Machine learning (ML) has become essential to a vast range of applications, while ML experts are in short supply. To alleviate this problem, AutoML aims to make ML easier and more efficient to use. Even so, it is not clear to which extent AutoML techniques are actually adopted in an engineering context, nor what facilitates or inhibits adoption. To study this, we define AutoML engineering practices, measure their adoption through surveys, and distil first insights into factors influencing adoption from two initial interviews. Depending on the practice, results show around 20 to 30% of the respondents have not adopted it at all and many more only partially, leaving substantial room for increases in adoption. The interviews indicate adoption may in part be inhibited by usability issues with AutoML frameworks and the increased computational resources needed for adoption.