Course unit details:
Digital Economy: Platforms, AI and The Business

Course unit fact file
Unit code BMAN31952
Credit rating 20
Unit level Level 6
Teaching period(s) Semester 2
Offered by Alliance Manchester Business School
Available as a free choice unit? No

Overview

This advanced course leverages the latest research to equip you with tools for analysing the economics of AI and digital transformation, preparing you for the evolving digital economy.  

We begin by examining the foundations: exploring the economic theories of network industries and multi-sided platforms. The course delves into platform strategies, design principles, business models, competition dynamics, and advanced pricing algorithms. We will also examine how big data analytics drives success in these ecosystems.

The course then takes a deep dive into the AI revolution, with a particular focus on generative AI (GenAI) and cognitive automation. You will develop a robust theoretical framework to understand AI's potential business applications and critically assess its profound impact on the economy, labour markets, and the future of work. We will examine AI-driven business models and the trajectory of AI start-ups.

We also cover the dynamics of FinTech, payment systems, blockchain applications, the process and challenges of digital transformation for firms, strategic adoption of digital technologies by established players, the economics of automation, and evolving competition policies for a digital world.

Drawing extensively on recent economic and firm data, the course emphasises empirically informed analysis, enabling you to develop critical insights into AI technologies. It is well suited for students aiming for careers in tech strategy, digital entrepreneurship, or policy related to the digital economy, or for those considering starting a business or joining a start-up. 

Pre/co-requisites

Unit title Unit code Requirement type Description
Economic Principles : Microeconomics BMAN10001 Pre-Requisite Compulsory
BMAN10001 is a Pre-Requisite for BMAN31952. Only available to students on: Mgt/Mgt Specialism; IMABS; IM and ITMB/ITMB Specialism.

This course is available to third year students on BSc Management and Management (Specialisms), BSc International Management and BSc ITMB. 

Aims

The unit aims to provide a solid understanding of foundational concepts, theories and technologies that are essential for understanding the digital economy. It offers a thorough review of platform strategies / competition and covers the emerging literature on corporate digital transformation: the process by which traditional firms adopt digital and AI technologies (including generative AI) to adapt to changes in the market.

The course also aims to demonstrate how emerging technologies such as AI, generative AI, machine learning algorithms, and Blockchain, joined with platform technologies, have begun to transform industries such as the financial sector, retail, advertising, healthcare, manufacturing, services, and transportation.

By examining a rich list of cases and data and using recent theories, the course aims to help students to form a systematic view of how digital technologies are likely to shape corporations and industries and change the nature of competition.

Finally, by examining numerous young online firms from different sectors, the course will seek to explain the process of start-up formation and show how to set up an online business.

The course combines recent economic theories with a rich selection of case materials to provide both an analytical and applied understanding of the digital economy / AI, supported by up-to-date data on each topic. 

Learning outcomes

The course uses a rich mix of theory and practice, with a strong emphasis on recent advances in AI and platform technologies, to help students understand the complex changes occurring in almost all industries, from retail and health to finance. In today’s competitive job market, understanding these shifts is increasingly vital. The ability to assess emerging AI capabilities and their business applications, and to formulate effective strategies, offers students a significant advantage. A rich list of recent cases will equip students with a pragmatic approach to running businesses.

Syllabus

Foundations:  

Network Industries   
Platform Firms: Design Development and Growth  
Predictive AI: Theory and Recent Advancements 
Generative AI: Theory and Recent Advancements 
Blockchain: Theory and general applications. 
The Economics of Data – Data as Capital 
AI Agents: Design, Functions and Applications


Applications:

AI, Pricing and Revenue Management  
Reputation, Search and Brands, and Generative AI 
AI & Financial Technologies 
AI & Entrepreneurial Finance  
AI, Human Resources, and Talent Selection 
AI & Healthcare

Final Thoughts:  

AI and Human Experts 
AI and the Firm 
AI and Future Work 
Harms of AI  

Teaching and learning methods

Methods of delivery: Lecture/seminars /computer aided learning

Lecture hours: 30 (3 hours per week over 10 weeks) plus 3 hours overall course revisions (synchronous)

Seminar hours: 8 (1 hour per week) (asynchronous, pre-recorded videos)

6 hours optional revision sessions for students needing help with economics (synchronous)

8 hours optional coding sessions for students aiming to improve their knowledge of machine learning and data analysis.  

Short pre-recorded whiteboard-style videos for students needing help with economics and coding. (asynchronous)

Course activities include weekly blogs discussing recent business cases relevant to the topics covered, in-class case studies and quizzes, and weekly think-pair-share exercises during lectures and tutorials. 

Knowledge and understanding

Analyse the economic principles underpinning network industries and multi-sided platforms, including strategies related to leadership, growth, development, and pricing.

Explore the design of modern online platforms, focusing on essential components including recommender systems, reputation systems, and governance rules.

Explore the foundations, potentials, and limitations of artificial intelligence and cognitive automation technologies.

Critically assess the growth models of platform and AI start-ups to formulate effective strategies for establishing and nurturing successful AI enterprises.

Examine the ways in which cognitive automation technologies reshape firms, the nature of work, and broader economic activity. 
 

Intellectual skills

Develop a sound understanding of the AI revolution, Blockchain, and general-purpose technologies, assessing their potential to reshape future business operations and strategies.

Explain the role of platform, AI, and generative AI technologies in transforming industries, with specific reference to the financial sector, Fintech start-ups and healthcare.

Analyse the process of digital transformation within firms and industries, with a focus on key challenges and strategies. 

Practical skills

Leverage economic data and analysis to guide corporate decision-making.

Design business strategies for growing and running digital marketplaces.

Analyse dominant AI business models and devise new AI business models. 
 

Transferable skills and personal qualities

Leverage generative AI tools for tasks including planning, problem-solving, ideation, and forecasting market trends.

Train machine learning algorithms using established methods to address business challenges.

Use programming languages such as R and Python to analyse data and support business decision-making in firms. 

Assessment methods

12 optional short weekly mock randomised MCQ / selected response tests

Mid-term randomized multiple-choice test (20%)
Randomized multiple-choice test (30%)
Final Individual Economic Project (50%) - Please note that this assessment requires AI use as part of the assessed task (Category D: AI-integrated).

Feedback methods

  • Prior to the exam during weekly seminars and after marks are released.
  • General guidance during revision sessions, individual feedback on outlines, and comments / feedback after marks are released. 

Recommended reading

The course mainly draws on relevant research papers, simplified and summarised to make it accessible for UG students. Typical research papers include:

Brynjolfsson, E. and Hitzig, Z., 2025. AI’s Use of Knowledge in Society. The Economics of Transformative AI. National Bureau of Economic Research, Cambridge, MA.

Manning, B.S., Zhu, K. and Horton, J.J., 2024. Automated social science: Language models as scientist and subjects (No. w32381). National Bureau of Economic Research.

Felin, T. and Holweg, M., 2024. Theory is all you need: AI, human cognition, and decision making. Human Cognition, and Decision Making (February 23, 2024).

Batista, R.M. and Ross, J., 2024. Words that work: Using language to generate hypotheses. Available at SSRN 4926398.

Toner-Rodgers, A., 2024. Artificial intelligence, scientific discovery, and product innovation. arXiv preprint arXiv:2412.17866.

Si, C., Yang, D. and Hashimoto, T., 2024. Can LLMs generate novel research ideas? a large-scale human study with 100+ nlp researchers. arXiv preprint arXiv:2409.04109.

Pham, V.H. and Cunningham, S., 2024. Can Base Chatgpt Be Used for Forecasting Without Additional Optimization?. Available at SSRN 4907279.

Alekseeva, L., Azar, J., Giné, M. and Samila, S., 2024. AI adoption and the demand for managerial expertise. FEB Research Report MSI_2412, pp.1-60.

Luca, M., Kleinberg, J. and Mullainathan, S., 2016. Algorithms need managers, too. Harvard business review, 94(1), p.20.

Acemoglu, D., 2021. Harms of AI (No. w29247). National Bureau of Economic Research.

Shahidi, P., Rusak, G., Manning, B.S., Fradkin, A. and Horton, J.J., 2025. The Coasean Singularity? Demand, Supply, and Market Design with AI Agents (No. w34468). National Bureau of Economic Research. 

Study hours

Scheduled activity hours
Assessment written exam 2
Lectures 33
Seminars 8
Independent study hours
Independent study 157

Teaching staff

Staff member Role
Mohammad Salehnejad Unit coordinator

Additional notes

Pre-requisite units - BMAN 10001

Please note: AI forms part of the assessment method for this course unit.  For more information, please see: AI in Teaching and Learning Policy

Fees and funding

Fees

Tuition fees for home students commencing their studies in September 2027 will be £10,050 per annum. Tuition fees for international students will be £35,300 per annum.

For general information please see the undergraduate finance pages.

You will receive a significant tuition fee discount for the placement year. UK students with a household income of up to £35,000 are also eligible to receive a  cash bursary  worth up to £2,000.

Additional expenses

All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme

Policy on additional costs

All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).

Scholarships/sponsorships

We know that student finance can be complicated. The links below provide further information to help guide you.

Learn more about student finance options for UK students.

Learn more about fees and finance for international students.

As an international student you may be eligible for our Global Futures Scholarships . This is open to students starting their studies in September 2027.

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