AI-Powered Personalization at Work and in the Market:Examining the Convergence of Employee Experience and Customer Engagement

Abstract

Artificial intelligence (AI) has made individualized treatment economically feasible at population scale, yet organizations continue to build two separate personalization estates: one that tailors work to employees and another that tailors offers to customers. This paper argues that the separation is an artefact of organizational history rather than of technology, and that the two estates are converging on a common substrate of data, models and governance. Drawing on the service–profit chain, service-dominant logic, sociotechnical systems theory and the technology affordance perspective, the study develops and evaluates a Convergence Model in which AI personalization capability on the work side and on the market side jointly shape employee experience and customer engagement, which in turn accumulate into a firm-level property termed personalization convergence and, through it, into dual-sided value. The paper combines a structured synthesis of 118 peer-reviewed studies published between 2015 and 2025 with an illustrative demonstration on a calibrated simulated dataset (n = 612). Results support a reciprocal employee–customer spillover, identify algorithmic transparency and privacy concern as opposing boundary conditions, and show that convergence rather than raw capability is the stronger proximal driver of dual-sided value. The paper contributes a formal definition and measurement scheme for personalization convergence, a three-layer reference architecture, a five-stage maturity model, and a governance agenda that treats employees and customers as a single population of data subjects rather than two unrelated ones.