Resistance to Change and Implementation Challenges of AI in Moroccan Public Administrations
The integration of artificial intelligence (AI) into public administrations presents significant opportunities for improving efficiency, transparency, and service delivery. However, its implementation in the Moroccan public sector faces multiple challenges, particularly related to organizational resistance and structural limitations. This study examines the underlying factors driving resistance to AI adoption in Moroccan administrative institutions and identifies the key obstacles that hinder successful implementation. Using a qualitative methodology based on semi-structured interviews with public officials and experts, the research explores perceptions, institutional cultures, and technical barriers encountered during digital transformation initiatives. The findings reveal a strong correlation between resistance to change and factors such as lack of digital literacy, fear of job displacement, limited infrastructure, and insufficient leadership commitment. Moreover, the absence of a coherent national strategy and clear regulatory frameworks further exacerbates the difficulties of AI integration. The study concludes that successful implementation of AI in public administrations requires not only technical upgrades but also comprehensive change management strategies, capacity building, and inclusive policy frameworks. These results highlight the urgent need for a holistic approach to digital transformation in the Moroccan public sector, where human, institutional, and technological dimensions are addressed simultaneously to ensure sustainable and effective innovation.
Authors
- Cheklekbire Malainine (ORCID: https://orcid.org/0000-0002-5262-518X)
- Oussama Najari (ORCID: https://orcid.org/0009-0009-2639-526X)
- Oumaima Ayi (ORCID: https://orcid.org/0009-0002-9460-5248)
Institutions
- Université Ibn-Tofail (MA)
Publication Details
- Journal
- International Journal of Research in Economics and Finance
- Published
- 2025-06-07
- DOI
- https://doi.org/10.71420/ijref.v2i5.102
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.00