Despite the promised potential of artificial intelligence (AI), insights into real-life human-AI hybrids and their dynamics remain obscure. Based on digital trace data of over 1.4 million forecasting decisions over a 69-month period, we study the implications of an AI sales forecasting system's introduction in a bakery enterprise on decision-makers' overriding of the AI system and resulting hybrid performance. Decisionmakers quickly started to rely on AI forecasts, leading to lower forecast errors. Overall, human intervention deteriorated forecasting performance as overriding resulted in greater forecast error. The results confirm the notion that AI systems outperform humans in forecasting tasks. However, the results also indicate previously neglected, domain-specific implications: As the AI system aimed to reduce forecast error and thus overproduction, forecasting numbers decreased over time, and thereby also sales. We conclude that minimal forecast errors do not inevitably yield optimal business outcomes when detrimental human factors in decision-making are ignored.
Language
English
Keywords
Machine learning
sales forecasting
AI-assisted decision making
digital trace data
reliance
longitudinal
HSG Classification
contribution to scientific community
Book title
Proceedings of the 29th Americas Conference on Information Systems