The hidden barrier to AI success: Trusted data foundations

Abstract

Artificial intelligence (AI) has reached a critical inflection point in financial services. Despite its extraordinary promise, most AI initiatives in post-trade operations struggle to move beyond experimentation. This paper sets out to address that gap within post-trade operations, where accuracy, predictability, and control are non-negotiable. It examines why AI initiatives stall, identifies the conditions required for scalable adoption, and outlines how companies can move from isolated pilots to repeatable, production-grade deployment. Readers will gain a clear understanding of where AI adds genuine value, particularly across exceptions management, reconciliation, allocations, and trade enrichment. The paper also introduces a best practice centred on data readiness, governance, and decision transparency, ensuring every AI-driven outcome is traceable and controlled. By the end, readers will be equipped with practical guidance to define high-value use cases, strengthen data foundations, and implement AI in a way that delivers measurable, compliant, and sustainable results across post-trade functions. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.