TY - JOUR
T1 - Artificial Intelligence and Marketing
T2 - Pitfalls and Opportunities
AU - De Bruyn, Arnaud
AU - Viswanathan, Vijay
AU - Beh, Yean Shan
AU - Brock, Jürgen Kai Uwe
AU - von Wangenheim, Florian
N1 - Publisher Copyright:
© 2020 Marketing EDGE.org.
PY - 2020/8
Y1 - 2020/8
N2 - 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.
AB - 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.
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U2 - 10.1016/j.intmar.2020.04.007
DO - 10.1016/j.intmar.2020.04.007
M3 - Article
AN - SCOPUS:85087220792
SN - 1094-9968
VL - 51
SP - 91
EP - 105
JO - Journal of Interactive Marketing
JF - Journal of Interactive Marketing
ER -