Purpose—This paper provides a conceptual framework, including factors to assess the readiness of AI in wealth and asset management companies. It aims to provide guidance for wealth and asset management companies regarding the use of artificial intelligence (AI) and key considerations when assessing usability. Furthermore, it seeks to sensitize data-related issues and foster an understanding of implementation frameworks considering AI systems. Design/methodology/approach—This paper is based on a critical analysis of the latest and prevailing literature regarding AI and its relevant enablers, in conjunction with wealth and asset management. It builds upon the literature findings and proposes a theoretical framework comprising basic factors to consider when introducing AI in wealth and asset management companies. Findings—The factors influencing AI adoption in wealth and asset management companies in this framework include clear intent, data management, regulatory certainty, efficiency enhancement and cost reduction, client experience, and engagement. As the topic is novel, this paper concludes that carefully assessing recent technologies is crucial to avoid poor investment decisions. Furthermore, it highlights that a clearly outlined intent for AI implementations is of utmost importance to make the best use of the AI application. The adoption of AI further deepens one’s possibility to train an in-house AI solution or, as opposed, choose already trained models, as dataset sources, quality, and training aspects of the AI workflow must also be considered. Research limitations/implications—While this research provides valuable insights into the conceptual applicability of AI in wealth and asset management, the fast developments of AI technologies require a constant update of the framework. The rapid development could also lead to the potential neglect of AI topics that might emerge in the future. Thus, this paper cannot be seen as conclusive and suggests further exploration of the identified factors and extending the framework to include additional factors. Practical Implications—It aims to provide guidance and enable firms to make educated decisions, functioning as a roadmap for the strategic adoption of AI to enhance competitiveness. It seeks to clarify the current use cases of AI and reduce uncertainties and speculations about its practical capabilities and its ability to foster transparency. It delves into the role played by big data and the greater volumes of information as enablers of AI applied to wealth and asset management. Furthermore, it provides a theoretical framework for AI implementation with foundational factors. Originality/value—This paper provides a specifically tailored conceptual framework for using AI technology in wealth and asset management. It synthesizes insights while applying them to practical developments. It further fosters the theoretical understanding of basic factors to be considered in AI implementations.