Marketing professionals’ adoption of artificial intelligence and its influence on marketing efficiency

Purpose: Artificial intelligence (AI) has transformed marketing operations, creating new benchmarks for operational productivity, customer interaction and sales growth. This study investigates factors that affect the adoption of AI among marketing professionals, focusing on developing benchmarking archetypes and assessing the moderating impact of technology resistance (TR). Design/methodology/approach: Data from 353 marketing professionals across diverse sectors in Sri Lanka was analyzed using a dual-method approach. The UTAUT2 model guided hypotheses tested with PLS-SEM to establish generalizable benchmarks, while fuzzy-set qualitative comparative analysis (fsQCA) was employed to identify distinct adoption archetypes serving as configurational benchmarks. Findings: All the UTAUT2 factors significantly influence AI adoption, with TR as a substantial barrier. The fsQCA revealed seven distinct benchmarking archetypes, with behavioral intention, effort expectancy, facilitating conditions, hedonic motivation and price value emerging as core conditions for high adoption, while performance expectancy, social influence and habit functioning as peripheral factors. Practical implications: The research provides diagnostic benchmarking tools that organizations can use to assess their AI readiness, identify implementation pathways aligned with their contextual characteristics, reduce technology resistance and enhance marketing efficiencies. Originality/value: This study advances benchmarking literature by identifying both generalizable adoption drivers and distinct configurational archetypes for AI implementation in marketing while establishing technology resistance as a critical moderating variable.