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“AI adoption in early-stage FinTech startups influencing the traditional decision-making process under conditions of uncertainty.”

Lauret, Stan LU and Elsner, Tammo LU (2025) ENTN19 20251
Department 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:
author
Lauret, Stan LU and Elsner, Tammo LU
supervisor
organization
course
ENTN19 20251
year
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}},
}