Year: 2025
Type: article
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.
Author: Samuel Tatipamula -
Institution: Indian Institute of Technology Guwahati
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4409395067