2020/06/28 by Arnaud De Bruyn, Vijay Viswanathan, Yean Shan Beh +2 · 6 citations
Decision Sciences · Computer Science · #Stock Market Forecasting Methods #Forecasting Techniques and Applications #Explainable Artificial Intelligence (XAI)
paper · doi:10.1016/j.intmar.2020.04.007
openalex publication_date 2020/06/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This article discusses the pitfalls and opportunities of AI in marketing through the lenses of knowledge creation and knowledge transfer. First, we discuss the notion of “higher-order learning” that distinguishes AI applications from traditional modeling approaches, and while focusing on recent advances in deep neural networks, we cover its underlying methodologies (multilayer perceptron, convolutional, and recurrent neural networks) and learning paradigms (supervised, unsupervised, and reinforcement learning). Second, we discuss the technological pitfalls and dangers marketing managers need to be aware of when implementing AI in their organizations, including the concepts of badly defined objective functions, unsafe or unrealistic learning environments, biased AI, explainable AI, and controllable AI. Third, AI will have a deep impact on predictive tasks that can be automated and require little explainability, we predict that AI will fall short of its promises in many marketing domains if we do not solve the challenges of tacit knowledge transfer between AI models and marketing organizations.