2026/2027
Искусственный интеллект в бизнесе
Статус:
Майнор
Кто читает:
Международный институт экономики и финансов
Где читается:
Международный институт экономики и финансов
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
5
Контактные часы:
52
Course Syllabus
Abstract
This course introduces undergraduate students to the strategic, technological, and economic foundations of artificial intelligence in business. The course focuses on how modern AI systems—including machine learning, large language models, and agent-based solutions—are designed, deployed, and managed to create measurable business value. Students study the full AI value chain, from data and infrastructure to models, processes, governance, and impact measurement, with an emphasis on real-world implementation rather than programming. Through case studies, workshops, and a team-based project, students learn to design AI strategies, evaluate AI initiatives, manage portfolios and programs, address ethical and security risks, and assess the broader economic implications of AI adoption.
This course is designed for students who are interested in learning about the application of artificial intelligence in a business context. It is ideal for those who are pursuing careers in management, finance, analytics, or any field where data-driven decision making is important. Prior knowledge of AI is not required, but a basic understanding of business concepts is recommended.
Course pre-requisites:
• This course DOES NOT require any technical and engineering knowledge of AI as mainly the business/applied side will be discussed, but basic programming or ML skills will be beneficial
• Understanding of the basic business/management principles is required
• Practical understanding of the basic IT concepts (databases, operation systems, etc.) is required
• Access to any kind of LLM service (ChatGPT, GigaChat, Alice AI, Gemini, etc.) is required
Learning Objectives
- Develop an understanding of how artificial intelligence can be applied to solve business problems and make data-driven decisions.
Expected Learning Outcomes
- Define the basic concepts of artificial intelligence and how they can be applied in a business context
- Analyze business problems and determine if AI is a viable solution.
- Confidently use AI instruments (such as LLMs) to efficiently solve business problems.
- Develop and implement AI strategies that align with business objectives.
- Interpret and communicate AI results to stakeholders in a clear and understandable way.
- Outline the latest technological trends and current areas of research.
- Evaluate the current and potential impact of AI on the global economy
- Design and prototype simple agent-based AI solutions for business tasks using no-code / low-code tools
- Assess ethical, security, and regulatory risks of AI deployment and propose appropriate mitigation and governance measures
Course Contents
- Intro to AI for Business and AI Strategy
- Enabler 1: Data
- Enabler 2: Infrastructure
- Enabler 3: Models
- Practical AI Agent Workshop
- Enabler 4: People
- Enabler 5: Processes
- Enabler 6: Ethics & Security
- AI Agent Demo [EVALUATION]
- AI Governance & Program Management
- AI Strategy Workshop
- Impact on the economy
- AI Policy Debates
- Group Project Presentation [EVALUATION]
Assessment Elements
- AI Agent DemoThe team result is converted into individual grades using: (1) a peer-assessment coefficient, collected confidentially from all team members after the defence; and (2) an individual Q&A component — during the defence, each student answers questions on the solution individually. The resulting individual grade may deviate from the team grade by up to 20%.
- Group ProjectAI Strategy Workshop (section 11) serves as an intermediate, non-graded checkpoint: teams present project progress and receive structured feedback. The team result is converted into individual grades using: (1) a peer-assessment coefficient, collected confidentially from all team members after the defence; and (2) an individual Q&A component — during the defence, each student answers questions on the solution individually. The resulting individual grade may deviate from the team grade by up to 20%.
- Home AssignmentsActivity, incl. AI Policy Debates
- Final ExamIn order to get the passing grade the student must sit all parts of the examination
Interim Assessment
- 2026/2027 4th module0.15 * AI Agent Demo + 0.5 * Final Exam + 0.1 * Home Assignments + 0.25 * Group Project
