AI-native as an integrative level of digital transformation in business process management

Abstract

The purpose of the study is to substantiate AI-native as an integrative level of business process digital transformation, at which separate artificial intelligence solutions cease to operate as isolated tools and begin to form a cross-cutting logic of process design, execution, monitoring, control and improvement. Methods. The study uses comparative conceptual analysis of recent studies and professional sources devoted to AI-native systems, AI-enabled solutions, AI-augmented business process management systems, intelligent business process management suites, algorithmic management, agentic business process management, artificial intelligence capability and digital transformation. The sources are grouped according to their common analytical position in order to show how different research lines gradually converge into a process-oriented interpretation of AI-native. Results. The article shows that the term AI-native originated mainly in computer and telecommunication discourse, where it denotes the architectural embeddedness of artificial intelligence into a system, product or workflow. In business process management, the concept obtains managerial meaning when artificial intelligence is embedded not in a single function but in the whole process logic. AI-enabled describes local functions, AI-augmented business process management systems explain the strengthening of process-aware systems, intelligent business process management suites represent the platform basis, algorithmic management covers automated managerial coordination, and agentic business process management explains autonomous process participants. AI-native does not replace these concepts; it integrates their separate effects into an end-to-end transformation of process logic. The article also identifies the main advantages of AI-native, including process architecture integrity, adaptability, preventive control, stronger process mining and feedback-driven improvement. At the same time, it outlines risks related to blurred responsibility, data dependence, algorithmic opacity, excessive automation, platform lock-in, ethical constraints and governance complexity. Conclusions. AI-native may be interpreted as an integrative level of business process digital transformation when artificial intelligence becomes a normal element of process logic rather than an external instrument. Its value for management lies in explaining the transition from fragmented automation to coordinated human-AI process governance.