Designing AI-Ready Enterprise Platforms under Regulatory Constraints

Within modern social practices, artificial intelligence has been adopted mostly in the corporate world. Efforts to that end have been held back in places where institutions expressly require that distinct norms, values, and even structures of control should be in existence. This article shall present a framework, for instance, that informs the capability of an enterprise to embed artificial intelligence into its core business processes in a manner that meets the technical requirements of artificial intelligence adoption as well as meets the necessary requirements of the regulatory landscape. The work also goes far to dissect a group of rules (GDPR, financial services rules and principles, among others) whose analysis helps to understand the level of control needed for any given type of AI system and in this sense narrows down the most important aspects of technology transfer at the platform level. Our study identifies that regulated AI platforms in compliance burdens need to include the following aesthetics for their development: clear data lineage tracking or understanding, decision making which is explainable or with reasons, systems which have governance structure in them, compliance that is automatically controlled, and also always full trace of evidence available for example compliance. We terminologically divide these features into four classes, as incorporated into platform infrastructure or regarded as issues at the application level. Theories of regulation support this research in that regulatory constraints can both aid and hinder AI projects.