Change Management and the Integration of Generative AI in Organizational CRM Projects : A Qualitative Study in the Context of Salesforce Implementation

Abstract

This thesis examines how change management (CM) practices shape the adoption of generative AI in customer relationship management (CRM) implementation projects. Using a mixed-methods design combining semi-structured interviews (N=5) with a pilot survey (N=89), we study two Salesforce CRM projects conducted by Accenture for two clients, a major French energy and services group and a French water utility. The findings reveal that AI readiness is highly uneven across populations, that resistance takes primarily epistemic rather than affective forms, and that change management is systematically deprioritized despite its measurable impact on readiness. We introduce two theoretical concepts: epistemic resistance, resistance rooted in the inability to evaluate AI output reliability, and augmentation drift, the gradual shift from augmentation to de facto automation when users stop critically engaging with AI outputs. The pilot data supports epistemic resistance as a stronger predictor of non-adoption (β=-0.289) than affective resistance (β=-0.179). Practical implications include investing in AI literacy over traditional communication, diagnosing digital maturity before deployment, and using regulatory compliance as a trust-building mechanism.