"It is there, and you need it, so why do you not use it?" Achieving better adoption of AI systems by domain experts, in the case study of natural science research

TL;DR

This paper investigates why domain experts in natural science research often reject AI systems and offers practical recommendations to improve adoption and effective human-AI collaboration.

Contribution

It provides empirically grounded guidelines for increasing AI adoption among natural scientists through targeted support and communication strategies.

Findings

Supporting experts during initial use increases acceptance

Clear communication of AI capabilities enhances trust

Following collaboration rules improves system integration

Abstract

Artificial Intelligence (AI) is becoming ubiquitous in domains such as medicine and natural science research. However, when AI systems are implemented in practice, domain experts often refuse them. Low acceptance hinders effective human-AI collaboration, even when it is essential for progress. In natural science research, scientists' ineffective use of AI-enabled systems can impede them from analysing their data and advancing their research. We conducted an ethnographically informed study of 10 in-depth interviews with AI practitioners and natural scientists at the organisation facing low adoption of algorithmic systems. Results were consolidated into recommendations for better AI adoption: i) actively supporting experts during the initial stages of system use, ii) communicating the capabilities of a system in a user-relevant way, and iii) following predefined collaboration rules. We discuss the broader implications of our findings and expand on how our proposed requirements could support practitioners and experts across domains.