Artificial intelligence is transforming industrial marketing by reshaping processes, decision-making, and inter-firm relationships. However, research remains fragmented, with limited evidence on how adoption drivers create new capabilities and sustainability outcomes. This study develops and empirically validates an integrated framework that combines technology, organization, environment, user acceptance, resource-based perspectives, dynamic capabilities, and explainability. A convergent mixed-methods design was applied, combining survey data from industrial firms with thematic analysis of practitioner insights. The findings show that technological readiness, organizational commitment, environmental pressures, and user perceptions jointly determine adoption breadth and depth, which in turn foster marketing capabilities linked to measurable improvements. These include shorter quotation cycles, reduced energy consumption, improved forecasting accuracy, and the introduction of carbon-based pricing mechanisms. Qualitative evidence further indicates that explainability and human–machine collaboration are decisive for trust and practical use, while sustainability-oriented investments act as catalysts for long-term transformation. The study provides the first empirical integration of adoption drivers, capability building, and sustainability outcomes in industrial marketing. By demonstrating that artificial intelligence advances competitiveness and sustainability simultaneously, it positions marketing as a strategic lever in the transition toward digitally enabled and environmentally responsible industrial economies. We also provide a simplified mapping of theoretical lenses, detail B2B-specific scale adaptations, and discuss environmental trade-offs of AI use. Given the convenience/snowball design, estimates should be read as upper-bound effects for mixed-maturity populations; robustness checks (stratification and simple reweighting) confirm sign and significance.
Riti et al. (2025) studied this question.