  In the ever-expanding landscape of Artificial Intelligence (AI), where
innovation thrives and new products and services are continuously being
delivered, ensuring that AI systems are designed and developed responsibly
throughout their entire lifecycle is crucial. To this end, several AI ethics
principles and guidelines have been issued to which AI systems should conform.
Nevertheless, relying solely on high-level AI ethics principles is far from
sufficient to ensure the responsible engineering of AI systems. In this field,
AI professionals often navigate by sight. Indeed, while recommendations
promoting Trustworthy AI (TAI) exist, these are often high-level statements
that are difficult to translate into concrete implementation strategies. There
is a significant gap between high-level AI ethics principles and low-level
concrete practices for AI professionals. To address this challenge, our work
presents an experience report where we develop a novel holistic framework for
Trustworthy AI - designed to bridge the gap between theory and practice - and
report insights from its application in an industrial case study. The framework
is built on the result of a systematic review of the state of the practice, a
survey, and think-aloud interviews with 34 AI practitioners. The framework,
unlike most of those already in the literature, is designed to provide
actionable guidelines and tools to support different types of stakeholders
throughout the entire Software Development Life Cycle (SDLC). Our goal is to
empower AI professionals to confidently navigate the ethical dimensions of TAI
through practical insights, ensuring that the vast potential of AI is exploited
responsibly for the benefit of society as a whole.
