Organizations increasingly use AI to plan ahead and base decisions on data that present one future as given. Scenario planning offers an alternative. It confronts decision makers with several plausible futures at once and helps them challenge their assumptions before they decide. In corporate practice, however, there is a tension. Qualitative scenarios capture uncertainty but are hard to link to strategic planning. Quantitative forecasts fit planning processes but cannot represent uncertainty or decision points. Generative AI has cut the cost of writing scenarios to minutes, and it now serves scenario creation, narrative exploration, and strategy development. Yet AI-generated scenarios tend to reproduce statistically likely expectations, focus on technology, and barely differentiate between time horizons. A 2-year and a 10-year future of the same sector can come out as two versions of the same narrative. 

Time series foundation models open a new path: they can extrapolate company data separately for each scenario and horizon, while language models take over the narration. We still lack knowledge of how such tools should be designed and how decision makers make sense of the scenarios these tools produce. This thesis investigates how AI-supported futuring tools can help decision makers explore multiple futures, question their own assumptions, and translate scenarios into strategic considerations. Depending on the student's interest, the thesis can focus on one of the following directions:

• the design of AI-supported scenario tools, e.g., how to combine quantitative data, forecasts, and generated narratives;
• how decision makers use and judge AI-generated scenarios, e.g., plausibility, desirability, and trust;
• how scenario work affects strategic reflection and alignment within management teams.

Possible methods include a structured literature review, Design Science Research, expert interviews or Delphi studies, workshops with practitioners, the analysis of interaction and rating data, and qualitative content or thematic analysis.

I have authored the following articles that closely relate to this project, please get familiar with at least one of them:

Comparative Study: https://www.sciencedirect.com/science/article/pii/S0016328725001673?via%3Dihub

Technical report: https://dl.acm.org/doi/abs/10.1145/3591196.3596827

Opinion Piece https://aisel.aisnet.org/icis2026/contheme/contheme/7/

Benefits:

• Close supervision within an ongoing research project at the Institute of Information Systems and Digital Business (IWI-HSG)
• Access to a working futuring prototype and its data basis (AI disclosures from SEC filings, scenario forecasts)
• Contact with practitioners and executives from foresight and corporate strategy
• Work at the intersection of strategic foresight, generative AI, and Information Systems
• The opportunity to publish the results together with the research team 

Send me a mail that has not been generated by an LLM that states what you would like to contribute and if possible, a coding-work sample.