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.

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We gather detailed data on organizational practices and IT use at 253 firms to examine the hypothesis that external focus -the ability of a firm to detect and therefore respond to changes in its external operating environment -increases returns to information technology, especially when combined with decentralized decision-making. First, using survey-based measures, we find that external focus is correlated with both organizational decentralization and IT investment. Second, we find that a cluster of practices including external focus, decentralization and IT is associated with improved product innovation capabilities. Third, we develop and test a 3-way complementarities model that indicates that the combination of external focus, decentralization and IT is associated with significantly higher productivity in our sample. We also introduce a new set of instrumental variables representing barriers to IT-related organizational change and find that our results are robust when we account for the potential endogeneity of organizational investments. Our results may help explain why firms that operate in information-rich environments such as high-technology clusters or areas with high worker mobility have experienced especially high returns to IT investment and suggest a set of practices that some managers may be able to use to increase their returns from IT investments.

T his paper uses newly collected panel data that allow for significant improvements in the measurement and modeling of information technology (IT) productivity to address some longstanding empirical limitations in the IT business value literature. First, we show that using generalized method of moments-based estimators to account for the endogeneity of IT spending produces coefficient estimates that are only about 10% lower than unadjusted estimates, suggesting that the effects of endogeneity on IT productivity estimates may be relatively small. Second, analysis of the expanded panel suggests that (a) IT returns are substantially lower in midsize firms than in Fortune 500 firms; (b) they materialize more slowly in large firms-in midsize firms, unlike in larger firms, the short-run contribution of IT to output is similar to the long-run output contribution; and (c) the measured marginal product of IT spending is higher from 2000 to 2006 than in any previous period, suggesting that firms, and especially large firms, have been continuing to develop new, valuable IT-enabled business process innovations. Furthermore, we show that the productivity of IT investments is higher in manufacturing sectors and that our productivity results are robust to controls for IT labor quality and outsourcing levels.