Artificial Intelligence and Management: The Automation-Augmentation Paradox

Source: Sebastian Raisch, Sebastian Krakowski; Academy of Management Review 46(1):192-210 · 2021

Introduction. Taking three recent business books on artificial intelligence (AI) as a starting point, we explore the automation and augmentation concepts in the management domain. Whereas automation implies that machines take over a human task, augmentation means that humans collaborate closely with machines to perform a task. Taking a normative stance, the three books advise organizations to prioritize augmentation, which they relate to superior performance. Using a more comprehensive paradox theory perspective, we argue that, in the management domain, augmentation cannot be neatly separated from automation. These dual AI applications are interdependent across time and space, creating a paradoxical tension. Overemphasizing either augmentation or automation fuels reinforcing cycles with negative organizational and societal outcomes. However, if organizations adopt a broader perspective comprising both automation and augmentation, they could deal with the tension and achieve complementarities that benefit business and society. Drawing on our insights, we conclude that management scholars need to be involved in research on the use of AI in organizations. We also argue that a substantial change is required in how AI research is currently conducted in order to develop meaningful theory and to provide practice with sound advice.

The rise of powerful AI will be either the best or the worst thing ever to happen to humanity. We do not yet know which. —Stephen Hawking, theoretical physicist (University of Cambridge, 2016) What all of us have to do is to make sure we are using AI in a way that is for the benefit of humanity, not to the detriment of humanity. —Tim Cook, CEO of Apple (Byrnes, 2017) Artificial intelligence (AI) refers to machines performing cognitive functions that are usually associated with human minds, such as learning, interacting, and problem solving (Nilsson, 1971). Organizations have long used AI-based solutions to automate routine tasks in operations and logistics. Recent advances in computational power, the exponential increase in data, and new machine-learning techniques now allow organizations to also use AIbased solutions for managerial tasks (Brynjolfsson & McAfee, 2017). For example, AI-based solutions now play important roles in Unilever’s talent-acquisition process (Marr, 2018), in Netflix’s decisions regarding movie plots, directors, and actors (Westcott Grant, 2018), and in Pfizer’s drug discovery and development activities (Fleming, 2018). In the 1950s, pioneering research predicted that AI would become essential for management (Newell, Shaw, & Simon, 1959; Newell & Simon, 1956). However, initial technological progress was slow and the discussion of AI in management was “effectively liquidated” in the 1960s (Cariani, 2010: 89). Scholars subsequently adopted a contingency view: The routine operational tasks that machines could handle were separated from the complex managerial tasks reserved for humans. Consequently, AI was researched in computer science and operations research, whereas organization and management studies focused on humans (Rahwan et al., 2019; Simon, 1987). Management scholars have therefore provided very little insight into AI during the last two decades (Kellogg, Valentine, & Christin, 2020; Lindebaum, Vesa, & den Hond, 2020). Nonetheless, these scholars’ understanding will be required, because AI is becomingincreasinglypervasiveinmanagerialcontexts. In this review essay, we strive to reposition AI at the crux of the management debate. Three highly influential business books on AI (Brynjolfsson & McAfee, 2014; Daugherty & Wilson, 2018; Davenport & Kirby, 2016) serve as a source of inspiration to challenge our thinking and spark new ideas in the management field (Bartunek & Ragins, 2015). The three books have developed a common AI narrative for practicing managers. The authors distinguishedtwobroadAIapplicationsinorganizations: automation and augmentation. Whereas automation implies that machines take over a human task, augmentation means that humans collaborate closely with machines to perform a task. Taking a normative stance, the authors accentuated the benefits of augmentation while taking a more negative viewpoint on automation. Their combined advice was that organizations should prioritize augmentation, which the authors related to superior performance. In addition, the two more recent books (Daugherty & Wilson, 2018; Davenport & Kirby, 2016) provided managers with ample advice on how to develop and implement such an augmentation strategy. Assuming a more encompassing paradox theory perspective (Schad, Lewis, Raisch, & Smith, 2016; Smith & Lewis, 2011), we argue that augmentation cannot be neatly separated from automation in the management domain. These dual AI applications are interdependent across time and space, which creates a paradoxical tension.

Related work. We started with a review of three recent business books on the use of AI in organizations. While there are many other books on this topic, we selected the following three, which have been widely influential in managerial practice, filling the void arising from the lack of scholarly research. The New York Times bestseller The Second Machine Age by MIT Professors Erik Brynjolfsson and Andrew McAfee (Brynjolfsson & McAfee, 2014) was called “the most influential recent business book” in a memorandum that Harvard Business School’s dean sent to the senior faculty (Economist, 2017). The muchdebated (Press, 2016) Only Humans Need Apply is the latest book on AI by Babson Professor Thomas H. Davenport and Harvard Business Review Contributing Editor Julia Kirby (Davenport & Kirby, 2016). Finally, the recently published Human 1 Machine by Accenture leaders Paul R. Daugherty and H. James Wilson (Daugherty & Wilson, 2018) has had an immediate impact on both academia and practice (Wladawsky-Berger, 2018). Collectively, the three books have suggested that we are on the cusp of a major transformation in business, comparable to the industrial revolution in scope and impact. During this “first machine age,” which started with the invention of the steam machine in the eighteenth century, mechanical machines enabled mass production by taking over manual labor tasks at scale. Today, we face an analogous inflection point of unprecedented progress in digital technology, taking us toward the “second machine age” (Brynjolfsson & McAfee, 2014: 7). Instead of performing mechanical work, machines now take on cognitive work, which was traditionally an exclusively human domain. However, machines still have many limitations, which means we are entering an era in which the human–machine relationship is no longer dichotomous, but evolving into a machine “augmentation” of human capabilities. Rather than being adversaries, humans and machines should combine their complementary strengths, enabling mutual learning and multiplying their capabilities. Instead of fearing automation and its effects on the labor market, managers should acknowledge that AI has the potential to augment, rather than replace, humans in managerial tasks (Davenport & Kirby, 2016: 30–31). Building on this analysis, the three books have advised organizations to focus on augmentation rather than on automation. The two more recent books explicitly related such an augmentation strategy to superior firm performance. For example, Daugherty and Wilson (2018: 214) concluded that companies using “AI to augment their human talent (. . .) achieve step gains in performance, propelling them to the forefront of their industries.”

Method. THE AUTOMATION–AUGMENTATION PARADOX Taking the three books as a starting point, we use a paradoxtheoryperspective(Schadetal.,2016;Smith& Lewis,2011)toexploreorganizations’useofAIfurther. A paradoxlensallows usto elevatethelevel of analysis to study both automation and augmentation, which reveals a paradoxical tension between these dual AI applications in management. Following the Smith and Lewis (2011) paradox framework, we will analyze the paradoxical tension, the management strategies used to address it, and their outcomes.

Paradoxical Tension The three books described the relationship between automation and augmentation as a trade-off decision: Organizations attempting to use AI have the choice of either automating the task or using an augmentation approach. If they opt for automation, humans hand over the task to a machine with little or no further involvement. The objective is to keep humans out of the equation to allow more comprehensive, rational, and efficient processing (Davenport & Kirby, 2016: 21). In contrast, augmentation implies continued close interaction between humans and machines. This approach allows for complementing a machine’s abilities with humans’ unique capabilities, such as their intuition and common-sense reasoning (Daugherty & Wilson, 2018: 191f). The nature of the task determines whether organizations opt for one or the other approach. Relatively routine and well-structured tasks can be automated, while more complex and ambiguous tasks cannot, but can be addressed through augmentation (Brynjolfsson & McAfee, 2014: 138ff; Daugherty & Wilson, 2018: 107ff; Davenport & Kirby, 2016: 34ff). The arguments that the books have provided are difficult to refute, but their perspective is largely limited to a given task at a specific point in time. Paradox theory, however,warnsthatsuchanarrowtrade-offperspective does not adequately represent reality (Smith & Lewis, 2011). A paradox lens can help increase the scale or level of analysis for a more systemic perspective (Schad & Bansal, 2018), which allows organizations to perceive not only the contradictions but also the interdependenciesbetweenautomationandaugmentation.Amore comprehensive paradox perspective (both–and) then replacesthetraditionaltrade-offperspective(either–or). The essence of paradox is that the dual elements are both contradictoryandinterdependent—forming a persistent tension (Schad et al., 2016). Automation and augmentation are contradictory, because organizations choose either one or the other approach to address a given task at a specific point in time. This choice creates a tension, since these AI approaches rely on competing logics with different organizational demands. For example, Lindebaum et al. (2020) maintained that automation instills a logic of formal rationality in organizations that conflicts with the logic of substantive rationality, or the human capacity for value-rational reflection, whereas augmentation preserves this substantive rationality. The tension is further reinforced because some organizational actors prefer augmentation (e.g., managers at risk of losing their jobs to automation) while others prioritize automation (e.g., owners interested in efficiencies) (Davenport & Kirby, 2016: 61). While these contradictions are real, they only reveal a partial picture. If we increase our analysis’s temporal scale (from one point in time to its evolution over time) and spatial scale (from one to multiple tasks), we comprehend that, in the management domain, the two AI applications are not only contradictory but also interdependent. Increasing the temporal scale. Taking a process view of paradox reveals a cyclical relationship between opposing forces (Putnam, Fairhurst, & Banghart, 2016; Raisch, Hargrave, & van de Ven, 2018). Engagement with one side of the tension may set the stage, or even create the conditions necessary, for the other’s existence; in addition, over time there is often a mutual influence between the opposing forces, with swings from one side to the other (Poole & van de Ven, 1989).

Discussion. Inspired by three recent business books, we have explored the emergent use of AI-based applications in organizations to automate and augment managerial tasks. The central argument of this review essay is that automation and augmentation are not only separable and conflicting but in fact fundamentally interdependent. We suggest that the prevailing trade-off perspective is overdrawn; viewed as a paradox, augmentation is both the driver and outcome of automation, and the two applications of AI develop and fold into one another across space and time. The automation–augmentation model’s popularity stems largely from its clear boundaries and simplicity. However, adopting this intuitively appealing model uncritically obscures how automation and augmentation intertwine in managerial practice. This review essay therefore sheds light on automation and augmentation’s complementarities and identifies opportunities to transcend the paradoxical relationship between them. In the following, we discuss our reflections’ implications for organization and management research. We argue that the ways in which scientific research on AI is conducted need to change in order to accurately capture and analyze its organizational and societal implications for managerial practice. Our discussion will be loosely structured along the famous “5w & 1h” questions (addressed in order here as who, how, what, why, where, and when).12 Our analysis of the automation–augmentation paradox suggests that the jury is out on whether the use of AI in management will turn out to be a blessing or a curse. Scholars can still make a difference by exploring the topic and informing practice regarding the ways forward. However, this will require a change in who conducts research on AI. Currently, computer scientists, roboticists, and engineers are the scholars most commonly studying AI. Their primary objective is to automate as far as possible, because they often regard humans as “a mere disturbance in the system that can and should be designed out” (Cummings, 2014: 62). Computer scientists may be expert technologists, but they are generally not trained social or behavioral scientists (Rahwan et al., 2019). Instead, they tend to use laboratory settings for their research, which reduce the inherent variability that characterizes human behavior. These settings permit the use of methodologies aimed at maximizing algorithmic performance, but disregard the role of humans and the wider organizational and societal implications.

The three business books’ augmentation perspective reveals the importance of inducing social scientists to participate in the debate. With respect to managerial tasks, humans will remain “in the loop” and interact closely with machines. Management scholars are particularly well-equipped to study these human–machine interactions in real-world settings, as well as to explore the organizational and societal implications thereof. It is therefore no wonder that the three business books devote the bulk of their attention to augmentation. However, our analysis suggests that management research limited to augmentation may be as biased as computer science’s traditional focus on automation. Research on AI in management would therefore benefit greatly from more interdisciplinary efforts. The technological and the social worlds are merging (Orlikowski, 2007), which means that computer scientists and management scholars no longer study separate phenomena. By juxtaposing and integrating their different perspectives, theories, and methodologies, computer scientists and management scholars can jointly create a foundation for meaningful research on the use of AI in management. Such efforts also require a change in how research on AI is conducted in the management domain.

Conclusion. While such meso-level topics will likely play a central role in future research, organizations’ use of AI in management should also be explored on the micro and macro levels of analysis. Micro-level research could investigate how the emergence of AI-based solutions changes the role of managers in organizations. In the past, management theories emphasized managers’ domain expertise, which granted them expert power and status in their organizations (Finkelstein, 1992). Although domain expertise remains relevant for managers regarding educating and challenging machines, automation and augmentation will lead to institutionalized knowledge—for example, in the form of algorithms— which is often superior to individual managers’ expert knowledge. At the same time, general human skills that complement machines, such as creativity, common sense, and advanced communication (Davenport & Kirby, 2016: 30), as well as integration skills such as AI literacy, will gain further importance in an era of automation and augmentation (Daugherty & Wilson, 2018: 191). These developments could lead to important shifts in managers’ roles, competencies, and status. Macro-level research could explore how the emergence of automation and augmentation in management leads to institutional action and change. For example, AI is often applied in open systems, blurring organizational boundaries (Panait & Luke, 2005). Data are collectedwidely,withdiversestakeholdersupdatingthem continuously and collectively through their actions (Gregory et al., 2020). Inputs from agents within and outsidetheorganizationtherebyimpacttheautomation and augmentation process, which, in turn, can have wide-reaching societal implications. A core focus of management scholars’ future research attention should therefore be on studying how broader networks of actors, comprising activists, companies, governments, international organizations, and public institutions, collaborate to set standards, build institutions, and organize collective action to address issues pertaining to the use of AI in management.