Abstract
The adoption of advanced technology is considered a source of competitive advantage over companies with slower technology assimilation capabilities. Early adoption of emerging technologies and tools is paramount, especially in business-critical functions such as R&D and product development. This seems especially important in the case of artificial intelligence (AI), a powerful new technology with the potential to affect not only a firm’s product portfolio but also the very development processes required to create products. However, recent surveys suggest that firms differ significantly in their ability to adopt AI, despite strong pre-existing technological competencies and clear business needs.
Recent literature has established the potential of AI adoption not only in management (Kemp, 2023) but also in traditionally core human abilities such as creativity and innovation (Amabile, 2020). AI was initially considered to be useful only in more limited application domains, e.g., Verganti et al. (2019) focused on AI-empowered customer data collection and how it may improve design practices. Theory contributions conceptualizing AI as building blocks in the product developing innovation process have emerged only recently. For instance, Haefner et al. (2021) and Kakatkar et al. (2020) investigated how AI capabilities help managers overcome front-end innovation constraints, and Füller et al. (2022) introduced a theoretical framework that discusses AI use cases at every stage of innovation process, including development and commercialization. In the same vein, Cooper and McCausland (2024) explored how AI can be incorporated in specific stages during the NPD process. Thus, while some attention has been paid to how adoption of AI can enhance firm innovation capabilities (Gamma and Magistretti, 2023), the enablers of integration of AI into the products through the innovation process have been subject to only minimal scrutiny. Adoption of emerging technologies such as AI does not happen in a vacuum (Füller et al., 2022) and must be supported by strategic, operational, and organizational factors (Maghazei et al., 2020). In their research, Verganti et al. (2019) called for further investigation on whether AI-innovation practices were appropriate in any organizational context, or whether they depended on company-specific factors, such as culture and strategy, and if they did, which strategies could be associated with higher likelihood of AI adoption for innovation (e.g., Füller et al., 2022). Thus, while previous research has focused on AI adoption in the process, similar research on the integration of AI into the product itself is still underdeveloped.
To investigate this research question, we use the results of the PDMA’s 2021 global NPD benchmarking survey, covering 651 responses from NPD and innovation managers on best practices in NPD and, among others, adoption of AI and other emerging technologies. Specifically, we investigated the relationship between self-declared innovation strategy (using the 1978 Miles & Snow classification) and AI adoption, extent of AI adoption in both product and process R&D, five different degrees of NPD formalization, and eventual performance of these firms in the marketplace.
Our preliminary analysis shows that more proactive innovation strategy is positively associated with a higher likelihood of integration of AI into the product through innovation, whereas, the relationship with process innovation is ambivalent. We further test the effects of contextual characteristics such as degree of formalization of the innovation process, orientation towards new market and technologies, risk-taking on AI adoption for process. We also suggest that adoption of other emerging technologies such as additive manufacturing, simulation tools, augmented and virtual reality are drivers of AI adoption for process innovation.
With these results this paper contributes to the understanding of drivers of product innovation with emerging technologies. In this way, the research is casting new light on the recent upsurge of research on AI within innovation management. The paper concludes with discussions and implications for innovation managers.
Recent literature has established the potential of AI adoption not only in management (Kemp, 2023) but also in traditionally core human abilities such as creativity and innovation (Amabile, 2020). AI was initially considered to be useful only in more limited application domains, e.g., Verganti et al. (2019) focused on AI-empowered customer data collection and how it may improve design practices. Theory contributions conceptualizing AI as building blocks in the product developing innovation process have emerged only recently. For instance, Haefner et al. (2021) and Kakatkar et al. (2020) investigated how AI capabilities help managers overcome front-end innovation constraints, and Füller et al. (2022) introduced a theoretical framework that discusses AI use cases at every stage of innovation process, including development and commercialization. In the same vein, Cooper and McCausland (2024) explored how AI can be incorporated in specific stages during the NPD process. Thus, while some attention has been paid to how adoption of AI can enhance firm innovation capabilities (Gamma and Magistretti, 2023), the enablers of integration of AI into the products through the innovation process have been subject to only minimal scrutiny. Adoption of emerging technologies such as AI does not happen in a vacuum (Füller et al., 2022) and must be supported by strategic, operational, and organizational factors (Maghazei et al., 2020). In their research, Verganti et al. (2019) called for further investigation on whether AI-innovation practices were appropriate in any organizational context, or whether they depended on company-specific factors, such as culture and strategy, and if they did, which strategies could be associated with higher likelihood of AI adoption for innovation (e.g., Füller et al., 2022). Thus, while previous research has focused on AI adoption in the process, similar research on the integration of AI into the product itself is still underdeveloped.
To investigate this research question, we use the results of the PDMA’s 2021 global NPD benchmarking survey, covering 651 responses from NPD and innovation managers on best practices in NPD and, among others, adoption of AI and other emerging technologies. Specifically, we investigated the relationship between self-declared innovation strategy (using the 1978 Miles & Snow classification) and AI adoption, extent of AI adoption in both product and process R&D, five different degrees of NPD formalization, and eventual performance of these firms in the marketplace.
Our preliminary analysis shows that more proactive innovation strategy is positively associated with a higher likelihood of integration of AI into the product through innovation, whereas, the relationship with process innovation is ambivalent. We further test the effects of contextual characteristics such as degree of formalization of the innovation process, orientation towards new market and technologies, risk-taking on AI adoption for process. We also suggest that adoption of other emerging technologies such as additive manufacturing, simulation tools, augmented and virtual reality are drivers of AI adoption for process innovation.
With these results this paper contributes to the understanding of drivers of product innovation with emerging technologies. In this way, the research is casting new light on the recent upsurge of research on AI within innovation management. The paper concludes with discussions and implications for innovation managers.
| Original language | English |
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| Publication date | 2024 |
| Publication status | Published - 2024 |
| Event | R&D Management Conference 2024: Transforming Industries Through Technology - KTH Royal Institute of Technology, Stockholm, Sweden Duration: 17 Jun 2024 → 19 Jun 2024 https://rnd2024.org/ |
Conference
| Conference | R&D Management Conference 2024 |
|---|---|
| Location | KTH Royal Institute of Technology |
| Country/Territory | Sweden |
| City | Stockholm |
| Period | 17/06/2024 → 19/06/2024 |
| Internet address |
