We conduct research research on the intersection between digital entrepeneurship, digital ecosystems, and organizing data and knowldedge. We will provide multiple courses that help to explain changes on firm- and ecosystem levels using multiple theories and methodologies. Our teaching is usually highly interactive and builds upon concepts of blended learning, oftentimes 'flipping the classroom'. It is important to us to not only reflect latest findings of information systems research with our students but also to work together on applying that knowledge. 

Our teaching extends to Bachelor, Master, PhD and Postdocs and will be more clearly defined in the following months.

Towards Sustainable Futures with AI

Lecture & Tutorial

Towards Sustainable Futures with AI

  • Prof. Dr. Hannes Rothe
Summer Semester 2023
Fridays, 10:00 - 14:00
R09 R01 H02

Important Notes:

(Preliminary information)


Artificial Intelligence (AI) is widely considered a generative technology that has the potential to have great impact on our society, economy, and ecology. Whether these impacts will be for worse or for better is up for discussion and depends on the actions of individuals, companies, and authorities worldwide towards the 18 UN Sustainable Development Goals.

Throughout the lecture series, students get familiar with concepts and theories that describe and explain AI companies, and learn about the design of Machine Learning-based applications. Do we need AI – or does AI solve our problems? What problems can machine learning effectively solve? What is the current impact of AI technologies on economy, society and ecology? How can we apply AI to a new domain or problem? What role do humans play in designing AI applications?

Building on fundamentals of information systems strategy and enterprise modelling, students reflect the impact of strategy and organizing in AI companies towards their ability to produce sustainable futures. We particularly investigate the generative capacity of data, tools, and (machine learning) models to produce such futures. Among others, we will cover the impact of biases in data and algorithms, explainability of AI applications, as well as accuracy, sovereignty, (inverse) scalability and framing of ML models. Throughout the entire module, we critically reflect impacts of managerial and algorithmic decision-making on sustainability, this includes impacts, for instance, on aspects of health and well-being (SDG 3), gender equality (SDG 5), or climate action (SDG 13).

Learning Targets:

Students will be able to

  • reflect on data-centric thinking in companies
  • explain the difference between types of tasks for AI and multiple machine learning techniques
  • apply machine learning techniques with low-code tools and are familiar with current models and libraries.
  • understand and apply theories of strategy and organization to AI companies
  • understand generative properties and mechanisms of information systems, especially AI applications
  • explain and critically reflect the impact of characteristics of digital resources, including data, digital tools, and (machine learning) models on AI applications.
  • explain and critically reflect the impact of information systems, particularly AI applications, on multiple sustainable development goals
  • describe fundamental processes, methods, and tools producing AI applications
  • describe and apply fundamental methods of ML project management.
  • design a business case for an AI application and produce a minimum-viable product
  • apply text generation and image generation models in assignments and reflect on their use


•          AI Companies & Data-centric Thinking

•          Sustainable Information Systems

•          Strategy & AI Companies for Sustainable Futures

•          Organization & AI Companies for Sustainable Futures

•          Managing Machine Learning Projects for Sustainable Futures

•          Building AI Applications

•          Generativity and Boundaries from Digital Tools

•          Generativity and Boundaries from Data

•          Generativity and Boundaries from (ML) Models


  • Berente, N., Gu, B., Recker, J., & Santhanam, R. (2021). Managing artificial intelligence. MIS Quarterly, 45(3).
  • Brynjolfsson, E., & Mcafee, A. (2017). Artificial intelligence, for real. Harvard Business Review, 1, 1-31.
  • Brynjolfsson, E., Rock, D., & Syverson, C. (2018). Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. In The economics of artificial intelligence: An agenda (pp. 23-57). University of Chicago Press.
  • Fürstenau, D., Baiyere, A., Schewina, K., Schulte-Althoff, M., and Rothe, H. (forthcoming). Extended Generativity Theory on Digital Platforms, Information Systems Research.
  • Gregory, R. W., Henfridsson, O., Kaganer, E., & Kyriakou, H. (2021). The role of artificial intelligence and data network effects for creating user value. Academy of Management Review, 46(3), 534-551.
  • Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. O'Reilly Media, Inc.
  • Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192-210.
  • Russell, S., & Norvig, P. (2021). Artificial Intelligence, Global Edition: A Modern Approach. (4th ed.). Pearson Education.

Further literature will be provided during the course