AI in Business, part 2 2400-MIKRO-AIBF-AIB2
Course content description
The course begins with an introduction to Machine Learning for Business, explaining how machine learning uses data to support prediction, classification, recommendation, fraud detection and other business decisions. Students analyze practical cases from different industries and discuss the benefits, limitations, ethical concerns and data-protection risks connected with the use of AI and machine learning.
The second part focuses on AI-supported project management. Students use AI tools to develop a project structure, prepare a Work Breakdown Structure, estimate task duration, identify dependencies, milestones and project risks, and then transfer the project plan into Asana. The course then introduces process management, where students analyze organizational processes and create BPMN models in Cardanit, including activities, decision points, participants, documents and possible process improvements.
The final part connects machine learning, project management and process management with business value creation. Students examine how AI can improve processes, reduce costs and risks, increase efficiency, support better decisions and allow organizations to handle more work with available resources. Through practical cases, students also assess where AI provides real value, where human verification is still required, and what organizational, ethical and data-governance conditions are necessary for responsible AI implementation.
The project ‘Application of Artificial Intelligence in Business and Finance’ (No. BPI/SPI/2024/1/00078) is implemented by the University of Warsaw as part of the Spinaker programme organised by the National Agency for Academic Exchange (within the project ‘Wsparcie tworzenia i realizacji międzynarodowych programów kształcenia’, funded by the European Funds for Social Development 2021–2027).
Course coordinators
Micro-credential certificate
Learning outcomes
Upon completing the course, the participant
in terms of knowledge: Knows and understands the basic principles of artificial intelligence and machine learning and the main forms of their application in business and economic activities; knows and understands how AI systems use data to generate predictions, classifications, recommendations, rankings and anomaly alerts; knows and understands the role of AI in project planning, risk analysis, resource allocation, budget management and managerial decision-making; knows and understands the main mechanisms through which AI creates business value, including process redesign, scale and uncertainty reduction; knows and understands the basic principles of Work Breakdown Structure, project milestones, task dependencies and schedule optimization; knows and understands the basic elements of BPMN process modelling; knows and understands the importance of data quality, organizational processes, employee competences and governance in successful AI implementation; knows and understands the main ethical, legal, privacy and data-protection risks related to AI applications.
in terms of skills: Is able to identify business and organizational problems that may be supported by AI; is able to analyze real AI applications and distinguish between technological functions and measurable business value; is able to use generative AI tools to support project planning, develop a Work Breakdown Structure, estimate task duration, identify dependencies and define project milestones; is able to transfer an AI-supported project plan into Asana and create a clear operational schedule; is able to identify project risks and use AI to propose mitigation measures and schedule improvements; is able to analyze an organizational process and create a logically correct BPMN model in Cardanit; is able to identify process stages that may be automated or supported by AI; is able to prepare a simplified comparison of costs, processing time and resource requirements before and after AI implementation; is able to evaluate information produced by AI and verify it using reliable sources; is able to communicate the results of an AI-related business analysis in a clear written and oral form.
in terms of social competences: is ready to use artificial intelligence responsibly and critically in individual and group work; is ready to recognize the limits of AI-generated information and the continuing importance of human judgement and accountability; is ready to consider the interests of organizations, employees, customers and society when evaluating AI implementation; is ready to identify and discuss ethical concerns related to privacy, bias, transparency, manipulation and data protection; is ready to cooperate with other participants in analyzing complex business and organizational problems; is ready to justify assumptions, acknowledge uncertainty and distinguish verified evidence from AI-generated suggestions.
Assessment criteria
Written assessment (post-test)
Bibliography
Literature:
1. Verster, P. (2024). AI for business: A practical guide for business leaders to extract value from artificial intelligence. Rethink Press.
2. Russell, S. J., and Norvig, P. (2021). Artificial Intelligence: A Modern Approach. Fourth edition. Pearson.