The rapid evolution of AI has created a wealth of possibilities for businesses, e.g. to enhance their operations, improve their innovativeness and advance customer interaction. Technology departments are under pressure to adopt AI in their enterprise architecture. The immense power and readily available services offered by big tech platforms seems make design, implementation and operation of AI applications, including machine learning and deep learning, seamless. The paradigm of Machine Learning Operations (MLOps) emerged to develop ML products and rapidly bring them into production at industrial scale. It has been found that DevOps teams can contribute to firm competitive advantage by building both business and technology-related capabilities which enable them to sense market opportunities, make fast and targeted decisions and transform their assets in case of changing circumstances. While increasingly popular, MLOps has shown to be difficult. Many ML initiatives fail to provide value, while many ML models never reach production. This study surveys challenges of AI adoption and discusses a framework based approach to facilitate the adoption and scaling of AI in a tech firm. The paper concludes that while a framework based approach does eliviate some adoption challenges, much research remains to be done. Such research challenges are presented and discussed.