Artificial Intelligence Technologies in Digital Marketing: Data Architecture, Personalisation Tools and Brand Customer Experience Management

Abstract

The article systematises key directions in the development of AI-driven marketing technologies — from automation of individual operations to integrated customer journey orchestration. A three-level maturity model is proposed (automation — intelligent decision support — orchestration), and a data architecture framework including customer data platforms (CDP) is described. Conversational AI, programmatic/DCO advertising, social commerce, user-generated content (UGC), and immersive technologies (AR/VR) are examined. Special attention is paid to the anticipated proliferation of AI agents and the transition from batch to real-time streaming data processing. Empirical cases of predictive scoring, chatbot deployment and AR virtual try-on are presented with quantitative outcomes. The AI-washing phenomenon is analysed, and international (NIST AI RMF, EU AI Act) as well as emerging Russian regulatory initiatives in AI governance are reviewed. The article concludes that sustainable competitive advantage stems not from a single technology but from a company’s ability to integrate analytics, personalisation and feedback into a unified managed system under responsible AI principles. The research is based on a systematic review of current scientific literature (2020–2026), cross-industry analysis of empirical cases, content analysis of industry reports (McKinsey, Gartner, Forrester), and a comparative analysis of international and Russian regulatory documents in the fi eld of AI. The proposed three-level maturity model provides a systemic benchmark for planning investments in marketing technologies, while the multi-level system of metrics makes it possible to avoid a situation where short-term growth in operational efficiency is achieved at the expense of customer experience degradation or accumulation of reputational risks.