“AI adoption in early-stage FinTech startups influencing the traditional decision-making process under conditions of uncertainty.”
(2025) ENTN19 20251Department of Business Administration
- Abstract
- This thesis investigates how early-stage FinTech startups integrate Artificial Intelligence (AI) into strategic decision-making processes under conditions of uncertainty. By employing the OODA loop (Observe, Orient, Decide, Act) as a foundational framework, the study examines the evolving role of AI within each phase of the decision-making cycle. Drawing on nine in-depth interviews with founders and decision-makers across European FinTech ventures, the findings reveal a nuanced pattern of AI usage: from operational assistance in the "Observe" and "Act" stages, to interpretive support in "Orient," and human-AI collaboration in "Decide." Notably, the study identifies a critical gap in existing frameworks by highlighting the absence of a... (More)
- This thesis investigates how early-stage FinTech startups integrate Artificial Intelligence (AI) into strategic decision-making processes under conditions of uncertainty. By employing the OODA loop (Observe, Orient, Decide, Act) as a foundational framework, the study examines the evolving role of AI within each phase of the decision-making cycle. Drawing on nine in-depth interviews with founders and decision-makers across European FinTech ventures, the findings reveal a nuanced pattern of AI usage: from operational assistance in the "Observe" and "Act" stages, to interpretive support in "Orient," and human-AI collaboration in "Decide." Notably, the study identifies a critical gap in existing frameworks by highlighting the absence of a monitoring or post-action feedback phase. To address this, a revised OODA model is proposed, integrating a fifth phase centered on monitoring. The results underscore AI's augmentative value rather than a purely automating force. This research contributes both theoretically and practically by refining decision-making models and offering actionable insights for AI adoption strategies in modern entrepreneurial settings. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9204629
- author
- Lauret, Stan LU and Elsner, Tammo LU
- supervisor
-
- Ziad El-Awad LU
- organization
- course
- ENTN19 20251
- year
- 2025
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- Entrepreneurship, FinTech, Decision-Making, OODA Loop, AI Adoption, Uncertainty
- language
- English
- id
- 9204629
- date added to LUP
- 2025-07-01 08:35:48
- date last changed
- 2025-07-01 08:35:48
@misc{9204629, abstract = {{This thesis investigates how early-stage FinTech startups integrate Artificial Intelligence (AI) into strategic decision-making processes under conditions of uncertainty. By employing the OODA loop (Observe, Orient, Decide, Act) as a foundational framework, the study examines the evolving role of AI within each phase of the decision-making cycle. Drawing on nine in-depth interviews with founders and decision-makers across European FinTech ventures, the findings reveal a nuanced pattern of AI usage: from operational assistance in the "Observe" and "Act" stages, to interpretive support in "Orient," and human-AI collaboration in "Decide." Notably, the study identifies a critical gap in existing frameworks by highlighting the absence of a monitoring or post-action feedback phase. To address this, a revised OODA model is proposed, integrating a fifth phase centered on monitoring. The results underscore AI's augmentative value rather than a purely automating force. This research contributes both theoretically and practically by refining decision-making models and offering actionable insights for AI adoption strategies in modern entrepreneurial settings.}}, author = {{Lauret, Stan and Elsner, Tammo}}, language = {{eng}}, note = {{Student Paper}}, title = {{“AI adoption in early-stage FinTech startups influencing the traditional decision-making process under conditions of uncertainty.”}}, year = {{2025}}, }