AI-Driven Low-Code Workflow Automation: A Cross-Sector Analysis (2019–2025)

Authors: Nan Wu

DOI: https://doi.org/10.37082/IJIRMPS.v13.i3.232541

Short DOI: https://doi.org/g9mv2r

Country: USA

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Abstract: This paper comparatively examines AI-driven low-code/no-code (LCNC) workflow automation in healthcare, finance, and education from 2019–2025. By analyzing sector-specific cases, this research identifies commonalities like enhanced efficiency, reduced costs, and democratized technology access. It also highlights critical sector-specific differences in regulatory compliance, data privacy, and personalization requirements. Strategic recommendations include establishing clear objectives, robust integration practices, comprehensive training programs, and fostering a culture of innovation. The insights presented can guide organizations in effectively implementing intelligent automation solutions, enabling them to better navigate sectoral challenges and capitalize on technological advancements for sustained operational improvement.

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Paper Id: 232541

Published On: 2025-05-31

Published In: Volume 13, Issue 3, May-June 2025

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