  Companies dealing with Artificial Intelligence (AI) models in Autonomous
Systems (AS) face several problems, such as users' lack of trust in adverse or
unknown conditions, gaps between software engineering and AI model development,
and operation in a continuously changing operational environment. This
work-in-progress paper aims to close the gap between the development and
operation of trustworthy AI-based AS by defining an approach that coordinates
both activities. We synthesize the main challenges of AI-based AS in industrial
settings. We reflect on the research efforts required to overcome these
challenges and propose a novel, holistic DevOps approach to put it into
practice. We elaborate on four research directions: (a) increased users' trust
by monitoring operational AI-based AS and identifying self-adaptation needs in
critical situations; (b) integrated agile process for the development and
evolution of AI models and AS; (c) continuous deployment of different
context-specific instances of AI models in a distributed setting of AS; and (d)
holistic DevOps-based lifecycle for AI-based AS.
