INTEGRATION OF KNOWLEDGE FROM COMMERCIAL MARKETING INTO AI‑AUGMENTED POLITICAL MARKETING: ANALYTICAL FRAMEWORK AND LITERATURE SYNTHESIS

Abstract

This article synthesizes the literature on how techniques and capabilities developed in commercial marketing travel into AI‑augmented political marketing. We group evidence into three channels of transfer—direct adoption (e.g., micro‑segmentation, A/B testing, attribution modeling), adaptation under political constraints (compliance, transparency, content governance), and capability spillovers (data infrastructures and generative content pipelines). Using a scoping review across political communication, marketing, and information systems, we map: (1) the technical repertoire (targeting, recommendation, predictive modeling, generative systems), (2) organizational enablers (data governance, absorptive capacity, boundary‑spanning teams), and (3) outcomes and risks (effectiveness, measurement validity, bias, and normative implications). We find consistent evidence for capability diffusion but methodologically heterogeneous findings on persuasive impact and causal attribution. We propose a typology of transfer mechanisms and a research agenda prioritizing external validity, auditability, and cross‑jurisdictional comparisons. The contribution is integrative—bridging siloed strands—and programmatic, by outlining standards for transparency, reporting, and evaluation pertinent to AI‑enhanced political campaigns.