Algorithm Aversion in Internal Investment Decision-Making

As AI becomes increasingly embedded in business decision-making, understanding how and when people choose to trust algorithmic advice is critical. This new report, Algorithm Aversion in Internal Investment Decision-Making, explores why employees often hesitate to use AI - even when it outperforms human judgment.

Led by researchers from Ruhr University Bochum, the study investigates how decision context (human vs. non-human) and the type of algorithm explanation (technical vs. general) influence adoption. Through a series of controlled experiments, the findings reveal that algorithm use is significantly higher in non-human-related decisions, and that general benefit explanations are more effective than technical ones in encouraging adoption - especially in human-centric tasks like hiring.

The report offers practical guidance for organisations aiming to shift from algorithm aversion to algorithm appreciation, helping finance professionals unlock the full potential of AI in strategic decision-making.