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Innovation analytics: Leveraging artificial intelligence in the innovation process

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  • Kakatkar, Chinmay
  • Bilgram, Volker
  • Füller, Johann

Abstract

Artificial intelligence (AI) is about imbuing machines with a kind of intelligence that is mainly attributed to humans. Extant literature—coupled with our experiences as practitioners—suggests that while AI may not be ready to completely take over highly creative tasks within the innovation process, it shows promise as a significant support to innovation managers. In this article, we broadly refer to the derivation of computer-enabled, data-driven insights, models, and visualizations within the innovation process as innovation analytics. AI can play a key role in the innovation process by driving multiple aspects of innovation analytics. We present four different case studies of AI in action based on our previous work in the field. We highlight benefits and limitations of using AI in innovation and conclude with strategic implications and additional resources for innovation managers.

Suggested Citation

  • Kakatkar, Chinmay & Bilgram, Volker & Füller, Johann, 2020. "Innovation analytics: Leveraging artificial intelligence in the innovation process," Business Horizons, Elsevier, vol. 63(2), pages 171-181.
    • Handle: RePEc:eee:bushor:v:63:y:2020:i:2:p:171-181
    DOI: 10.1016/j.bushor.2019.10.006
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    References listed on IDEAS

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    1. Paschen, Ulrich & Pitt, Christine & Kietzmann, Jan, 2020. "Artificial intelligence: Building blocks and an innovation typology," Business Horizons, Elsevier, vol. 63(2), pages 147-155.
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    3. Kakatkar, Chinmay & Spann, Martin, 2019. "Marketing analytics using anonymized and fragmented tracking data," International Journal of Research in Marketing, Elsevier, vol. 36(1), pages 117-136.
    4. Kaminski, Jermain & Hopp, Christian & Tykvová, Tereza, 2019. "New technology assessment in entrepreneurial financing – Does crowdfunding predict venture capital investments?," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 287-302.
    5. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
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