Generative AI applications like ChatGPT demonstrate how large language models can quickly and cheaply perform some tasks that only humans could do before. Organizations might see an opportunity to use this technology to automate knowledge work, but implementing it comes with practical challenges that still require skilled employees involvement. This suggests that while newer AI tools might be better equipped to handle some tasks, they are unlikely to reshape organizations reliance on humans.

There is a substantial gap between the promise and reality of artificial intelligence in human resource (HR) management. This article identifies four challenges in using data science techniques for HR tasks: complexity of HR phenomena, constraints imposed by small data sets, accountability questions associated with fairness and other ethical and legal constraints, and possible adverse employee reactions to management decisions via data-based algorithms. It then proposes practical responses to these challenges based on three overlapping principles-causal reasoning, randomization and experiments, and employee contribution-that would be both economically efficient and socially appropriate for using data science in the management of employees.