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Artificial Intelligence in Economy

Course developer

Course abstract

The aim of the course is to provide students with knowledge and skills to analyse the significance of artificial intelligence in economic processes and its application possibilities for process optimization, as well as for the creation of new products. The course explores the use of AI tools available on the market and the utilization of open-access models for developing new products. Additionally, it discusses AI applications in accordance with existing regulations and best practice standards, responding to the latest trends in the digital world. Finally, the course examines future trends in the development of artificial intelligence and related technologies, outlining new ideas for the realization of potential business models.

Learning outcomes and their assessment

Knowledge:
• Understanding the principles of artificial intelligence and its applications in business and economic process analysis (test 1),
• Awareness of the existing regulatory framework for the use of AI tools - test 2, independent assignment 2).
Skills:
• Ability to practically apply various AI models to improve business processes - independent assignment 1,
• Proficiency in using the most common AI tools for developing new products - independent assignment 2.
Competence:
• Ability to independently select and integrate AI tools into economic processes.
• Utilizing AI capabilities for the creation of new products or enhancing the marketability of existing products - independent assignment 1, independent assignment 2.

Course Content(Calendar)

Full-Time On-Site Studies (24 h):
1. Fundamentals of Artificial Intelligence – Key concepts, history, and development cycles, the journey from machine learning to large language models. The most important roles in the formation of artificial intelligence. The most common applications of artificial intelligence today, enhanced information retrieval. (2 hours)
2. Analysis of Artificial Intelligence Methods and Their Applications in Business – Analysis of machine learning application scenarios in comparison with the integration possibilities of generative artificial intelligence. (2 hours)
3. Analysis of AI Tool Offerings and Their Potential Applications – Review and comparison of the most popular tools, practical experimentation in content creation. Analysis of open-access AI tools and their further use in product creation. (4 hours)
4. Query Formulation Strategies – The most popular ways to create queries for artificial intelligence. Query engineering, popular strategies for query formulation. Enhancing query context with local company knowledge. (2 hours)
5. Developing an AI Implementation Strategy – Strategy formulation for AI adoption in an organization, product lifecycle planning, and monitoring. (2 hours)
6. Artificial Intelligence Agent Systems – The role of agents in solving and analyzing business problems. Types of intelligent agents and their applications in process automation. (2 hours)
7. Data and Its Role in AI Utilization for Product Creation – The use of data in business process analysis, data visualization possibilities, AI-based data analysis, and an overview of various examples. (2 hours)
8. AI Applications in E-Commerce and Product Sales Enhancement – Possible ways to personalize products according to consumer needs, analyze target markets, and optimize market positioning with the help of artificial intelligence. (2 hours)
9. Ethical and Copyright Issues in AI for Economic Processes – The most well-known biases, ethical questions related to AI product usage, and intellectual property protection. (2 hours)
10. AI Regulations and Security Requirements – Existing regulations and the most well-known AI governance standards – the EU AI Act, AI governance standards for institutions, and risk analysis in product implementation processes. (2 hours)
11. Future Trends in AI Development and Applications – Related technologies, market trends, and forecasts. Analysis of current shortcomings and potential development scenarios. (2 hours)

Requirements for awarding credit points

The final assessment is based on the cumulative evaluation throughout the semester:
• Test 1: 25 points
• Test 2: 25 points
• Independent assignment 1: 25 points
• Independent assignment 2: 25 points

In order for the test to be passed, it must be completed with a score of at least 60%.

Description of the organization and tasks of students’ independent work

Independent Assignments:
1. Independent Assignment 1 – AI Integration Plan for a Company
Develop a plan for integrating AI into a company based on given criteria. The work must include the company's data flow structure and an explanation of how AI can be used for automation or process improvements. The steps required to ensure compliance with regulatory requirements must be outlined. Minimum length: 10 pages (main content).
2. Independent Assignment 2 – AI-Driven Marketing Campaign
Design a marketing campaign utilizing AI-powered solutions. The work must include a campaign plan, strategy, visual materials, and distribution approach. Minimum length: 10 pages (main content).

Criteria for Evaluating Learning Outcomes

Tests can only be taken at the specified time.
To receive the final grade, all practical assignments must be completed and assessed as passed.

Compulsory reading

1. A.Bahree (2024). Generative AI in Action. Manning Publications Co., ISBN 9781633436947, 439 p.
2. R. Akerkar (2019). Artificial Intelligence for Business. Springer Cham. ISBN 978-3-319-97436-1, https://doi.org/10.1007/978-3-319-97436-1
3. D. Kumar (2018). Artificial Intelligence for Big Data: Complete guide to automating Big Data solutions using Artificial Intelligence techniques. 1st Edition. Google. PacktPub. ISBN: 9781788476010

Further reading

1. E. Rothman (2018). Artificial Intelligence By Example: Develop machine intelligence from scratch using real artificial intelligence use cases. PactPub. 1st Edition, ISBN: 9781788990028. 490p.
2. D. Fasnacht (2024). Open and Digital Ecosystems. Springer Wiesbaden. ISBN: 978-3-658-45395-4. 273 p., https://doi.org/10.1007/978-3-658-45395-4

Periodicals and other sources

1. IEEE Transactions on Big Data (IEEE): https://www.ieee.org/publications/transactions-on-big-data.html
2. Journal of Cybersecurity (Oxford University Press): https://academic.oup.com/cybersecurity
3. Artificial Intelligence Review. An International Science and Engineering Journal. Springer Nature. https://link.springer.com/journal/10462

Notes

For students of the academic master's study program "Economics"