International Journal on Science and Technology

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AI and Human-AI Collaboration in Enterprise Integration and Document Automation

Author(s) Venkatesh Nagubathula
Country United States
Abstract The integration of artificial intelligence with human expertise is transforming enterprise document processing and system integration workflows. This synergistic relationship creates a powerful framework where AI handles routine, high-volume tasks while humans manage exceptions, provide oversight, and handle complex decision-making. Organizations implementing collaborative approaches experience significant improvements in processing speed, accuracy, compliance, and operational elasticity compared to either purely manual or fully automated alternatives. These hybrid systems continuously improve through structured feedback mechanisms, with human experts training AI to handle increasingly complex scenarios while maintaining appropriate ethical guardrails. Despite implementation challenges including bias concerns, employee resistance, data privacy considerations, and integration complexity, successful deployments fundamentally reimagine organizational workflows rather than simply automating existing processes. Future developments in hyperautomation, AI-augmented APIs, conversational interfaces, and digital twins promise to further enhance this partnership model, enabling unprecedented levels of operational excellence while preserving critical human judgment for scenarios requiring contextual understanding, ethical reasoning, and novel situation handling.
Keywords Keywords: Human-AI collaboration, enterprise integration, document automation, augmented intelligence, workflow optimization
Field Computer
Published In Volume 16, Issue 1, January-March 2025
Published On 2025-03-11
Cite This AI and Human-AI Collaboration in Enterprise Integration and Document Automation - Venkatesh Nagubathula - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.2317
DOI https://doi.org/10.71097/IJSAT.v16.i1.2317
Short DOI https://doi.org/g87rhm
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