Advanced algorithms, including systems identified as artificial intelligence (AI), promise to increase productivity by harnessing computers and data for increasingly complex tasks, faster and cheaper than humans (McAfee & Brynjolfsson, 2014). While AI gained notoriety during recurring hypes accompanied by waves of bankruptcies in the 1970s, 1980s, and 1990s, billions of dollars were invested into AI-based start-ups in 2015. The current crop of artificial intelligence companies all exploits big data, processing gigantic datasets through various analytics technologies, most prominently deep learning. With merely a hint of irony, AI and more generally smart algorithms really look to be for real this time around. While the most obvious application of advanced algorithms is to replace human talent with cheaper computers, more interesting developments involve entirely new functionalities built around humongous data sets that humans could never process. It is time for organization theorists to attend to the potent effects that big data is having on organizing and management (Gerry & Lin, 2017). Strategy and information systems scholars have examined how consumer data shape strategy and business models (Constantiou & Kallinikos, 2015; Zuboff, 2015). Economists have identified AI as a general purpose technology set to shape large number of industries (McAfee & Brynjolfsson, 2014). This will influence work and the value of skills (Autor, 2015), with predictions of polarization in productivity so that consequently average is over (Cowen, 2013). Data, it is argued, push organizations to become more responsive and more interconnected. Yet, these technologies can also directly shape the internal workings of organizations and management practice (Huber, 1990; Zammuto, Griffith, Majchrzak, Dougherty, & Faraj, 2007). Big data enable organizations to more reliably predict and control their key processes. While initial big data applications were technical (e.g. optimizing material flows or targeting of advertising), new applications relate now to organizing and work. As a consequence, algorithmic management of work is emerging inside and between firms (Lee, Kusbit, Metsky, & Dabbish, 2015). The term captures the new reality where algorithms track the performance of employees or contractors, optimizing decisions concerning their tasks and future employment. Algorithms are taking over scheduling work in fast food restaurants and grocery stores, using various forms of performance metrics and even mood, for example to assign the fastest employees to work in peak times. In algorithmic management, computers do not facilitate governance (as in evidence-based management), but instead governance itself is made obsolete through total control provided by data (Zuboff, 2015). This is already evident in gig economy firms, such as Uber and Deliveroo, where algorithms make underperforming employees/ contractors redundant automatically without human involvement. This algorithmic management, or Scientific Management 2.0, if you wish, shifts power from a hierarchy of managers to larger cadres of professionals who master analytics, programming, and business. Management is no longer a human practice, but a process embedded in technology. Consequently, organizational learning will increasingly be embodied in IT-driven processes. Required: 1. Critically discuss algorithmic management and its implications relating to organizational structure of business organizations. (300 words minimum)