The DOI matches its Crossref record; 102 of the 102 references it cites were found in a registry or an index; 31 of the 142 sources its claims cite were checked for support.
Artificial Intelligence and Management: The Automation–Augmentation Paradox
Abstract
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.
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Claims the paper makes
28 Valid · 5 Not valid · 83 Not checked · 108 Own results and interpretations
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The resulting tight coupling between humans and machines, with the two influencing one another, makes it increasingly difficult, or even impossible, to decouple their influence on the resulting model (Amershi et al., 2014).”
§ ARTIFICIAL INTELLIGENCE AND MANAGEMENT: THE AUTOMATION-AUGMENTATION PARADOX > Paradoxical Tension
What the sources say
- Partly supportedcites Amershi et al., 2014 sourceExperimental — accuracy not yet measured
Passage [2] says that rapid interaction cycles tightly couple the user and system, making their influence on the resulting model difficult to decouple. The Summary says it is impossible to study the system in isolation from the user, but does not say that decoupling their influence is impossible or becoming increasingly difficult. Neither passage has a page number in our copy. (LLM, conf=0.83)
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Humans, on the other hand, possess the capacity to transfer learning, allowing them to generalize from one task context to another (Parisi, Kemker, Part, Kanan, & Wermter, 2019).”
§ ARTIFICIAL INTELLIGENCE AND MANAGEMENT: THE AUTOMATION-AUGMENTATION PARADOX > Paradoxical Tension
What the sources say
- Supportedcites Parisi, Kemker, Part, Kanan, & Wermter, 2019 source
The Introduction states that humans can transfer knowledge across domains and tasks. Section 4.3 defines transfer learning as applying knowledge acquired in one domain to a problem in a new domain, supporting the claim's description of generalizing between task contexts. (LLM, conf=0.94)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Paradox theory, however, warns that such a narrow trade-off perspective does not adequately represent reality (Smith & Lewis, 2011).”
§ ARTIFICIAL INTELLIGENCE AND MANAGEMENT: THE AUTOMATION-AUGMENTATION PARADOX > Paradoxical Tension
What the sources say
- Supportedcites Smith & Lewis, 2011 source
In “Alternative Perspectives on Organizational Tensions: Beyond Contingency Theory,” the paper says that choosing between dualities can oversimplify complex, dynamic contexts, whereas paradox theory attends to persistent tensions. It also qualifies the point: a narrower choice-based approach can be valuable in a narrower context and shorter time horizon. (LLM, conf=0.89)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“According to paradox theory, these organizational responses fuel reinforcing cycles that can be either negative or positive (Smith & Lewis, 2011).”
§ ARTIFICIAL INTELLIGENCE AND MANAGEMENT: THE AUTOMATION-AUGMENTATION PARADOX > Management Strategies
What the sources say
- Supportedcites Smith & Lewis, 2011 source
In “Managing Tensions: Enabling Vicious and Virtuous Cycles,” the paper states that responses to paradoxical tensions fuel reinforcing cycles that can be negative or positive. This directly supports the citing sentence; the paper also discusses both vicious and virtuous cycles. (LLM, conf=0.95)
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Contrary to automation, augmentation demands continued human involvement and experimentation (Amershi et al., 2014).”
§ ARTIFICIAL INTELLIGENCE AND MANAGEMENT: THE AUTOMATION-AUGMENTATION PARADOX > Management Strategies
What the sources say
- Partly supportedcites Amershi et al., 2014 sourceExperimental — accuracy not yet measured
The Interactive Machine Learning section says users can steer models through rapid interaction cycles and experimentation, supporting continued human involvement in the systems the paper discusses. The paper does not establish the broader claim that augmentation, as opposed to automation, necessarily demands that involvement. (LLM, conf=0.78)
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
107 more claims · 7 with their cited sources checked
Create a free account to see all 224 claims and every source verdict.
- Probably not a substantial claim. 58
- It may state two separate claims as one. 67
- The quote only partly states this claim. 1
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Automation is not necessarily the opposite of human augmentation and can in fact foster augmentation (Raisch & Krakowski, 2020).”
From Artificial intelligence and knowledge management: A partnership between human and AI (2023) · cites this paper as Raisch & Krakowski, 2020
The cited source says what the claim attributes to it.
In the citing paper
“Indeed, scholars describe human-AI augmentation as an expansion of expertise or knowledge where humans and machines ' combine their complementary strengths ' to ' multiply their capabilities ' (Raisch and Krakowski 2021, p. 6).”
From To Engage or Not to Engage with AI for Critical Judgments: How Professionals Deal with Opacity When Using AI for Medical Diagnosis (2022) · cites this paper as Raisch and Krakowski 2021
The cited source says what the claim attributes to it.
In the citing paper
“Its vocabulary follows Raisch and Krakowski, who distinguish automation, in which the machine takes over a task, from augmentation, in which human and machine capabilities are combined by design, and theorize the two as interdependent rather than mutually exclusive [45].”
From Borrowed Competence: When AI-Driven HR Builds Startups’ People Capability and When It Hollows It Out (2026) · cites this paper as [45]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“It suggests that the LLM decision process, at least under the current prompt design and judge model, was able to maintain high decision quality across structures.”
From Semantic Camouflage in Artificial Organizations: A Real-LLM Multi-Agent Simulation of Information Distortion and Audit (2026) · cites this paper as Raisch & Krakowski, 2021
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Our review found that the adoption of AI within HRM processes and wider organisation will impact employees in different ways, such as job substitution, training needs, and uncertainty regarding roles and responsibilities (Frey & Osborne, 2017), limited understanding about how and why AI will be used (Raisch & Krakowski, 2020), trust and confidence (Gunning, 2017), formation of project teams where”
From Unlocking the value of artificial intelligence in human resource management through AI capability framework (2023) · cites this paper as Raisch & Krakowski, 2020
Experimental — accuracy not yet measured
2 more claims about this paper
Create a free account to see every claim other papers make about this one.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ ARTIFICIAL INTELLIGENCE AND MANAGEMENT: THE AUTOMATION-AUGMENTATION PARADOX > Paradoxical Tension
“To provide illustrations of such transitions from automation to augmentation, we briefly discuss two examples from managerial practice.”
§ ARTIFICIAL INTELLIGENCE AND MANAGEMENT: THE AUTOMATION-AUGMENTATION PARADOX > Paradoxical Tension
“After the initial augmentation stage, JP Morgan Chase decided to automate the candidate assessment task on the basis of the identified criteria.”
Citations
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Cited by
Mikalef, P., & Gupta, M. (2021) · Information & Management · doi:10.1016/j.im.2021.103434 · Cited by 1,700
Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A., & Truong, L. (2023) · Human Resource Management Review · doi:10.1016/j.hrmr.2022.100899 · Cited by 927
Dell'Acqua, F., McFowland III, E., Mollick, E. R., Lifshitz-Assaf, H., Kellogg, K., Rajendran, S., Krayer, L., Candelon, F., & Lakhani, K. R. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4573321 · Cited by 794
When and How Artificial Intelligence Augments Employee Creativity
Jia, N., Luo, X., Fang, Z., & Liao, C. (2024) · Academy of Management Journal · doi:10.5465/amj.2022.0426 · Cited by 768
Makarius, E. E., Mukherjee, D., Fox, J. D., & Fox, A. K. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2020.07.045 · Cited by 780
Metadata
- Authors
- Raisch, S., & Krakowski, S.
- Affiliations
- University of Geneva (CH)
- Stockholm School of Economics (SE)
- Venue
- Academy of Management Review
- Publisher
- Academy of Management
- Published
- 2021-01
- Volume
- 46
- Issue
- 1
- Pages
- 192-210
- ISSN
- 0363-7425 (ISSN-L)
- 1930-3807
- OpenAlex
- W4247155454
- Cited by
- 2,755
- References
- 95
- Citations by year
- 2026: 1391 · 2025: 586 · 2024: 318 · 2023: 235 · 2022: 156 · 2021: 65
- FWCI
- 117.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Academy of Management Review (publishedVersion)
- Funders
- National Science Foundation
- European Commission (856688)
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (185164, 181364)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Keywords
- artificial intelligence in organizations, automation, augmentation, paradox theory, human-machine collaboration, organizational performance, societal impact of AI
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- CoChat. (2026). Citation check: Artificial Intelligence and Management: The Automation–Augmentation Paradox. https://app.cochat.ai/papers/10.5465/amr.2018.0072
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- Raisch, S., & Krakowski, S. (2021). Artificial Intelligence and Management: The Automation–Augmentation Paradox. Academy of Management Review. https://doi.org/10.5465/amr.2018.0072
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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