ORG472 AI in Business Transformation (Spring 2027)

On completion of the course, students should:

• have knowledge about the dynamic nature of responsible AI, including challenges and opportunities.
• be able to develop and implement AI strategies to meet business objectives.
• be able to identify risks associated with AI use and ways to address them.
• understand ethical, business-oriented, and societal issues linked to various AI systems for different actors (e.g., customers, employees, employers, and companies).
• be able to reflect on AI use in relation to aspects such as privacy, trust, transparency, fairness, autonomy, and accountability as well as uniqueness neglect and algorithm aversion.
• have advanced knowledge about various theoretical models from the areas of Big Data and AI.
• have insights regarding ways to redesign organizational systems, procedures, or roles to either becoming purely AI-driven or mixed in a hybrid AI-human format.

Artificial intelligence (AI) is completely transforming current business practices. After all, most people with a business profession have heard of ChatGPT, Dall-E, and a wide range of personal assistants, service robots, and chatbots like Alexa, Roomba, Pepper, or Cortana. Through AI and data, companies in today’s competitive landscape are continuously seeking novel ways to improve the way they think about, create, deliver, and share value.

This course focuses on the interplay between AI, data, and human actors, with an emphasis on how AI can be used in an ethical, strategic, and feasible way from a business perspective. The course describes the many opportunities and challenges that companies and individuals face when considering the adoption of different AI solutions. Responsible use of AI will be highlighted, and students will get exposed to theories and real-world examples of moral dilemmas in the AI domain as well as ways to mitigate some of the many challenges posed by new AI systems. Theories and practical tasks on General or Strong AI, algorithm aversion, hybrid human-AI solutions, and AI accountability will be covered as well as strategies to identify, manage, and reduce AI risks and biases. The course will also familiarize students with a set of existing AI systems, which will be dynamically used during lectures and as part of course assignments.

Four-hour written examination under supervision. Assessment expression: letter grade, A-F.

More information in Canvas.