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Paper info: Harnessing artificial intelligence in B2B marketing: A conceptual framework and managerial guidelines

Title


Harnessing artificial intelligence in B2B marketing: A conceptual framework and managerial guidelines

Authors


Joel Mero and Joona Keranen

Place of Publication


The paper was published at the 35th IMP-conference in Paris, France in 2019.

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Abstract


Artificial intelligence (AI) is one of the most disruptive forces in the B2B landscape. The promise of AI is fundamentally based on its ability to free employees from repetitive tasks to undertake more productive work and to enhance humans’ cognitive capabilities. In particular, AI helps firms in exploiting data for making predictions that can be used for creating more intelligent systems and making better business decisions. However, despite the lucrative opportunities, only a few companies have been able to transform their businesses to take a full advantage of AI. The rest, while making increasing investments into AI, are struggling to extract meaningful business benefits from their efforts. The purpose of this study is to explore how firms utilize AI in B2B marketing, and develop insights into the capabilities and strategies firms use to harness AI, and the potential business benefits they derive from AI-enhanced processes. We do this by reviewing the emerging literature on AI in B2B marketing, and subsequently, drawing empirical insights from a theoretically sampled multiple case study centred on B2B firms who are already using AI in their business operations. The findings from this study reveal distinct change pathways, capabilities, strategies, typical challenges and potential business benefits at different stages during the organizational transition towards AI-enhanced business operations. Based on the findings, this study develops an empirically grounded conceptual framework that illustrates; (i) a taxonomy of B2B marketing tasks that can be augmented by AI; (ii) capabilities and strategies needed to manage AI in different organizational processes; and (iii) potential benefits of AI-driven operation to different actors.