The human resource analytics (HRA) market is experiencing robust growth, with a compound annual growth rate of 12.9 per cent. It is projected to attain a global market value of USD 9 billion by 2030. This paper presents a systematic literature review tracing the evolution of HRA adoption over the past decade, critically assessing the value HRA has generated, the increase in organisational investments in HRA during this period, and its impact on human capital.  The findings reveal several salient implications for the implementation of human resource analytics (HRA), organisational effectiveness and the future of the workplace. In general, current HRA practices tend to focus on individual-level analyses at the expense of comprehensive organisational assessment, thereby limiting the broader strategic value of these initiatives. Moreover, the successful deployment of HRA is contingent upon three principal factors: the integrity and quality of the data, the sophistication of the analytical capability, and, finally, the adoption of a well-articulated strategic implementation framework. Additionally, the findings highlight substantial ethical concerns, including issues of algorithmic transparency and workplace datafication. Some of the gaps that continue to evolve are how to manage remote workers, addressing contextual factors such as technology and big data, and addressing environmental implications. This paper is relevant to understanding the gaps that persist in HRA adoption in today’s business context. The Topic becomes more relevant with the emphasis on organisations using data to develop insights and interventions that can create a competitive advantage for the business from a resource-based view. This research works to provide an accessible summary of how HRA has evolved, studies done, applications, opportunities and risks, trade-offs as HRA transitions into a framework where data is being used to create automated models for decision-making. The topic remains highly relevant in today’s context, as HRA systems have evolved, and there are still gaps in data quality, in the contextualization of how data is used, and in the ability to combine data with non-measurable factors to facilitate unbiased decision-making. Even with the advanced new applications that use AI and ML technology, the foundational layer of how data analytics applications form the base for these advanced tools makes this literature review relevant to the current business context.