Industrial software ecosystems refer to a network of interdependent actors, co-creating value through a shared technological platform specifically tailored to industrial sectors. Developing, maintaining, and orchestrating such platforms involves many challenges that require complex decision making. Experimentation can help alleviate this complexity and reduce decision uncertainty and bias. However, experimentation requires certain organizational, infrastructural, and data-related prerequisites which can be uniquely challenging to achieve in industrial software ecosystems. Through semi-structured interviews with 25 industry professionals involved in various roles across 17 ecosystems, we analyze the difficulties faced in conducting effective experiments in such environments. The interview protocol covered aspects related to the methodologies, data handling processes, and current experimentation practices, as well as the challenges faced by practitioners who engage in experimentation initiatives. The study findings reveal technical, organizational, and market-related challenges, detailing the complexities facing experimentation initiatives in industrial software ecosystems. The findings are presented in an actionable manner, following a model that allows business-oriented alignment of architecture, process, and organizational evolution strategies. The study identifies key impediments, such as data integration difficulties, stringent regulatory environments, and prevailing organizational cultures that hinder continuous experimentation practices. Our analysis provides a foundation for understanding the unique challenges facing experimentation efforts in industrial software ecosystems and offers insights into potential strategies to improve the effectiveness of these initiatives.