Scalable Trust and Auditability for Generative AI in Enterprise CRM Platforms: A Framework and Reference Architecture

Generative artificial intelligence is increasingly integrated into enterprise customer relationship management (CRM) platforms, raising challenges related to trust, transparency, and regulatory compliance. While these systems enhance automation and decision-making, their opaque nature complicates auditability on a scale. This study proposes a scalable trust framework and reference architecture that embeds auditability, traceability, and accountability directly into generative AI-enabled CRM systems. Using a design science research approach, the study develops a multi-layered architecture incorporating model version control, prompt governance, immutable logging, and policy enforcement. Empirical evaluation using simulated enterprise datasets shows significant improvements in audit coverage, traceability depth, trust scores, and compliance efficiency compared to baseline systems. The findings demonstrate that trust can be operationalized as an architectural property and provide a practical blueprint for deploying generative AI in enterprise environments.