<p>This study advances the understanding of artificial intelligence (AI) integration in marketing by examining the organizational and technological barriers that shape social media campaign performance in the Fast-Moving Consumer Goods (FMCG) sector. Using a sequential mixed-methods research (MMR) approach that combines expert interviews with a large-scale survey, we investigate how these barriers influence key marketing performance indicators (KPIs)—reach, engagement, and conversion. The findings reveal that AI-driven personalization and real-time adaptability improve marketing outcomes, but their impact is limited by poor data quality, algorithmic bias, and organizational constraints. Effects are KPI-specific: reach and engagement remain robust, whereas conversion is highly sensitive—enhanced by high absorptive capacity but hindered by technological and organizational barriers. Furthermore, firm size and digital maturity amplify AI’s effectiveness, enabling larger and digitally advanced FMCG companies to derive disproportionately greater benefits. Theoretically, this study contributes by integrating the Dynamic Capabilities and Absorptive Capacity frameworks, along with insights from the Perceived Relevance Model, into a unified model, offering a novel explanation of how AI adoption affects differential KPI-level outcomes. Practically, the findings provide actionable guidance for FMCG marketers, emphasizing AI transparency and real-time data integration to maximize social media campaign performance.</p>