Robo-advisors in financial services: Redefining wealth management in the age of artificial intelligence

• Following are the major highlights of our study on different forms of robo advisors and how they influence investment decisions • Develops a conceptual model of robo-advisors grounded in Dubin’s (1978) theory-building framework, to clarify how robo-advisors influence investor objectives and financial inclusion. • Introduces a novel 3-by-3 matrix that systematically maps types of robo-advisors—i.e. Wealth Management Firm Robo-Advisor (WMRA), Regulator Robo-Advisor (RRA), and Personal Robo-Advisor (PRA)—to three expected outcomes i.e. return maximization, risk minimization, and financial inclusion. • Examines both individual-user and institutional-level moderators that shape the effectiveness and outcomes of robo-advisor adoption. • Provides actionable insights for researchers, policymakers, managers and, regulators, to support the usage of robo-advisors among investors and design digital, automated and inclusive financial ecosystems. • Identifies key interaction mechanisms—such as trust, literacy, and compliance orientation—that moderate user engagement with robo-advisors. • Establishes a research agenda that guides future empirical validation of digital advisory models. In recent years, the delivery and accessibility of investment advisory services have been redefined through the integration of Financial Services and Artificial Intelligence (AI). The present study develops a structured conceptual model, grounded in Dubin’s (1978) framework on theory-building, to understand how distinct institutional designs of robo-advisors: Wealth Management Firm Robo-Advisors (WMRAs), Regulatory Robo-Advisors (RRAs), and Personal Robo-Advisors (PRAs), contribute to three core outcomes: return maximization, risk minimization, and financial inclusion. These outcomes form the heart of the conceptual model, highlighting how institutional form determines strategic value. The present study emphasizes the role of behavioral and contextual mechanisms, including trust, digital competence, compliance orientation, and perceived security, as well as moderators such as platform type and institutional constraints, rather than focusing solely on technological effectiveness. By integrating these mechanisms with institutional design and market conditions, the framework examines the interplay between robo-advisor structures and their outcomes. The paper introduces a temporal perspective, i.e., how regulation and user competence change over time through feedback loops, and clearly specifies situations in which the proposed relationships may fail or be empirically disproven. The study serves as a foundation for empirical testing and experimental studies, with significant implications for policy design, innovation, and regulatory governance in financial advisory services.