A critical marketing analysis of AI hype

Artificial intelligence has become one of the most prominent themes in contemporary marketing discourse. Vendor narratives, consultancy reports and managerial communication frequently present AI as an inevitable source of radical transformation, superior efficiency and durable competitive advantage. This thesis critically examines the gap between those claims and the organizational practices through which AI is selected, interpreted, implemented and governed. The study asks what benefits AI can realistically bring to marketing processes and which organizational conditions determine whether those benefits are achieved. The research adopts a qualitative, interpretative and comparative design. It combines a critical review of academic and professional literature, qualitative content analysis, contextual semi-structured interviews and comparison between creative and technical organizational settings. The analytical framework brings together the resource-based view, dynamic capabilities and sociotechnical perspectives while also considering algorithm aversion, automation bias, transparency, privacy and accountability. The findings indicate that AI is most commonly used for bounded operational applications such as segmentation, forecasting, campaign optimization, recommendation and performance monitoring. Its contribution is generally incremental and depends on data quality, managerial capability, cross-functional coordination, strategic alignment and effective governance. The comparison also shows that sectoral value-creation logics matter: creative settings place greater emphasis on originality, interpretation and symbolic value, whereas technical settings tend to accept algorithmic support more readily when it aligns with measurable performance routines. In both contexts, however, human judgment remains central. The thesis therefore argues that AI-driven marketing should be treated as a sociotechnical practice rather than as an autonomous technological force. Sustainable value does not follow from adoption alone; it emerges when technological capabilities are integrated with organizational routines, professional expertise and accountable decision structures. The practical implication is a more selective, evidence-based and context-sensitive approach to AI investment and performance assessment.