Understanding the future: Designing Intelligent Foresight Systems

Contact person: Andreas Wunder

The development and ongoing refinement of a company’s strategy involves a series of interconnected decisions, especially concerning the operational areas and competitive advantages (Lafley & Martin, 2013). Managers are responsible for making respective decisions, drawing insights from analyst teams and consultants (Paroutis et al., 2013). During strategy development managers struggle to cope with the uncertainty and volatility of the rapidly changing corporate environment (Finkenstadt et al., 2023; Mankins & Gottfredson, 2022). A popular approach to prepare for an uncertain future is strategic foresight (Cordova-Pozo & Rouwette, 2023; Iden et al., 2017; Piirainen et al., 2012) and explains “a practice that permits an organization to lay the foundation for a future competitive advantage” (Rohrbeck et al., 2015).

Despite the considerable benefits of strategic foresight, its implementation is declining (Ködding et al., 2023). Driven by digitalization, managers and their teams face challenges in processing and analyzing exponentially increasing data arriving at varying speeds, necessitating quicker decision-making and more frequent revisions. Moreover, foresight work is methodologically complex, time-consuming and too costly for routine implementation (Ködding et al., 2023; Mankins & Gottfredson, 2022). As decisions about strategy should be left to managers and time is scarce “we need approaches that facilitate direct participation of the management team that is responsible for setting the course of action." (Lehr et al., 2017)

Generative AI (GenAI) is a promising way to address these challenges. Recent advances, particularly in large language models and diffusion models, have streamlined digital content creation, facilitating the rapid production of high-quality material, including text, images or videos (Banh & Strobel, 2023). GenAI-based systems could enhance an organization's capability in conducting strategic foresight faster and with active participation of management teams (Finkenstadt et al., 2023; Geurts et al., 2022; Ködding et al., 2023; Spaniol & Rowland, 2023).

Accordingly, the dissertation project aims to understand how intelligent foresight systems should be designed to create a better understanding of the future. It follows Design Science Research (Peffers et al., 2018; Tuunanen et al., 2024) and is guided by the underlying theory of sensemaking and technology affordances and constraints. A mixed-methods approach, using a variety of research methods, is used to reach four preliminary research objectives.

1. Map the landscape: Literature review and survey assessment to understand the current landscape of generative AI use cases in the strategy development process.
2. Examine collaboration: Qualitative study of human-AI collaboration in intelligence tasks.
3. Build the system: Design and implement a prototype for intelligent foresight.
4. Test and validate: Experimental utility assessment, validation and refinement of design principles.

This is a cooperative doctoral project with Mainz University of Applied Sciences.

Banh, L., & Strobel, G. (2023). Generative artificial intelligence. Electronic Markets, 33(1), 63. https://doi.org/10.1007/s12525-023-00680-1

Cordova-Pozo, K., & Rouwette, E. A. J. A. (2023). Types of scenario planning and their effectiveness: A review of reviews. Futures, 149, 103153. https://doi.org/10.1016/j.futures.2023.103153

Finkenstadt, D. J., Eapen, T. T., Sotiriadis, J., & Guinto, P. (2023, November 30). Use GenAI to Improve Scenario Planning. Harvard Business Review. https://hbr.org/2023/11/use-genai-to-improve-scenario-planning

Geurts, A., Gutknecht, R., Warnke, P., Goetheer, A., Schirrmeister, E., Bakker, B., & Meissner, S. (2022). New perspectives for data-supported foresight: The hybrid AI-expert approach. FUTURES & FORESIGHT SCIENCE, 4(1), e99. https://doi.org/10.1002/ffo2.99

Iden, J., Methlie, L. B., & Christensen, G. E. (2017). The nature of strategic foresight research: A systematic literature review. Technological Forecasting and Social Change, 116, 87–97. https://doi.org/10.1016/j.techfore.2016.11.002

Ködding, P., Ellermann, K., Koldewey, C., & Dumitrescu, R. (2023). Scenario-based Foresight in the Age of Digitalization and Artificial Intelligence – Identification and Analysis of Existing Use Cases. Procedia CIRP, 119, 740–745. https://doi.org/10.1016/j.procir.2023.01.015

Lafley, A. G., & Martin, R. L. (2013). Playing to win: How strategy really works. Harvard Business Review Press.

Lehr, T., Lorenz, U., Willert, M., & Rohrbeck, R. (2017). Scenario-based strategizing: Advancing the applicability in strategists’ teams. Technological Forecasting and Social Change, 124, 214–224. https://doi.org/10.1016/j.techfore.2017.06.026

Mankins, M., & Gottfredson, M. (2022, September 1). Strategy-Making in Turbulent Times. Harvard Business Review. https://hbr.org/2022/09/strategy-making-in-turbulent-times

Paroutis, S., Heracleous, L. T., & Angwin, D. (2013). Practicing strategy: Text and cases. SAGE.

Peffers, K., Tuunanen, T., & Niehaves, B. (2018). Design science research genres: Introduction to the special issue on exemplars and criteria for applicable design science research. European Journal of Information Systems, 27(2), 129–139. https://doi.org/10.1080/0960085X.2018.1458066

Piirainen, K. A., Gonzalez, R. A., & Bragge, J. (2012). A systemic evaluation framework for futures research. Futures, 44(5), 464–474. https://doi.org/10.1016/j.futures.2012.03.008

Rohrbeck, R., Battistella, C., & Huizingh, E. (2015). Corporate foresight: An emerging field with a rich tradition. Technological Forecasting and Social Change, 101, 1–9. https://doi.org/10.1016/j.techfore.2015.11.002

Spaniol, M. J., & Rowland, N. J. (2023). AI-assisted scenario generation for strategic planning. FUTURES & FORESIGHT SCIENCE, 5(2), e148. https://doi.org/10.1002/ffo2.148

Tuunanen, T., Winter, R., & Brocke, J. vom. (2024). Dealing with Complexity in Design Science Research: A Methodology Using Design Echelons. Management Information Systems Quarterly, 48(2), 427–458.