AI Adoption as the 6th Force in Porter's Five Forces Model: A Conceptual Framework for SME Competitive Strategy

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Auteur principal: Prof Vincent English
Format: Recurso digital
Langue:anglais
Publié: Zenodo 2026
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author Prof Vincent English
author_facet Prof Vincent English
contents <p><a>ABSTRACT :</a></p> <p>Artificial intelligence (AI) is rapidly reshaping competitive dynamics across industries, creating both acute threats and significant opportunities for small and medium-sized enterprises (SMEs). Building on critiques of the static, pre-digital assumptions embedded in Porter’s Five Forces framework, this paper argues that AI adoption should be conceptualised as a distinct <strong>sixth competitive force</strong>—one that is pervasive, self-accelerating, and capable of reconfiguring the original five forces rather than simply influencing them at the margins.</p> <p>Drawing on empirical insights synthesised from Fascinari’s (2025) AI-based Strategic Technology Management research, the paper develops a conceptual model explaining how differential AI adoption alters barriers to entry (through both commoditised tools and proprietary “data moats”), shifts buyer–supplier power via enhanced prediction and optimisation, accelerates substitution through AI-enabled products and services, and intensifies rivalry by moving competition toward AI capability and learning speed.</p> <p>The paper further outlines practical strategic implications for SMEs, emphasising AI as a socio-technical transformation requiring dynamic capability development, human–AI augmentation, and robust governance and risk management.  In doing so, it contributes an updated lens for analysing industry structure and strategy in AI-shaped markets, and identifies directions for future empirical validation across sectors and regions.</p>
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spellingShingle AI Adoption as the 6th Force in Porter's Five Forces Model: A Conceptual Framework for SME Competitive Strategy
Prof Vincent English
Artificial intelligence adoption; Porter's Five Forces; sixth competitive force; SMEs; competitive strategy; dynamic capabilities; strategic technology management; human–AI collaboration; data governance; digital transformation.
<p><a>ABSTRACT :</a></p> <p>Artificial intelligence (AI) is rapidly reshaping competitive dynamics across industries, creating both acute threats and significant opportunities for small and medium-sized enterprises (SMEs). Building on critiques of the static, pre-digital assumptions embedded in Porter’s Five Forces framework, this paper argues that AI adoption should be conceptualised as a distinct <strong>sixth competitive force</strong>—one that is pervasive, self-accelerating, and capable of reconfiguring the original five forces rather than simply influencing them at the margins.</p> <p>Drawing on empirical insights synthesised from Fascinari’s (2025) AI-based Strategic Technology Management research, the paper develops a conceptual model explaining how differential AI adoption alters barriers to entry (through both commoditised tools and proprietary “data moats”), shifts buyer–supplier power via enhanced prediction and optimisation, accelerates substitution through AI-enabled products and services, and intensifies rivalry by moving competition toward AI capability and learning speed.</p> <p>The paper further outlines practical strategic implications for SMEs, emphasising AI as a socio-technical transformation requiring dynamic capability development, human–AI augmentation, and robust governance and risk management.  In doing so, it contributes an updated lens for analysing industry structure and strategy in AI-shaped markets, and identifies directions for future empirical validation across sectors and regions.</p>
title AI Adoption as the 6th Force in Porter's Five Forces Model: A Conceptual Framework for SME Competitive Strategy
topic Artificial intelligence adoption; Porter's Five Forces; sixth competitive force; SMEs; competitive strategy; dynamic capabilities; strategic technology management; human–AI collaboration; data governance; digital transformation.
url https://doi.org/10.5281/zenodo.18502544