What resources and organizational capabilities influence an effective integration of generative artificial intelligence in companies?
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What resources and organizational capabilities influence an effective integration of generative artificial intelligence in companies.pdf
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- The digitalization of organizations has established itself as a systemic and irreversible transformation, disrupting both internal processes and business models. In recent years, a particularly significant turning point has been the rapid emergence of generative artificial intelligence GenAI, which is part of a wave of innovation driven by the spectacular progress of large language models LLMs such as GPT, Gemini or Claude. Unlike traditional digital technologies, GenAI not only makes it possible to automate tasks but also to generate new content, to support decision making or to interact in natural language with users. Its transformational potential is so profound that some authors see it as a general-purpose technology comparable to electricity. Many companies are currently exploring the possible uses of these generative technologies, for writing assistance, rapid prototyping, automated summaries, coding support, etc. Yet the adoption of GenAI is not merely a matter of technological access. It questions more deeply the way in which companies mobilize and transform their internal resources to take advantage of this emerging technology in a context of uncertainty and rapid evolution. While experiments are multiplying, truly efficient integration cases remain rare and fragmented, especially outside the major technology groups. It is at this level that our problem is situated. This thesis seeks to answer the following question “What resources and organizational capabilities influence an effective integration of generative artificial intelligence in companies?”. This analytical shift allows us to go beyond general technical or ethical debates to focus specifically on the internal levers that companies can mobilize. In other words, it is not a question of understanding what GenAI does, but how organizations can integrate it in a sustainable and strategic way into their activities. The main objective of this thesis is therefore to identify internal factors both in terms of resources and dynamic capabilities that facilitate or hinder a successful appropriation of GenAI. This approach reflects a desire to provide concrete insights to executives, managers and practitioners facing the challenge of deploying GenAI beyond test phases or proof of concept. By combining academic literature with empirical data from the field, this work aims to identify patterns, good practices and tensions specific to this technological transition. To do so, we rely on two complementary theoretical frameworks. The first is the resource- based view (RBV) introduce by Wernerfelt (1984) and formalized by Barney (1991) which allows the identification of internal resources considered strategic for the integration of GenAI. We distinguish three major categories: human resources, technological resources and organizational resources. However, the RBV remains a mostly static framework not very suitable for capturing the rapid transformation dynamics imposed by emerging technologies. This is why we also mobilize the dynamic capabilities approach, introduced by Teece Pisano and Shuen (1997), in order to analyse how companies sense, seize and transform the opportunities related to GenAI. The articulation of these two frameworks makes it possible to combine an analysis of available resources with a dynamic reading of the capacities to make them evolve. On the empirical level our approach is mainly based on an exploratory qualitative study. It relies on seven semi structured interviews conducted with professionals from various sectors (banking, consulting, technology, etc) with concrete experience in the use of GenAI in companies. Additionally, we created a general questionnaire that we shared on our social media to capture the representation and perception of GenAI for a broader audience. While this survey only captured a limited sample, it highlights some of the points addressed in the theory and enriches our analysis with additional perspective.