International Journal on Science and Technology

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The Evolution of AI Workflow Automation: From Rules to Adaptive Intelligence

Author(s) Samuel Tatipamula
Country United States
Abstract The transition from rule-based automation to adaptive intelligence represents a fundamental reimaginingof workflow automation in enterprise environments. Traditional rule-based systems, while effective forstructured tasks with predictable inputs, encounter significant limitations when confronting ambiguity,unstructured data, and evolving business requirements. This creates an "automation ceiling" that constrainsdigital transformation initiatives. By contrast, adaptive intelligence systems leverage deep learning, transferlearning, and continuous adaptation to handle ambiguous inputs, learn from minimal examples, andimprove over time through operational feedback. The most effective implementations combine thesecapabilities through thoughtful human-AI collaboration frameworks that dynamically allocate tasks basedon confidence levels and continuously learn from human decisions. Case studies in financial services andhealthcare demonstrate substantial improvements in both efficiency and effectiveness through this hybridapproach. Despite compelling benefits, successful implementation requires addressing challenges inexplainability, data governance, and integration with legacy systems through strategic planning andorganizational change management.
Keywords Adaptive Intelligence, Automation Ceiling, Human-Ai Collaboration, Machine Learning, Workflow Optimization
Field Computer
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-04-11
Cite This The Evolution of AI Workflow Automation: From Rules to Adaptive Intelligence - Samuel Tatipamula - IJSAT Volume 16, Issue 2, April-June 2025. DOI 10.71097/IJSAT.v16.i2.3531
DOI https://doi.org/10.71097/IJSAT.v16.i2.3531
Short DOI https://doi.org/g9fcfv
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