The role of AI in learning from innovation failures: A way to succeed or a source of failures?

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Introduction

This special issue focuses on the role that artificial intelligence (AI) may have in both its potential to help in learning from failures and its capacity to cause failures inadvertently. Failures have always been a main part of innovation, mainly when working with breakthrough innovations as also AI can be (Freisinger & McCarthy, 2024; Hu et al., 2024; Sedkaoui and Benaichouba, 2024; Yu et al., 2024). Both companies and scientific partners, such as universities and research centers, frequently encounter failures throughout their innovation processes (Greco et al., 2022). The absence of a clear definition of innovation failure within a company, the absence of metrics to measure a failure and the need to demonstrate the creation of relevant outcomes sometimes hinder talking and analysing failures. Thus, everyone is always prone to celebrate the market leaders who have changed the world often forgetting noteworthy flops. (Baxter et al., 2023). In the context of AI, this can be even truer because AI-related innovations are highly complex, the measure of the outcome can be particularly difficult, data can contain biases or errors and algorithms can lead to overconfidence.  

Since the 1960s, it has been demonstrated that failure is deemed to be common with products and services accounting for rates at around 40% (Markham and Lee, 2013, Cozijnsen et al., 2000). Consequently, research spurs firms to learn from failure to innovate more effectively (Cannon and Edmondson, 2005; Khanna et al., 2016; Leoncini, 2016; Maslach, 2016; Ponta et al., 2024). It is widely established within management research that failure may be generatively framed in a learning process, specifically labelled as “learning-by-failing” (Van der Panne et al., 2003). Thus, learning from or by failure has been demonstrated to differ from learning by success and different learning processes can be set up. Furthermore, the learning-by-failure process leads companies to several benefits. Edmondson (2023, p. 25) quotes a surgeon from a team pioneering already in the 1950s: “In medicine, we learn more from our mistakes than from our success. Error exposes truth.” This quote underscores how innovation failure is often an important source of information. Examining how firms seek to learn from the experience has encountered similar reasoning such as “studying failures is an opportunity or a precursor to future success” (Rhaiem and Amara, 2021, p.189), and “within some failures lie the seeds of subsequent project success” (Shepherd et al., 2009 p. 589). Specifically, learning from failures may help to avoid harmful failures from occurring (Woolthuis et al., 2005; Välikangas et al., 2009). Then, as shown by Leoncini (2016), learning from failure leads to later improvements in the percentage of turnover from new products, showing that the learning from failure process acts as a spur to further innovative activity. In addition, failure increases a firm’s knowledge stock and allows for questioning existing routines. In this context, Vinck (2017) in his review concludes that “In innovation too the study of failure should be encouraged as it is likely to stimulate new modelling and theorization”, (Vinck, 2017, p.235). 

Nowadays, this new model and theory cannot be thought without embodying the concept of AI which offers powerful tools to analyze, predict, and mitigate failures but at the same time it can itself be considered as a possible source of failures.  

Specifically, on one hand, AI may help to develop models to uncover and depict patterns and factors that may contribute to failures, it may help to prevent or early detect failures, it may support the creation of a system to support decision-making in innovation management and it may sustain organizations in learning from past failures and integrate these lessons into future innovation strategies (Burger et al., 2023; Haefner et al., 2021).  

However, on the other hand, as for most new technologies, several issues can arise leading to a waste of time, money, and effort. For example, it may be difficult to integrate AI into existing processes, it may be difficult to manage possible errors because of unknown paths due to the novelty of the technology and several ethical issues are emerging. This would also lead to possible failures (Steiber, & Alvarez, 2024). 

This special issue aims to explore how AI can be utilized in the context of innovation failures to detect failures, develop predictive models, and design strategies to reduce the risk of failure in future innovation efforts.  At the same time, it aims to explore the drawbacks of AI itself, which in turn could undermine the innovation process.  

List of Topic Areas

In sum, studies on all the two sides - beneficial and harmful – of AI are welcome. We seek contributions that cover a broad range of topics related to the application of AI in understanding and addressing innovation failures, including but not limited to: 

• Theoretical advancements on the role of AI as an instrument to address innovation failures 
• Theoretical advancements on the role of AI as a cause of innovation failures 
• The Predictive role of AI in innovation failure 
• AI as an instrument to uncover patterns and factors that contribute to innovation failures. 
• AI as Decision Support Systems in innovation management failures 
• AI as a Learning Systems that help organizations to learn from past failures and integrate these lessons 
• Investigation of the effects of AI on the organization  
• Ethical Considerations about using AI in the context of innovation failures. 
• Ways to mitigate the AI innovation failures 
• Comparative studies of AI applications in innovation failure analysis across different contexts 
• Emerging AI technologies and methodologies that could further enhance the understanding and mitigation of innovation failures. 

Submissions Information

Submissions are made using ScholarOne Manuscripts. Registration and access are available here.
Author guidelines must be strictly followed. Please see here.
Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to ““Please select the issue you are submitting to”. 
Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.

Key Deadlines

Open January 31st 2025.
Close September 30th 2025.

Guest Editors

Gloria Puliga – Liuc Università Carlo Cattaneo 
Valentina Lazzarotti – Liuc Università Carlo Cattaneo 
Raffaella Manzini – Liuc Università Carlo Cattaneo 
Simonetta Primario – Università degli Studi di Napoli – Federico II 
Gillian Barrett - University College Cork 
Fazel Keshtkar - St. John’s University 

References

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