Earticle

초록

영어

The proliferation of no-code and low-code automation platforms has fundamentally transformed how organizations approach workflow automation, enabling non-technical users to create sophisticated integrations previously requiring extensive programming expertise. While existing comparative studies predominantly focus on feature inventories and pricing tiers, this paper distinguishes itself through three novel perspectives. First, it employs a strategic deployment sovereignty lens, explicitly examining how cloud-native versus self-hosted architectures align with organizational data governance requirements and infrastructure philosophies—a dimension largely absent in conventional tool comparisons. Second, unlike studies treating AI integration as a peripheral feature, this research positions AI-driven intelligent orchestration as a fundamental architectural paradigm, analyzing how platforms differ in their native AI capabilities versus external service integration approaches. Third, this study provides empirical validation through three realworld use case implementations spanning data synchronization, automated reporting, and AI-augmented customer support, demonstrating practical performance characteristics rather than relying solely on vendor specifications. Through this multi-dimensional framework combining architectural analysis, deployment model implications, AI integration strategies, and scenario-based empirical evidence, the research reveals that platform selection constitutes a strategic decision requiring alignment with organizational infrastructure philosophy, regulatory constraints, technical capability, and AI adoption maturity. This study indicates that Make optimizes for rapid deployment with comprehensive pre-built integrations, n8n provides maximum flexibility and data sovereignty through open-source self-hosting, and Opal demonstrates emerging AI-native capabilities with corresponding maturity trade-offs. These insights enable organizations to move beyond superficial feature comparisons toward strategic tool selection aligned with long-term automation vision and operational requirements.

목차

Abstract
1. Introduction
1.1 Background and Research Objectives
1.2 Research Methodology
2. Overview of Automation Tools
2.1 Make: Visual Integration Platform
2.2 n8n: Open-Source Workflow Automation
2.3 Opal: No-Code Automation for Intelligent Orchestration
3. Comparative Analysis
3.1 Architecture and Design Paradigms
3.2 Integration Capabilities and API Support
3.3 Scalability and Performance
3.4 User Experience and Accessibility
3.5 Security and Data Privacy Considerations
4. Use Cases and Practical Applications
4.1 Real-World Use Case Comparison: Cross-Platform Data Synchronization
4.2 Real-World Use Case Comparison: Automated Financial Reporting
4.3 Real-World Use Case Comparison: AI-Augmented Customer Support Workflow
5. Discussion
6. Conclusion
References

키워드

저자

• Yo-Seob Lee [ Professor, Dept. of Smart Contents, Pyeongtaek University ]
• Phil-Joo Moon [ Professor, Dept. of Information & Communication, Pyeongtaek University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380