Details

Autor(en) / Beteiligte

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

Titel

Artificial Intelligence and Marketing: Pitfalls and Opportunities

Ist Teil von

• Journal of interactive marketing, 2020-08, Vol.51 (1), p.91-105

Ort / Verlag

Los Angeles, CA: Elsevier Inc

Erscheinungsjahr

2020

Link zum Volltext

Quelle

EBSCOhost Business Source Ultimate

Beschreibungen/Notizen

• 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. •What differentiate AI from traditional statistical techniques is the autonomous discovery of higher-order constructs.•A black box that remains impervious to knowledge transfer will continue to pose significant threats and challenges.•Such challenges include specification of objective functions, biased or unexplainable AI, and the automation paradox.•These challenges will plague marketing to a great extent because of the ubiquitous role of tacit knowledge in said domains.