Comment l'IA impacte les PME en Wallonie et quels sont les facteurs influençant son adoption ?
Cartenstadt, Thomas
Promoteur(s) :
Blavier, André
Date de soutenance : 28-aoû-2025 • URL permanente : http://hdl.handle.net/2268.2/24067
Détails
| Titre : | Comment l'IA impacte les PME en Wallonie et quels sont les facteurs influençant son adoption ? |
| Auteur : | Cartenstadt, Thomas
|
| Date de soutenance : | 28-aoû-2025 |
| Promoteur(s) : | Blavier, André
|
| Membre(s) du jury : | Lambert, Aurore
|
| Langue : | Français |
| Nombre de pages : | 65 |
| Discipline(s) : | Sciences économiques & de gestion > Multidisciplinaire, généralités & autres |
| Public cible : | Chercheurs Professionnels du domaine Autre |
| Institution(s) : | Université de Liège, Liège, Belgique |
| Diplôme : | Master en sciences de gestion, à finalité spécialisée en droit |
| Faculté : | Mémoires de la HEC-Ecole de gestion de l'Université de Liège |
Résumé
[fr] The study analyzes the impact of AI on SMEs in Wallonia and the factors that condition its adoption,
with the aim of providing an evidence-based account of actually deployed uses and their dynamics of
appropriation. It is situated within a context of promises of efficiency and societal risks, and asks:
“How does AI affect SMEs in Wallonia, and which factors influence its adoption?”
The methodology relies on semi-structured interviews with field practitioners and an inductive, two
cycle qualitative coding. Nine families of factors emerge; use cases are distinguished between sector
specific and cross-cutting, and operative distinctions are drawn between mass-market generative AI
and targeted solutions, as well as between “internal” and “external” solutions. Observed uses notably
include software-development assistance, construction pre-visualization, geospatial analysis, and the
optimization of marketing campaigns.
The principal drivers identified are productivity gains, improved decision quality, creativity, and new
value propositions; barriers concern reliability, the need for a critical mindset, and a central trade-off
between data security, performance, and costs, mediated by data, skills, and organizational culture.
From a regulatory perspective, the AI Act chiefly imposes light obligations on deployers of non-“high
risk” systems and provides relief measures and regulatory sandboxes for SMEs. Prospects include
quantitative measurement, sectoral analyses, and examination of the regional ecosystem’s role.
Fichier(s)
Document(s)
TFE_Comment l’IA impacte les PME en Wallonie et quels sont les facteurs influençant son adoption_Cartenstadt_Thomas_2024-2025.pdf
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