Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Envisioning the Future Using Generative AI—Implications for Corporate Foresight Practices
2
Zitationen
3
Autoren
2025
Jahr
Abstract
OVERVIEW: This study explores the integration of generative artificial intelligence (gen AI)—specifically ChatGPT—into corporate foresight practices, focusing on how its use influences scenario generation and strategic thinking. Using a combination of critical discourse analysis and content analysis, we identify three distinct ways innovation managers engage with ChatGPT: full delegation, confirmation of beliefs, and information retrieval for cognitive support. Our findings reveal how these different interactions affect the depth and diversity of foresight exercises, influencing whether gen AI challenges or reinforces existing assumptions. This study contributes to the foresight literature by illustrating that gen AI’s role in creative processes is contingent on user interaction. We provide managerial insights on how to leverage gen AI to enhance strategic imagination while promoting critical evaluation, ultimately supporting more balanced and reflective future-oriented decision-making. PRACTITIONER TAKEAWAYS ChatGPT is a valuable tool for scenario generation, integrating key trends and building comprehensive, systemic narratives that focus on broader dynamics rather than isolated events or individual characters, without losing analyzed information. Three main approaches to ChatGPT use emerged: delegation, that is, full task execution for novel exploration; belief confirmation, which entails expanding preexisting ideas; and cognitive support, the iterative integration with existing mental models. Alignment with foresight goals is essential to determine whether ChatGPT should be used for novel exploration, confirmation of beliefs, or critical integration of new and prior ideas.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.339 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.211 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.614 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.478 Zit.