The Strategic Risk of Delayed and Non-Adoption of Artificial Intelligence: Evidence from Organizational Decline in Extinction Zones

Artificial intelligence (AI) is foundational to competitive strategy and operational resilience in today's digital transformation era. However, the delayed or non-adoption of AI technologies poses significant strategic risks that are often underestimated. This paper presents and elaborates on the concept of AI Extinction Zones, a market environment characterized by insufficient AI integration resulting in gradual organizational decline. Drawing on a narrative literature review and comparative case analysis, the study identifies five interrelated risk factors contributing to this decline: organizational inertia, strategic myopia, leadership deficits, resource misallocation, and ethical paralysis. By examining public cases, such as Sears, Bed Bath & Beyond, Walmart, and Moderna, this paper demonstrates that shallow or delayed adoption of artificial intelligence undermines innovation, market relevance, and long-term viability. The findings indicate that failure to adopt is not the sole threat; rather, superficial or misaligned adoption can also prove to be equally detrimental. A strategic resilience framework is proposed to assist organizations in evading or preventing extinction, focusing on deep integration, leadership capabilities, cultural agility, and timely investment. This study contributes to the discourse on digital transformation by recontextualizing AI adoption as a multidimensional strategic commitment rather than merely a technical upgrade.

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Publication Details

Journal
International Journal of Business and Management
Published
2025-06-08
DOI
https://doi.org/10.5539/ijbm.v20n4p26
Citations
1
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
2.28
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article

The Strategic Risk of Delayed and Non-Adoption of Artificial Intelligence: Evidence from Organizational Decline in Extinction Zones

Victor Frimpong
1 citations
International Journal of Business and Management
Big Data and Business Intelligence
2.28
article

The Strategic Risk of Delayed and Non-Adoption of Artificial Intelligence: Evidence from Organizational Decline in Extinction Zones

Victor Frimpong
article en
1 citations

Abstract

Artificial intelligence (AI) is foundational to competitive strategy and operational resilience in today's digital transformation era. However, the delayed or non-adoption of AI technologies poses significant strategic risks that are often underestimated. This paper presents and elaborates on the concept of AI Extinction Zones, a market environment characterized by insufficient AI integration resulting in gradual organizational decline. Drawing on a narrative literature review and comparative case analysis, the study identifies five interrelated risk factors contributing to this decline: organizational inertia, strategic myopia, leadership deficits, resource misallocation, and ethical paralysis. By examining public cases, such as Sears, Bed Bath & Beyond, Walmart, and Moderna, this paper demonstrates that shallow or delayed adoption of artificial intelligence undermines innovation, market relevance, and long-term viability. The findings indicate that failure to adopt is not the sole threat; rather, superficial or misaligned adoption can also prove to be equally detrimental. A strategic resilience framework is proposed to assist organizations in evading or preventing extinction, focusing on deep integration, leadership capabilities, cultural agility, and timely investment. This study contributes to the discourse on digital transformation by recontextualizing AI adoption as a multidimensional strategic commitment rather than merely a technical upgrade.

International Journal of Business and ManagementVol. 20(4)
Openalex Percentile: Top 11%
Big Data and Business Intelligence
2.28
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The Strategic Risk of Delayed and Non-Adoption of Artificial Intelligence: Evidence from Organizational Decline in Extinction Zones — Victor Frimpong · International Journal of Business and Management (2025) | TGRS Research Map | TGRS