2024/2025
AI in business: technologies and solutions
Type:
Mago-Lego
Delivered by:
Department of Business Informatics
When:
1, 2 module
Open to:
students of one campus
Instructors:
Zeljko Tekic
Language:
English
ECTS credits:
6
Contact hours:
48
Course Syllabus
Abstract
Artificial Intelligence (AI) is the key technology of today. It is already used to solve previously unsolvable scientific challenges, speed up creation of new drugs, move autonomous cars, but also facilitate online shopping, and help in finding our favorite content on entertainment platforms. As an emerging general-purpose technology, AI is expected to transform every industry, just as the Internet did two decades or electricity a century ago, and create an estimated GDP growth of more than $10 trillion during the next decade. The recent surge in using ChatGPT for a wide range of applications is just another visible example of how AI may shape the (near) future. The importance of understanding AI and the set of skills necessary for working with AI and succeeding in the era of AI cannot be overstated. Thus, industries, governments, researchers and society at large are highly interested in learning how to live, work and benefit from AI.
Learning Objectives
- The primary objective of this course is to cultivate AI-ready business leaders who possess a deep understanding of how the technology works, its diverse applications, potential limitations, challenges, and forthcoming developments over the next 5-10 years. In addition to equipping students of business informatics (BI) with a robust grasp of AI fundamentals and business applications, the course emphasizes the practical implementation of AI solutions within corporate information systems such as ERP, CRM, and SCM. This approach aims to leverage and enhance the existing knowledge and skill set of BI students. Structured into two segments, the course begins with an emphasis on the technical aspects of AI fundamentals, encompassing theoretical concepts and hands-on experience utilizing no-code/low-code AI tools. Subsequently, the focus shifts to exploring real-world business cases, as well as the evolving changes and challenges posed by the increasing adoption of AI within organizations.
Expected Learning Outcomes
- Productively work in groups.
- Understand AI principles and concepts (how AI works from math / tech side)
- Understand what AI can and cannot do (tech limitations at the moment and in the next 5-10 years);
- Understand critical role of data for AI
- Understands ethical and sustainability challenges AI brings;
- Analyze application of AI and its impact on corporate information systems such as ERP, CRM, and SCM
- Analyze impact of AI on business operations, strategy, and competitiveness;
- Analyze risks and challenges associated with implementation of AI in organizations;
- Synthetize and propose AI-related strategies which will enhance impact and minimize risks.
- Use no-code/low-code AI tools, specifically KNIME, in practical tasks
- Use a number of real-life cases and scenarios to illustrate value of AI for businesses;
- Understands a landscape of AI "production" and usage in Russia;
- Develop effective prompts for generative AI tools such as ChatGPT and GigaChat
- Reconcile tools and methods learned in the context of business informatics education with the need to analyze and synthesize AI strategies, and specifically in the context of corporate information systems such as ERP, CRM, and SCM
- Effectively communicate AI initiatives and strategies in oral and in written form
Course Contents
- Introduction to AI Fundamentals – Principles and Concepts
- AI in Corporate Information Systems
- Hands-on AI with KNIME: Case Studies and Demonstrations
- Ethical, Legal and Sustainability Considerations and AI
- Global AI revolution and its impact on economy
- AI in Business - implications for strategy, organisation and staffing
- Generative AI and prompt engineering
Assessment Elements
- In-class engagement, exercises and homeworkThis course requires a high level of motivation and active class participation. This is not simply a lecture attendance, it is ENGAGEMENT and PARTICIPATION in the lectures, with deep preparation, timely and relevant comments and discussion, comments linked to the previous lectures, personal experience or other courses; opinion based on evidence, thinking, responding to the lecturer’s questions.
- Project Work 1In each module, students will have to complete one project work. Both projects are team based.
- ExamThe exam is taken in a written format based on a selection of open-ended questions offlime or online with proctoring (start exam platform).
- Project Work 2
Interim Assessment
- 2024/2025 2nd module0.4 * Exam + 0.1 * In-class engagement, exercises and homework + 0.15 * In-class engagement, exercises and homework + 0.2 * Project Work 1 + 0.15 * Project Work 2
Bibliography
Recommended Core Bibliography
- Alexis Bogroff, & Dominique Guégan. (2019). Artificial Intelligence, Data, Ethics: An Holistic Approach for Risks and Regulation. Documents de Travail Du Centre d’Economie de La Sorbonne.
- Bernard Marr, & Matt Ward. (2019). Artificial Intelligence in Practice : How 50 Successful Companies Used AI and Machine Learning to Solve Problems. Wiley.
- Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0
- Haenlein, M., & Kaplan, A. (2019). A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925
- Osondu, O. (2021). A First Course in Artificial Intelligence. Bentham Science Publishers Ltd.
Recommended Additional Bibliography
- All-in on AI : how smart companies win big with artificial intelligence, Davenport, T. H., 2023