-Driven Consumer Interaction Today This chapter examines how AI enhances consumer interactions by personalising customer experiences, analysing consumer behaviour, and optimising service delivery. Discover the roles of chatbots, personalisation, and predictive analytics in e-commerce. Gain insights into strategies for leveraging AI to boost user engagement.
Week 4 Computer Vision Understand the capabilities and applications of computer vision. This chapter covers image recognition, object detection, and facial recognition. Learn about the use of computer vision in healthcare and security sectors.
Week 5 Natural Language Processing and Speech Recognition Natural Language Processing (NLP) and speech recognition, including through prompt engineering, are revolutionising human-machine interactions. This chapter explores the fundamentals of NLP, including text analysis, sentiment analysis, and transformers, as well as the applications of speech recognition technologies in different contexts. Understand how these AI applications create more intuitive and responsive user experiences.
Week 6 Robotics and Cognitive Automation Investigate the advancements in robotics and cognitive automation. This chapter covers the use of AI-powered robots. Learn about intelligent agents, human-robot interaction (HRI), and digital twins. Explore the impact of autonomous vehicles and drones, as well as swarm robotics.
Week 7 AI, Healthcare and Finance Case Studies Examine detailed case studies showcasing how AI is transforming healthcare and finance. Discover how AI is used in predictive analytics for patient care, telemedicine, and wearable health technology. In finance, explore applications of AI in portfolio management, risk assessment, and high-frequency trading. Understand the challenges and opportunities of implementing AI in these critical sectors.
Week 8 Enterprise AI This chapter focuses on the integration of AI technologies within enterprise settings. Learn how AI can enhance core business functions such as customer feedback analysis, human resources, and supply chain optimisation. Discover strategies for deploying AI to support decision-making, improve process efficiency, and drive innovation within corporate environments.
Week 9 AI-Infused Digital Strategies Learn how to develop and implement AI-infused digital strategies that align with business goals, regulations, and ethics. This chapter covers AI as a Service (AIaaS), business intelligence (BI), and how to integrate AI into business models for scalability. Understand innovative trends such as blockchain-AI convergence, and how to craft strategies leveraging AI for growth and sustainability.
Week 10 Futureproofing Industries Understand the importance of staying ahead of technological trends to prepare for the future. This chapter explores future AI trends, including generative AI, and their potential impact on various industries. Learn how to anticipate and adapt to changes in AI technology, ensuring business resilience and competitiveness.

Module References

Type Description
Book Akerkar, Rajendra. Artificial intelligence for business. Springer, 2019.
Book Castrounis, Alex. AI for people and business: A framework for better human experiences and business success. O'Reilly Media, 2019.
Book Corea, Francesco. Applied artificial intelligence: Where AI can be used in business. Vol. 1. Springer International Publishing, 2019.
Book Krunic, Veljko. Succeeding with AI: How to make AI work for your business. Simon and Schuster, 2020.

Methods of teaching/learning


Introductory lecture (1.50 hours)

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.


Concept learning (knowledge graph) (18.00 hours)

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.


AI formative assessment (9.00 hours)

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.


Case Study Review (9.00 hours)

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.


AI Roleplay (13.50 hours)

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.


Workshop/Lab Sessions (13.50 hours)

The 9 weekly sessions following the introduction (weeks 2 to 10) will be dedicated to teaching the contents of the module during interactive workshops. These sessions will complement the theory with practice, experience or analysis. Their purpose is to advance the student's cognition from 'knowledge' to 'understand' and 'apply'.

Depending on the nature of the content, challenges and learning activities will be pre-designed to apply flipped learning, and may include hands-on project work, group discussions or debates, roleplay, simulation, case study or other presentation, 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. There will be an opportunity also for Q&A in every session.


Independent reading, exploration and practice (61.50 hours)

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.

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: Practical insights for driving innovation across industries. (MA71)