Civil servants’ readiness for AI adoption: The role of change management in Morocco’s public sector

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The rapid digital transformation of public systems has improved interactions between governments and citizens. In Morocco, while efforts to digitalize public administration continue, the integration of artificial intelligence presents new challenges due to structural and technical limitations. This study explores the openness of Moroccan civil servants to adopting artificial intelligence solutions and examines the role of change management in facilitating this process. A quantitative approach was employed, with 129 civil servants from key ministries – Education, Finance, and Health – completing an online questionnaire. These ministries were selected due to their critical importance in the public system and their frequent interactions with citizens. Furthermore, they played a central role in the National Administrative Reform Plan (2018–2022), which emphasized digital transformation as a key pillar in advancing e-government. The collected data were analyzed using SPSS, enabling a comprehensive analysis of the factors influencing AI adoption. The findings reveal that while younger civil servants are more open to AI, over 40% of respondents pointed to insufficient digital skills as a major barrier to artificial intelligence integration. The study underscores the importance of effective change management strategies, highlighting that strong leadership and clear communication are essential in promoting artificial intelligence receptiveness and ensuring seamless integration within Morocco’s public sector.

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    • Figure 1. Factors responsible for a successful transitional process
    • Figure 2. Modeling on SPSS
    • Table 1. Sample design
    • Table 2. Level of familiarity with ICT
    • Table 3. Appropriateness of the change management strategy
    • Table 4. Level of openness for AI
    • Table 5. Main obstacles to successful digital transformation
    • Table 6. Variables chosen to apply SEM test
    • Table 7. Squared multiple correlations
    • Table 8. Validation of hypotheses: VIA regression weights and standardized regression weights
    • Conceptualization
      Mohamed Barodi, Siham Lalaoui
    • Data curation
      Mohamed Barodi, Siham Lalaoui
    • Formal Analysis
      Mohamed Barodi, Siham Lalaoui
    • Investigation
      Mohamed Barodi, Siham Lalaoui
    • Methodology
      Mohamed Barodi, Siham Lalaoui
    • Resources
      Mohamed Barodi, Siham Lalaoui
    • Software
      Mohamed Barodi, Siham Lalaoui
    • Writing – original draft
      Mohamed Barodi, Siham Lalaoui
    • Writing – review & editing
      Mohamed Barodi, Siham Lalaoui