Human-Centered AI Enhances Performance Through Tailored Interaction

GSEM Professor Sebastian Raisch, Sebastian Krakowski, Darek Haftor, Johannes Luger, and Natallia Pashkevich, co-authored an article published in the top-tier journal Management Science. It investigates how humans and AI collaborate on unstructured managerial tasks in a multinational pharmaceutical firm. The researchers adjusted AI implementation—work procedures, decision-making authority, training, and incentives—to match employees’ cognitive styles, classified as adaptors or innovators.

Results show that tailoring AI interactions to cognitive styles significantly improves sales performance, while untailored interactions can reduce performance due to workflow disruptions and role conflicts. Tailored interactions also increase AI system usage, partially explaining the performance gains. These findings highlight that effective human-AI collaboration requires aligning AI systems with human cognitive needs and the work context.

The authors benefited from the support of the Swiss National Science Foundation, the Erling Persson Family Foundation, and the Marianne and Marcus Wallenberg Foundation.

ABSTRACT

Humans and artificial intelligence (AI) algorithms increasingly interact on unstructured managerial tasks. We propose that tailoring this human-AI interaction to align with individuals’ cognitive preferences is essential for enhancing performance. This hypothesis is examined through a field experiment in a multinational pharmaceutical firm. In the experiment, we manipulated four contextual parameters of human-AI interaction—work procedures, decision-making authority, training, and incentives—to align with sales experts’ cognitive styles, categorized as either adaptors or innovators. Our results show that tailored interaction significantly improves sales performance, whereas untailored interaction results in negative treatment effects compared with both the tailored and control conditions. Qualitative evidence suggests that this negative outcome arises from role conflicts and ambiguities in untailored interaction. Exploring the mechanisms underlying these outcomes further, a mediation analysis of AI login data reveals that human-AI interaction tailoring leads sales experts to adjust their AI utilization, which contributes to the observed performance outcomes. These findings support a human-centered approach to AI that prioritizes individuals’ information-processing needs and tailors their interaction with AI accordingly.

Access the study: Human-Centered Artificial Intelligence: A Field Experiment

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