A consensus is beginning to emerge that the next phase of artificial intelligence (AI) induction in business organizations will require humans to work with AI in a variety of work arrangements. This article explores the issues related to human capabilities to work with AI. A key to working in many work arrangements is the ability to delegate work to entities that can do them most efficiently. Modern AI can do a remarkable job of efficient delegation to humans because it knows what it knows well and what it does not. Humans, on the other hand, are poor judges of their metaknowledge and are not good at delegating knowledge work to AI—this might prove to be a big stumbling block to create work environments where humans and AI work together. Humans have often created machines to serve them. The sentiment is perhaps exemplified by Oscar Wilde’s statement that “civilization requires slaves…. Human slavery is wrong, insecure and demoralizing. On mechanical slavery, on the slavery of the machine, the future of the world depends.” However, the time has come when humans might switch roles with machines. Our study highlights capabilities that humans need to effectively work with AI and still be in control rather than just being directed.

Public transport operators (PTOs) increasingly face a challenging problem in switching from conventional diesel to more sustainable battery electric buses (BEBs). In this study, we optimize the opportunity fast-charging schedule of transit BEB networks in order to minimize the charging costs and the impact on the grid. Two mixed-integer linear programming (MILP) formulations that use different discretization approaches are developed and compared. Discrete-Time Optimization (DTO) resembles a time-expanded network that discretizes the time and decisions to equal discrete slots. Discrete-Event Optimization (DEO) discretizes the time and decisions into nonuniform slots based on arrival and departure events in the network. In addition to the DEO’s higher practicability, the comparative computational study carried out on the transit-bus network in the city of Rotterdam, Netherlands, shows that the DEO is superior to the DTO in terms of computational performance. To show the potential benefits of the optimal schedule, it is compared with two reference common-sense greedy strategies: First-in-First-Served and Lowest-Charge-Highest-Priority.

We develop a general-purpose agent-based simulation and modeling approach to analyze how user–recommender interactions affect recommender systems in the long run. Our explorations show that, over time, user–recommender interactions consistently lead to the longitudinal performance paradox of recommender systems. In particular, users’ reliance on recommendations, while helping users discover relevant items, actually hurts the future diversity of items that are recommended and consumed as well as slows down the system’s learning pace (i.e., the rate of predictive accuracy improvement). We also demonstrate unique benefits of certain hybrid consumption strategies—that is, that take advantage of both popularity- and personalization-based recommendations—in facilitating improvements in consumption relevance over time. Because users’ consumption strategies can significantly influence the longitudinal performance of recommender systems, it is important for designers to analyze the histories of a system’s recommendations and users’ choices to infer and understand users’ consumption strategies. This would enable the system to anticipate users’ consumption behavior and strategically adjust the system’s parameters according to its long-term performance objectives.