Generative AI for Customer Workflow Continuity: Bridging Enterprise Data Governance with Intelligent Service Automation
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V5I6P104Keywords:
Generative AI, Conversational Workflow Continuity, Intelligent Service Automation, Contextual Customer Engagement, Workflow Intelligence, Enterprise Data Governance, AI-Assisted Customer Operations, Service ContinuityAbstract
Generative Artificial Intelligence (GenAI) is rapidly transforming enterprise operations by enabling intelligent automation, adaptive customer engagement, and real-time workflow optimization. In today's digital landscape, where businesses are often spread across multiple locations, AI-driven systems are becoming essential for ensuring workflow continuity and managing enterprise needs for governance, security, scale and compliance with customers. The adoption of Generative AI in enterprise ecosystems brings many challenges of data privacy, policy management, interoperability, transparency and reliability with operation. In this paper, the authors suggest a governance-driven approach that combines enterprise data governance and intelligent service automation to enable secure and resilient customer workflow continuity. The proposed architecture will bring together policy-aware data pipelines, access control mechanisms, contextual AI workflow engines, cloud-native microservices, knowledge management systems and automated service orchestration into a single enterprise architecture. The use of Generative AI technologies such as Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Intelligent Conversational Agents (ICAs) enhances enterprise service responsiveness, optimizes workflows, and automates customer interactions. The framework also introduces explainability, auditability, metadata management, and ongoing monitoring features to guarantee the trustworthiness and regulation-adhering function of AI. The proposed framework has been evaluated in an experimental setting, and it has been shown to achieve a significant increase in the accuracy of workflow automation, the rate of customer requests being resolved, service efficiency and adherence to governance in enterprise environments. Results show that using this approach to workflow management, the system can be more resilient during operation, reduce service interruptions and increase custxomer satisfaction compared to the traditional method of workflow management. The research underscores thsse significance of embedding governance-first principles into Generative AI tools to aid in enterprise digital transformation efforts that are scalable, secure, and intelligent.
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