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Resource-Poor, Risk-Rich: Why Small Businesses Struggle to Turn AI Into Strategic Advantage

Timothy R. Mcilveene et al.

Journal of Small Business Strategy2026https://doi.org/10.53703/001c.158957article
ABDC C
Weight
0.50

What the paper says

While many small businesses are adopting artificial intelligence (AI), many struggle to translate this adoption into a lasting and sustainable competitive advantage. This paper posits that this implementation gap is not merely a strategic failure but also introduces significant ethical risks that can harm key stakeholders. Using a dual theoretical framework approach, we employ the resource-based view to identify how “resource poverty,” specifically, gaps in financial, human, technological, and organizational resources, inhibits successful AI implementation. The paper then applies stakeholder theory to illustrate how these internal resource gaps directly lead to external harms, including risks to customer privacy, employee welfare, and broader societal well-being. To address these challenges, a practical four-stage AI capability roadmap is introduced to help small business leaders develop their internal resources and capabilities and establish the necessary ethical safeguards. This roadmap provides a guide for small businesses to navigate the complexities of AI.

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https://doi.org/https://doi.org/10.53703/001c.158957

Or copy a formatted citation

@article{timothy2026,
  title        = {{Resource-Poor, Risk-Rich: Why Small Businesses Struggle to Turn AI Into Strategic Advantage}},
  author       = {Timothy R. Mcilveene et al.},
  journal      = {Journal of Small Business Strategy},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.53703/001c.158957},
}

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Resource-Poor, Risk-Rich: Why Small Businesses Struggle to Turn AI Into Strategic Advantage

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.