Emerging systems involving humans and machines use data analytics as
subcomponents to help prioritize people's energy.  Whether considering
information retrieval, human resource actions, urban planning, or child welfare
protection, the goal is to use machine learning algorithms to rank tasks in
order of value using a noisy set of predictive features.  The ranked list is
then used for sequential selection by human practitioners.  This paper develops
a stochastic model of analytics-enabled sequential selection, derives
fundamental limits using the theory of concomitants of order statistics, and
assesses limits in terms of system-wide performance metrics like screening
effort and value of objects selected.  Connections to sample complexity results
for bipartite ranking are also developed.