Evolution of digital transformation (DT) in the era of artificial intelligence (AI): defining DT 2.0 for AI-Human service ecosystems

Purpose Existing digital transformation (DT) frameworks treat technology as a passive tool, failing to explain AI-driven autonomous adaptation, agentic co-configuration and ecosystem orchestration. This paper aims to reconceptualize DT in hospitality by introducing Digital Transformation 2.0 (DT 2.0), distinguishing it from DT 1.0 and proposing a research agenda for artificial intelligence (AI)-orchestrated service ecosystems. Design/methodology/approach Through systematic synthesis of management, information systems and hospitality literature, the study develops a conceptual framework grounded in dynamic capabilities theory and service-dominant logic. Findings Grounded in dynamic capabilities theory and service-dominant logic, DT 2.0 is defined by three necessary and sufficient conditions: cognitive augmentation (AI that autonomously adapts strategies through continuous learning rather than executing pre-programmed rules), human–AI co-configuration (AI as operant resources that actively cocreate service design) and ecosystem-mediated co-creation (intelligent coordination enabling real-time value co-creation across organizational boundaries). Competitive advantage theory shifts from process efficiency (DT 1.0) to ecosystem position, algorithmic learning speed and human–AI complementarity (DT 2.0). Research limitations/implications This research advances DT theory by positioning cross-functional integration and governance as foundational DT 2.0 capabilities, theorizing a shift from fragmented, department-centric stacks to enterprise architectures that unify data, processes and decision rights across hospitality functions. The framework provides hospitality executive strategies for transitioning to AI-orchestrated ecosystems, embedding ethical governance, leveraging algorithmic learning and navigating ecosystem coordination challenges. Originality/value This paper addresses the gap that prior frameworks cannot capture AI’s autonomous, agentic and ecosystem-orchestrating capabilities. It introduces a theoretically grounded DT 2.0 definition specifying necessary and sufficient conditions that distinguish it from rule-based DT 1.0, positions CDR as a dynamic capability linking ethical AI governance to competitive advantage and offers a research agenda for the AI era.