Artificial intelligence (AI) has disrupted the way organizations do business and changed the facet of many firms´ activities. Thanks to the incredibly fast advancements in AI, companies have started to employ AI not only for the automation of routinized activities, but also as a support tool for strategic managerial decisions (Wilson and Daugherty, 2018) – for instance in the context of investments and innovation management. By leveraging AI, companies can facilitate and accelerate managerial decision-making, while at the same time taking advantage of its capability to process and synthetize a large volume of data. Despite the rapid improvements and increases in accuracy that many AI and similar algorithm-based solutions have displayed, AI is still imperfect and requires human judgment and expertise to fully unleash its potential (Choudhury, Starr, and Agarwal, 2020). Hence, the collaboration between managers and AI seems to represent the most promising avenue for the use of AI in organizations (De Cremer and Kasparov, 2021). However, before companies introduce expensive AI tools to improve decision-making processes, it is crucial for them to understand under which conditions managers rely on the support of AI to make their decisions.

Last week, Jelena Cerar, Chiara Fabrizi and Phillip C. Nell have presented their latest work “When do Managers Rely on AI? Cognitive Exhaustion and AI Advice in Innovation Selection Processes” at the prestigious LUISS Business School in Rome. In their study, the authors conduct a field experiment with real managers of an Austria-headquartered MNC, to investigate the role of cognitive exhaustion on the manager´s reliance on AI advice for innovation-related decisions.

Whether you are a manager using AI for making decisions, a firm considering investing in AI solutions, or simply someone interested in this topic, keep posted to find out the results of this study.

We are thankful to eXplore! for supporting our work and helping us advance the research in this field.

Cited articles:

Choudhury, P., Starr, E., & Agarwal, R. (2020). Machine learning and human capital complementarities: Experimental evidence on bias mitigation. Strategic Management Journal, 41(8), 1381-1411.

De Cremer, D., & Kasparov, G. (2021). AI should augment human intelligence, not replace it. Harvard Business Review.