DIP: 18.02.055/20251003 
DOI: 10.25215/2455/1003055 
Abstract
As customer expectations evolve in the digital era, traditional Customer Relationship Management (CRM) systems are proving inadequate in delivering personalized, context-aware engagement at scale. This research introduces the concept of Customer Relationship Intelligence (CRI)—a transformative approach that leverages generative AI to move beyond data storage and workflow automation toward dynamic, insight-driven customer interaction. CRI represents a shift from reactive data handling to proactive relationship management, where systems interpret, predict, and enhance customer journeys in real time.
The study explores how generative AI models, particularly large language models (LLMs), can analyze unstructured data from emails, chat logs, social media, and support interactions to generate deep behavioral insights. These insights can then be used to craft individualized responses, identify sentiment trends, and anticipate customer needs with unprecedented accuracy. By integrating these capabilities into CRI platforms, businesses can foster trust, loyalty, and long-term value through hyper-personalized communication.
This paper examines the technological architecture required to support CRI, including data privacy, model training, and integration with existing CRM infrastructures. Case studies from sectors such as e-commerce, finance, and healthcare demonstrate practical applications and measurable improvements in customer satisfaction and retention. Ethical implications, including transparency and bias in AI-generated content, are also addressed to ensure responsible deployment.
Ultimately, the paper argues that CRI is not merely an extension of CRM but a paradigm shift in customer strategy. Generative AI enables businesses to listen more deeply, respond more intelligently, and build human-like relationships at scale. This research contributes a framework for transitioning from CRM to CRI and outlines the critical success factors for businesses seeking to remain competitive in an AI-powered marketplace.
Keywords
- Artificial Intelligence (AI)
- Auditing
- Machine Learning
- Audit Automation
- Risk Assessment
- Fraud Detection
- Data Analytics
- Intelligent Systems
- Audit Innovation
- Financial Assurance
- Predictive Analytics
- Audit Technology
- Auditor Roles
- Ethics in AI
- Digital Transformation in Auditing
- AI Governance
- Continuous Auditing
The author(s) appreciates all those who participated in the study and helped to facilitate the research process. 
The author(s) declared no conflict of interest. 
ISSN 2455-670X