Instructional goals
The course develops the ability to identify opportunities for data and artificial intelligence within organizations. It covers the main business functions, the data each generates, and the AI applications each enables, and how AI applications differ across business models, industries and markets.
Prerequisites
None required.
Intended learning outcomes
At the end of the course, students will be able to:
• Identify the main functions of a company and the data each generates;
• Recognise the AI applications available within each function;
• Compare AI applications across business models, industries and markets;
• Analyse a company and map realistic AI opportunities;
• Assess the risks of AI in organizations, including bias and fairness.
Course Contents
• Data and AI applications across the main business functions.
• For each function: what it does, the data it generates, its AI applications.
• AI applications across business models, industries and markets.
• Functions ordered from the most familiar to the most abstract.
• The programme may be adjusted during the course; changes communicated in advance.
Reference Books
• Slides and materials provided by the instructor.
• Distributed during class or via the course platform.
Teaching Methods
• Lectures with real cases.
• Recurring structure: function, data, AI applications.
• Group project developed in practical sessions.
• In-class exercises; guest speakers where available.
Assessment Method
Attending students:
• Group project: 30%.
• Midterm exam (Weeks 2–5): 30%.
• Final exam or second midterm (Weeks 7–10): 40%.
• Exams: multiple-choice and open questions. Participation considered for borderline cases and honours.
Non-attending students:
• Final written exam (100%) on all course material.
Thesis assignment criteria
Thesis assignment criteria:
• Academic performance in the course;
• A short research proposal (1–2 pages) on a data, AI and organizations topic;
• Demonstrated interest;
• Supervisor availability.
Students should contact the instructor in advance.
Week 1
Recognising AI in organizations.
• Organizations: functions, business model, industry, market.
• Where AI applies: data and repeated decisions.
• Course method: function by function; then business models, industries, markets.
Week 2
Marketing & Sales.
• Function: finding and acquiring customers; price vs differentiation.
• Data: views, clicks, purchases, reviews, profiles.
• AI: recommendation, targeting, segmentation, demand prediction.
Week 3
Customer service.
• Function: post-sale support; customer retention.
• Data: conversations, tickets, FAQs, customer history.
• AI: automated answers, request routing, churn prediction.
Week 4
Operations & logistics.
• Function: production and distribution; efficiency, quality; make-or-buy.
• Data: sensors, production and defect logs, inventory, routes, delivery times.
• AI: predictive maintenance, defect detection, demand forecasting, route optimisation.
Week 5
HR & people.
• Function: hiring, organizational structure, people development.
• Data: CVs, applications, performance data.
• AI: candidate screening and matching; bias and fairness (responsible AI).
Week 6
Midterm exam.
Week 7
Finance.
• Function: budgeting, risk, control, fraud.
• Data: transactions, payments, financial records.
• AI: fraud and anomaly detection, forecasting, credit scoring.
Week 8
R&D & innovation.
• Function: new product development; intellectual property.
• Data: patents, papers, technical documents, experiments.
• AI: prior-art search, idea generation and screening.
Week 9
AI across business models.
• Models: product, service, platform, data business.
• Differences in AI use: platforms match two sides; data businesses sell predictions.
Week 10
AI across industries & markets.
• Industries: manufacturing, retail, finance, health.
• Markets and competition; data as competitive advantage.
Week 11
Group project presentations.
• Team presentations; ~20 min + ~10 min Q&A.
Week 12
Group project presentations & wrap-up.
• Remaining presentations; recap; final exam guidance.