Artificial Intelligence in E-Business Process Automation: A Systematic Literature Review
Abstract
Abstract: Within today's data-intensive business environment, artificial intelligence has become central to process automation, yet scholarly work on this transformation remains dispersed across unrelated fields. Accounting researchers seldom engage with marketing automation literature; e-commerce scholars rarely consult compliance studies.
Forty articles indexed in Scopus and Web of Science (2020–2025) were selected using the PRISMA search protocol. The guiding question asks how AI pipelines change the way e-businesses operate and which design choices matter most for success. Workflow setup, system connections, and regulatory compliance appear most often in the sample.
The analysis indicates that orchestration techniques developed for machine learning operations transfer effectively to accounting, tax processing and customer service functions. When people review the outputs before final use, fewer errors reach production. Under the EU AI Act, high-risk systems must keep audit logs and explain their decisions. Smaller firms face a choice between building tools in-house or buying them, and technical skills and data control often tip the balance. Adoption maturity varies by function: content production and customer service automate faster than accounting or regulatory compliance, where error tolerance remains low.
What remains unclear is whether the money spent on automation generates measurable savings.
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