What question did this study set out to answer?

This survey aims to identify and analyze auditing and governance challenges in LLM-integrated decision systems for CRM platforms.

April 5, 2026Open Access

Auditing LLM-Integrated Decision Systems in Customer Relationship Management: A Systematic Survey

Key Points

This survey aims to identify and analyze auditing and governance challenges in LLM-integrated decision systems for CRM platforms.
Conducted a systematic review of auditing frameworks and techniques
Evaluated governance approaches in AI-driven CRM systems
Identified limitations in traditional audit mechanisms
Explored key risks associated with AI-assisted decisions
Revealed significant gaps in transparency, traceability, and accountability for LLM decisions
Identified risks related to bias and decision opacity
Provided future directions for developing auditable and trustworthy AI systems in CRM environments

Abstract

This paper presents a comprehensive survey of auditing and governance challenges in large language model (LLM)-integrated decision systems within customer relationship management (CRM) platforms. As CRMs evolve into AI-driven decision engines, LLMs increasingly influence workflows such as lead qualification, customer engagement, and service automation. This study examines limitations in traditional audit mechanisms, highlighting gaps in transparency, traceability, and accountability of AI-assisted decisions. It reviews existing frameworks, monitoring techniques, and governance approaches, and identifies key risks related to bias, hallucinations, and decision opacity. The paper outlines future directions for building auditable, risk-aware, and trustworthy AI systems in enterprise CRM environments.

Auditing LLM-Integrated Decision Systems in Customer Relationship Management: A Systematic Survey

Key Points

Abstract

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