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Innovating by prompting: How to facilitate innovation in the age of generative AI

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  • Sundberg, Leif
  • Holmström, Jonny

Abstract

This article focuses on how recent advances in artificial intelligence (AI), particularly chatbots based on large language models (LLMs), such as ChatGPT, can be used for innovation purposes. The article begins with a brief overview of the development and characteristics of generative AI (GenAI). Elaborating on the implications of GenAI, we provide examples to demonstrate four mechanisms of LLMs: translation, summarization, classification, and amplification. These mechanisms inform a framework that highlights how LLMs enable the creation of innovative solutions for organizations through capacities in two dimensions: context awareness and content awareness. The strength of LLMs lies in the combination of capacities in both these dimensions, which enables them to comprehend and amplify content. Four managerial suggestions are presented, ranging from starting out with small-scale projects and data exploration, to scaling through integration efforts and educating prompt engineers. By presenting the framework, recommendations, and examples of use cases in various contexts, the article contributes to the emerging literature on GenAI and innovation.

Suggested Citation

  • Sundberg, Leif & Holmström, Jonny, 2024. "Innovating by prompting: How to facilitate innovation in the age of generative AI," Business Horizons, Elsevier, vol. 67(5), pages 561-570.
    • Handle: RePEc:eee:bushor:v:67:y:2024:i:5:p:561-570
    DOI: 10.1016/j.bushor.2024.04.014
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    References listed on IDEAS

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    8. Hannigan, Timothy R. & McCarthy, Ian P. & Spicer, André, 2024. "Beware of botshit: How to manage the epistemic risks of generative chatbots," Business Horizons, Elsevier, vol. 67(5), pages 471-486.
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    Cited by:

    1. Ramaul, Laavanya & Ritala, Paavo & Ruokonen, Mika, 2024. "Creational and conversational AI affordances: How the new breed of chatbots is revolutionizing knowledge industries," Business Horizons, Elsevier, vol. 67(5), pages 615-627.
    2. Osadchaya, Elena & Marder, Ben & Yule, Jennifer A. & Yau, Amy & Lavertu, Laura & Stylos, Nikolaos & Oliver, Sebastian & Angell, Rob & Regt, Anouk de & Gao, Liyu & Qi, Kang & Zhang, Will Zhiyuan & Zhan, 2024. "To ChatGPT, or not to ChatGPT: Navigating the paradoxes of generative AI in the advertising industry," Business Horizons, Elsevier, vol. 67(5), pages 571-581.
    3. Brewer, Jordan & Patel, Dhru & Kim, Dennie & Murray, Alex, 2024. "Navigating the challenges of generative technologies: Proposing the integration of artificial intelligence and blockchain," Business Horizons, Elsevier, vol. 67(5), pages 525-535.
    4. Abdelhalim, Esraa & Anazodo, Kemi Salawu & Gali, Nazha & Robson, Karen, 2024. "A framework of diversity, equity, and inclusion safeguards for chatbots," Business Horizons, Elsevier, vol. 67(5), pages 487-498.
    5. Retkowsky, Jana & Hafermalz, Ella & Huysman, Marleen, 2024. "Managing a ChatGPT-empowered workforce: Understanding its affordances and side effects," Business Horizons, Elsevier, vol. 67(5), pages 511-523.
    6. Hannigan, Timothy R. & McCarthy, Ian P. & Spicer, André, 2024. "Beware of botshit: How to manage the epistemic risks of generative chatbots," Business Horizons, Elsevier, vol. 67(5), pages 471-486.
    7. Feng, Cai (Mitsu) & Botha, Elsamari & Pitt, Leyland, 2024. "From HAL to GenAI: Optimizing chatbot impacts with CARE," Business Horizons, Elsevier, vol. 67(5), pages 537-548.
    8. Cui, Yuanyuan (Gina) & van Esch, Patrick & Phelan, Steven, 2024. "How to build a competitive advantage for your brand using generative AI," Business Horizons, Elsevier, vol. 67(5), pages 583-594.

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