Author(s): Amit Sareen, Amit SS Jain,
Digital platforms have transformed the definition of value creation by transforming user interaction into data feedback loops that are self-reinforcing, otherwise called Data Network Effects (DNEs). Although the concept has gained increasing academic interest, the studies concerning the mechanisms and conditions that empower DNEs have been disjointed. A total of fifty-five high-quality publications are incorporated in this study in order to bring together the enablers that support the formation and scalability of DNEs in AI-driven platform ecosystems. A two-stage Desk/Documentary Delphi and Analytic Hierarchy Process (AHP) analysis, consequent to a systematic literature review based on the Theory-Context-Methodology (TCM) and the Antecedents-Decisions-Outcomes (ADO) frameworks, was carried out. The Delphi stage distilled consensus on six key enablers data stewardship and quality, algorithmic capability, governance and complementor strategy, feedback tightness and latency, user trust and overload, and leadership orientation while the AHP stage prioritised their relative importance. Results show that high data quality, adaptive algorithms, and sound governance structures are the most influential drivers of sustainable DNEs. These findings extend platform and resource-based theories by showing how learning from data, rather than network size alone, drives competitive advantage in digital ecosystems. The study contributes methodological novelty through an evidence-based Delphi–AHP framework that blends conceptual synthesis with analytical validation. Managerially, it offers a prioritised roadmap for platform leaders to invest in data governance, algorithmic transparency, and organisational readiness for AI-enabled growth.