Investing in AI in your company is more than developing cool algorithms, it´s about balancing your efforts between data engineering, model development and AI operations. This book explains how to successfully operate AI from a life cycle perspective including key aspects for effectively navigating security and data rights, but also the importance of trustworthiness and ethical considerations in AI. An overview of the AI model life cycle - this part explains the total scope of what the AI investment needs to cater for and why The importance of efficient Data Engineering - without data the AI model can´t run - still companies invest much less time and money on managing their data, than they do on developing new algorithms AI model development from an operational perspective - although AI models are developed and trained in the lab, that´s not where they will generate value. This part will explain how to be more successful by adopting an operational approach already in the AI model development phase. Operating AI is different from operating SW - AI is built on continuous learning and therefore another operational support model is needed. The feedback loop becomes fundamental, along with highly automated monitoring of model performance and data quality. Resilient and secure AI solutions - addressing security aspects in your AI architecture should be considered from the get-go AI is all about Trust - what does it mean to build trustworthy AI solutions and to operate your AI solutions in a reliable manner Operationalizing a Business Model - how do you industrialize your AI business idea (including learnings from all previous chapters) - moving it from the concept phase, through exploration and development to finally being able to operate it in a live setting (internal or commercial).