AI in business: technologies and solutions

2025/2026

Academic Year

ENG

Instruction in English

6

ECTS credits

Delivered at:

Department of Business Informatics

Course type:

Elective course

When:

2 year, 1, 2 module

Instructors

Jin, Seungmin

Tekic, Zeljko

Course Syllabus

Abstract

AI is the key technology of today. It is already used to solve (previously) unsolvable scientific challenges, help in creating new drugs faster, make autonomous cars possible, but also facilitate online shopping, and help us in finding what we like on entertainment platforms. As an emerging general-purpose technology, AI is expected to transform every industry, just as the Internet did 20 years or electricity 100 years 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. The overall goal of this course is to help developing AI-ready business leaders, people who will understand how the technology works, where it can be applied and for what type of problems, what are current limitations, challenges and dilemmas, and what to expect in the next 5-10 years. This course provides business students with a comprehensive understanding of AI fundamentals, with a specific focus on practical applications in corporate information systems like ERP, CRM, SCM, etc. It includes both theoretical knowledge and hands-on experience using KNIME, a no-code/low-code AI tool.

Learning Objectives

• This course prepares future business leaders to become AI-ready professionals by combining hands-on prototyping with strategic and managerial perspectives. Students learn not only how to experiment with leading large language models (LLMs) and low-code tools to rapidly build AI-driven applications, but also how to frame the right problems, negotiate data access, and evaluate business impact. The course is structured around team-based, iterative development. In the first half, students acquire technical and conceptual foundations through prompt engineering, rapid prototyping, and guided experimentation with best-in-class LLMs. Midway, each team consolidates its ideas through a pitch, selecting one project to develop further. In the second half, students deepen their projects by addressing data governance, forecasting, competitive strategy, ethics, and organizational adoption. Alongside technical experimentation, students engage with real-world business cases and the transformative role of AI in shaping corporate strategy and innovation. Students analyze how organizations are adopting AI to create new value propositions, enhance decision-making, automate processes, and adapt to emerging challenges. By the end, students will have created and presented a working AI business prototype supported by a strategic business case. Emphasis throughout is on continuous experimentation, ethical awareness, and business value metrics, ensuring participants can translate AI capabilities into actionable, responsible, and strategically sound solutions for business growth and competitiveness.

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 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 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
• Effectively communicate AI initiatives and strategies in oral and in written form
• Use LLMs for developing low-code business applications

Course Contents

• Introduction to AI Fundamentals – Principles and Concepts
• AI in Corporate Information Systems
• Ethical, Legal and Sustainability Considerations and AI
• Generative AI and prompt engineering
• Hands on AI: using leading LLMs for developing low-code business applications
• Global AI revolution and its impact on economy
• AI in Business - implications for strategy, organisation and staffing

Assessment Elements

• Capstone Project
  Capstone Project (40 points) The capstone is your chance to bring everything together by developing an AI-driven solution that addresses a real business problem. Each team will design a working prototype using leading LLMs or low-code tools and support it with a strategic business case. Your project will be evaluated on both the technical side (e.g., functionality, usability, creativity of the prototype) and the business side (e.g., problem framing, business model, and ethical considerations). Examples: 1. A chatbot that improves customer service in retail, supported by an analysis of cost savings and customer satisfaction. 2. A forecasting tool for sales or demand, paired with a business case on how it enhances decision-making and competitiveness. Capstone Project Timeline The Capstone Project runs throughout the semester, with continuous checkpoints to help teams refine their ideas and receive feedback. Key milestones are: Week 1 – Students begin collecting business problems they are interested in exploring. Week 2 – Teams should be formed, ideally around shared problem interests. Week 4 – Each student briefly presents (in group discussion) several identified problems (not solutions) for consideration. Week 6 (Midterm Pitch) – Each team presents 3–4 problem–solution ideas in front of all students and instructors. This is an opportunity to test ideas, get feedback, and compare alternatives. Based on feedback and team preferences, each group selects one final problem to develop into their Capstone Project. Weeks 7–11 – Teams work on their projects in depth: exploring the chosen problem, experimenting with AI-based prototypes, and crafting business models to capture value. Instructors will provide regular feedback during this period. Week 12 – Teams deliver their final Capstone Project presentation and demo (defense), assessed on both technical functionality and business/strategic insight.
• Midterm Pitch
  Midterm Pitch (Week 6, 10 points) The Midterm Pitch is your first major checkpoint in developing the Capstone Project. By this stage, your team will have explored multiple possible problems and drafted initial ideas for how AI might address them. What you will do: Each team will present 3–4 problem–solution ideas in a short pitch session (about 3 minutes per idea + Q&A). The aim is not to show polished prototypes, but to demonstrate clear problem framing, feasibility thinking, and creative options. Purpose: The pitch is designed to help you compare alternatives, receive structured feedback from instructors and peers, and then select one final problem to develop for the Capstone Project. Assessment (10 points): Your pitch will be evaluated on: Comprehensiveness, coherence, and quality of analysis (40%) – clarity of problem framing, initial feasibility, and ethical awareness. Collection and presentation of evidence to justify arguments (40%) – quality of reasoning, use of examples, early experiments where relevant. Technical and procedural excellence of presentation (20%) – clarity, timing, slide quality, and teamwork in delivery. Reminder: You will select your final Capstone Project idea THE LATEST in Week 7, based on feedback from this session.
• Tests & Quizzes
  Tests & Quizzes (15 points) Over the semester you will complete two or three short quizzes, announced in advance through the LMS. These will be scheduled around Weeks 2, 9, and possibly 11. Quizzes will combine conceptual multiple-choice questions with a few short applied answers, testing your understanding of both technical and strategic course content. Topics may include problem discovery, value mapping, AI strategy basics, competition logics, governance, and ethics. The goal is to help you stay engaged with the key ideas needed to connect hands-on experimentation with broader business insights.
• Hands-On Exercises
  Hands-On Exercises (15 points total) Throughout the course you will complete a series of short technical exercises, tied to hands-on sessions. These may include tasks such as: Prompt engineering (Week 4) Prototype sketch (Week 5) Forecasting model (Week 8) Multimodal demo (Week 10) Other small assignments linked to class activities Exercises are assessed as direct checks of functionality and process — does your solution work, and did you document how you got there? The goal is to reinforce practical skills step by step, so that by the end of the course you can confidently connect technical experimentation with business applications.
• In-Class Engagement
  In-Class Engagement (20 points) This component is not just about attendance, but about active engagement and participation in class. Points are awarded for: Timely and relevant comments during lectures and discussions Linking contributions to previous lectures, assigned readings (including videos), personal experience, or other courses Opinions supported by evidence and thoughtful reasoning Responding meaningfully to questions from the lecturer or peers On each lecture day, you can earn up to 2 points on average based on your engagement. Assessment is done by the course instructors during and immediately after class. There are 10 lecture days where engagement is scored, for a maximum of 10 points.

Interim Assessment

• 2025/2026 2nd module
  0.4 * Capstone Project + 0.15 * Hands-On Exercises + 0.2 * In-Class Engagement + 0.1 * Midterm Pitch + 0.15 * Tests & Quizzes

Bibliography

Recommended Core Bibliography

• 9781000790597 - Stefan Popenici - Artificial Intelligence and Learning Futures - 2023 - Routledge - https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=3408947 - nlebk - 3408947
• 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
• Generative artificial intelligence : what everyone needs to know®, Kaplan, J., 2024
• 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

Authors

• Komarov Mikhail Mikhailovich
• Tekich Zhelko