Low-code AI enabling marketing innovation: The mediating role of marketing ambidexterity

Abstract In the digital era's dynamic markets, firms face pressure to rapidly identify and capture emerging marketing opportunities—a challenge compounded by the steep learning curves of conventional AI-driven marketing tools. Firms struggle to translate technological adoption into innovation outcomes due to persistent skill gaps and interdepartmental collaboration barriers—marketers lack coding expertise and IT teams prioritize technical perfection over market agility. Low-code AI emerges as a transformative solution by integrating modular prebuilt components with large language model-powered copilots, democratizing advanced capabilities such as training customer service chatbots and predicting market trends without requiring programming proficiency. Drawing on a survey of 251 marketers adopting LCAI across five Chinese industries, this study demonstrates how LCAI enhances marketing innovation performance through ambidextrous dynamic capabilities. It simultaneously enables firms to exploit existing marketing resources and explore novel strategies. Crucially, an innovation-oriented culture amplifies these effects: firms fostering experimental mindsets and tolerance for iterative refinement observe stronger improvements in both product innovation and process innovation. These findings validate the path from low-code AI to marketing ambidexterity and marketing innovation performance, contributing to the understanding of LCAI and its role in driving innovation, while quantifying the cultural contingencies shaping AI-driven innovation. For practitioners, the research provides actionable insights into leveraging LCAI to overcome technical-commercial tradeoffs, ultimately driving market growth in dynamic market environments.