This study examines the effectiveness of adaptive machine translation (AMT) for gender-neutral language (GNL) use in English-German translation using the ModernMT engine. It investigates gender bias in initial output and adaptability to two distinct GNL strategies, as well as the influence of translation memory (TM) use on adaptivity. Findings indicate that despite inherent gender bias, machine translation (MT) systems show potential for adapting to GNL with appropriate exposure and training, highlighting the importance of customisation, exposure to diverse examples, and better representation of different forms for enhancing gender-fair translation strategies.