Artificial Intelligence and Marketing: Pitfalls and Opportunities

Arnaud De Bruyn*, Vijay Viswanathan, Yean Shan Beh, Jürgen Kai Uwe Brock, Florian von Wangenheim

*Corresponding author for this work

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)91-105
Number of pages15
JournalJournal of Interactive Marketing
Volume51
DOIs
StatePublished - Aug 2020

ASJC Scopus subject areas

  • Business and International Management
  • Marketing

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