Generative AI: Revolution or Threat for Digital Service Companies ?

IA générative: révolution ou menace pour les entreprises de services du numérique ?

Morgan Blangeois (morgan.blangeois@uca.fr)
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Morgan Blangeois: CleRMa - Clermont Recherche Management - ESC Clermont-Ferrand - École Supérieure de Commerce (ESC) - Clermont-Ferrand - UCA - Université Clermont Auvergne

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Abstract: This article delves into the revolution of generative artificial intelligence (AI) in digital service companies (DSCs), focusing on foundational models like GPT-4. It examines the debates surrounding these technologies, particularly their implications in terms of misinformation through hallucinations, the social biases they carry, and their influence on the digital transformation of organizations. It begins with an exploration of the emergence of generative AI, highlighting technological advances and their practical impacts. The article assesses the current capabilities of generative AI and its potential, highlighting its role in redefining the job market, especially in terms of skills. The core of the study focuses on the challenges and strategic opportunities for DSCs. Examining how generative AI transforms traditional functions and creates new strategies, the article underscores the importance for DSCs to reconsider their position in the IT value chain. We explore how the adoption of open-innovation and business model innovations can facilitate the adaptation of DSCs to these challenges and conclude with a call for empirical studies to further explore these themes.

Keywords: Business Model; Digital Services Companies; Open Innovation; Artificial Intelligence; Strategy; Modèles d'affaires; Entreprises des Services du Numérique; Innovation Ouverte; Intelligence Artificielle; Stratégie (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-ain, nep-cmp, nep-pay and nep-sbm
Note: View the original document on HAL open archive server: https://uca.hal.science/hal-04355219v1
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Published in Management & Data Science, 2023, ⟨10.36863/mds.a.26672⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04355219

DOI: 10.36863/mds.a.26672

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