  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.
