<p>As AI technologies rise to prominence in consumers’ lives, marketing scholars and practitioners face an urgent need to understand how different consumer segments perceive, trust, and adopt these tools. However, current literature often treats consumer AI sentiment as monolithic, and practical guidance for addressing diverse consumer concerns remains sparse. To address this gap, we introduce the RISE framework—organized around Relational meaning differences, In-context segmentation, Skepticism-usage paradox, and Equitable AI integration—as a diagnostic lens for understanding AI adoption heterogeneity. We offer RISE not as a grand theory but as pre-theoretic diagnostic scaffolding: each dimension classifies the type of heterogeneity problem researchers or managers face, determining which mechanisms to investigate and which strategic levers to pull. Drawing on illustrative evidence from a U.S. census-representative survey (<i>n</i> = 2,144), we document patterns that motivate each framework dimension and articulate twelve formal propositions (P1–P12) that provide specific, testable entry points for future research. We develop a structured research agenda comprising twelve priority research questions with specific methodological recommendations and boundary conditions. We also include a managerial diagnostic appendix with 25 strategic questions that practitioners can use to assess their customer base on each RISE dimension. By mapping these heterogeneous patterns, our research provides a nuanced perspective on AI acceptance and offers implications for both marketing theory and practice, including guidance on building consumer trust and tailoring AI solutions to diverse needs across consumer segments.</p>