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Why is the Influence of Artificial Intelligence (AI) at Work an Important Contemporary Topic?
Artificial intelligence (AI) is the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, natural language processing, and search & retrieval methods (Oxford, n.d.). AI is a subfield of computer science with significant influences from psychology and social science, and this subfield has been active since the 1950s. However, only since the early 2020s has the combination of advanced computer power and maturity of generative AI technologies allowed AI to enter the mainstream of society. Within a couple years, AI has grown from a niche field of study to full integration in the home and in the workplace with new applications coming out on a daily basis.
To say that AI is having a tremendous impact on the workplace is an understatement. The promises of these technologies include vast increases in productive efficiency, the automated routinization of simple tasks, the abilities to manage and analyze massive amounts of data, and support creative activities through AI-driven image, video, and music generation. Common use tools such as OpenAI’s ChatGPT have grown in number (e.g., Microsoft’s Co-Pilot, Google’s Gemini, and Anthropic’s Claude), as have specialized tools catering to specific purposes (e.g., Scite.ai for academics, DALL-E for high quality images, and Synthesia for videos). As these tools proliferate, managers have been looking to apply them in new ways. Virtually every organizational function could potentially see AI tools applied to them, which brings about concerns on the human dimension of the workplace and whether AI could eventually replace human workers.
Of course, the emergence and incorporation of new technologies has been studied for a long time. Our episodes 34 and 114 on Trist and Bamforth’s study on the introduction of the longwall method of coal-getting is but an early example. However, the speed of AI’s integration into the workplace and the fervency of manager’s desires to use it as a way of cutting costs is the most unique aspect of the present day situation. Researchers, policymakers, and many others in society have been barely able to keep up with the ongoing innovations and organizational changes. Of particular note is the impacts on higher education, as professors and administrators have been divided on whether and how to integrate AI usage into their programs — with responses ranging from prohibitions against usage to full embrace.
Obviously, this makes AI integration a ripe topic for research, although one may ask to what extent this area is becoming saturated, like a fad. Perhaps this merely reflects the immaturity of the discourse and it may require some stabilization in the technology before the true and latest impacts of the technology can become knowable.
This page begins with a brief history of the field of AI as a technology followed by a review of the study of its emerging impact on organizations. Relevant contemporary theories and areas of active research follow.
History of AI and the Study of AI at Work
What exactly is AI, anyway? Conceptions of artificial creations with intelligence date back to antiquity, such as the Greek myth of Talos, a bronze automaton that defended the island of Crete from invaders (Atsma, 2017). However, the conceptual birth of AI came from Alan Turing’s (1950) “Computing Machinery and Intelligence” (1950), which presented the Turing Test as a variety of the imitation game. If there were three separate rooms, one with a human, one with a computer, and one with an interrogator and the interrogator were to separately ask questions of the human and the computer without knowing which was which, could the interrogator deduce which was the human and which was the computer?
But the field of AI would take decades before taking off. Initial breakthroughs such as 1966’s ELIZA managed to perform some rudimentary tasks but the lack of computing power meant no follow-up. The field stagnated until the 1980s with the development of expert systems, rule-based technologies that attempted to mimic the diagnostic and treatment functions of experts. Again, scientists could not exploit the early successes into commercial viability, and the field once again stagnated. The current boom in AI began around the turn of the 21st century with the development of advanced machine learning capabilities that exploited the rapid increases in available computing power. Buoyed by the growth and proliferation of a large global network of data centers, developers have made rapid advances in AI capabilities. Natural language processing, image processing, and computer vision preceded today’s large language models and generative capabilities in text, audio, and video. Moreover, AI-based products moved from niche capabilities to today’s general-purpose tools.
History of AI in the workplace as a topic in organization studies. Because the phenomenon of AI at work is so new, it is useful to consider how information technologies (IT) more generally were integrated in the workplace over the past half-century. As personal computers entered workplaces, researchers began studying how information technology transformed work. Several literature streams emerged. Shoshana Zuboff’s (1988) In the Age of the Smart Machine (which we covered in Episode 96) introduced concepts of “informating” versus “automating” – distinguishing between technology that simply replaces human tasks versus technology that creates new information and requires new skills. Management information systems (MIS) research emerged separately, with scholars like Lynda Applegate (e.g., Applegate, Konsynski, & Nunamaker, 1986) and James Cash (e.g., Cash & McLeod, 1985) studying how IT changed organizational processes and structures. The concept of business process reengineering, popularized by Michael Hammer (e.g., Hammer, 1990) and James Champy (e.g., Hammer & Champy 2009), reflected growing recognition that technology required fundamental organizational redesign. Also in the 1990s, the field of knowledge management emerged as scholars like Ikujiro Nonaka (e.g., Nonaka, 1994) and Hirotaka Takeuchi (e.g., Nonaka, Takeuchi, & Umemoto, 1996) developing theories about organizational knowledge creation that would later inform AI adoption studies (Gemini, 2025).
Now, modern AI began entering the workplace in the late 2010s and 2020s due to the incredible gains made in generative AI capabilities. It proved to be very good at performing some non-routine tasks, as opposed to previous IT applications that focused on routine, replicable tasks or robotics that assumed physical tasks that were routine, repetitive, and required precision. The study of AI in the workplace began by examining its adoption and considering how such capabilities were being exploited to improve task performance that required some degrees of creativity or judgment. For example, scholars have examined the relationship between AI capability and organizational creativity, emphasizing the role of knowledge sharing within organizations (Li et al., 2022), and how AI can influence firm performance through transformation projects (Wamba-Taguimdje et al., 2020). Also, scholarly investigation into AI’s role has evolved to encompass the dynamics of organizational culture and change. Bilan et al. (2022) conducted a systematic review that highlights the increasing relevance of AI technologies in organizational management and development, asserting that the maturation of AI signals not merely a technological shift but a broader cultural transformation within organizations. This observation aligns with other findings that saw AI emerging as a critical asset that organizations must develop and leverage to sustain competitive advantage (Mikalef et al., 2019).
However, not all find AI adoption to be beneficial. One concern is that of algorithmic bias caused by bias in the data used to train AI applications. AI algorithms therefore run the risk of perpetuating and exacerbating those biases when making decisions, leading to potentially discriminatory outcomes (Panch, Mattie, & Atun, 2019). Researchers are thus seeking to help human trainers identify and mitigate such biases, develop fairness metrics, and develop suitable ethical guidelines. There is also the black box problem in that AI models are extremely difficult, if not impossible, to analyze because the ways it stores and uses information is not directly observable. This potentially reduces trust in AI, makes accountability difficult when errors do occur, and limits utility of the outcomes (von Eschenbach, 2021). AI developers have been trying to address this two ways: (a) by adding features that allow the AI to explain its analyses (for example, several contemporary AI tools now have explainability features), and (b) establishing auditing processes. A third concern is of the psychological impact on workers who fear being replaced by the technology or who witness morale and commitment to the organization erode as a result of AI adoption (e.g., Frenkenberg & Hochman, 2025). Finally, like most technologies, AI adoption greatly benefits from availability of expertise and talent in data science, machine learning, or other specialists. The lack of such specialists can result in slowed adoption, poor implementation, member resistance, and over-reliance on external vendors (Hangl, Krause, & Behrens, 2023; Gemini, 2025).
Relevant Theories and Ongoing Debates on AI Adoption
The theoretical debates in AI workplace studies are characterized by several major tensions, with leading contemporary scholars contributing distinct perspectives across multiple dimensions. The following are some of the on-going debates and leaders scholars in each camp:
The Automation vs. Augmentation Debate. Will AI replace workers or merely become a tool that enhances productivity? In the former camp are scholars such as Daron Acemoglu who finds little to support the claims made by AI proponents of its near-term potential and leads the skeptical view alongside MIT economist David Autor. Acemoglu (2025) argues that current AI development is primarily focused on automation which could lead to job displacement without corresponding productivity gains. Autor (2024) sees that AI has only accelerated the use of information as nothing more than an input for a “more consequential economic function, decision-making, which is the province of elite experts.” They both challenge the “AI productivity boom” narrative, arguing that most AI applications simply replace human tasks rather than creating new value.
On the other side are Erik Brynjolfsson (Stanford) and several scholars at MIT Sloan who maintain that AI will primarily augment human capabilities rather than replace them. Their studies uncovered how, for example, customer service agents who receive background information on their cases from generative AI tools can improve their productivity, with novice workers experience the biggest gains. Brynjolfsson’s (2022) recent work emphasizes that the key is designing AI systems that complement human skills rather than substitute for them. Meanwhile, Loaiza & Rigobon (2024) agree and developed an “EPOCH” index that measures tasks according to risk of substitution by AI and extent of potential for augmentation instead.
The Labor Market Polarization Debate. Acemoglu is joined by other scholars such as Pascal Restrepo (Boston University) in arguing that AI will create a so-called “barbell” labor market with high-skill, high-wage jobs at one end and low-skill, low-wage jobs at the other, while hollowing out middle-skill positions (Acemoglu & Restrepo, 2022). Their empirical work on industrial robots supports this thesis. But others like Danielle Li (MIT Sloan) argue that AI could democratize access to high-level capabilities, potentially reducing skill premiums. Their work on AI-assisted coding and writing suggests that AI tools might level the playing field rather than increase inequality (Brynjolfsson, Li, & Raymond, 2025).
The Organizational Control and Surveillance Debate. Is AI going to be cultivated into a highly-efficient and effective form of algorithmic management, greatly increasing control and suppresion of work? Or will it instead provide ways of improving fairness and reducing human bias in decisions? On the former side are scholars such as Karen Levy (Cornell) and Alex Rosenblat (Data & Society) who have been leading research on algorithmic management and argue that AI enables unprecedented workplace surveillance and control (Levy, 2022; Rosenblat, 2018). Their work on platforms like Uber reveals how algorithmic systems can manipulate worker behavior and undermine autonomy. On the other side is Alrakhawi et al. (2024) who are proposing that AI-driven management systems can improve fairness by reducing human bias and providing better performance feedback. They emphasize the potential for AI to create more objective and data-driven management practices.
The Skills and Future of Work Debate. Will AI accelerate the deskilling of the workforce or relieve workers of mundane tasks, thereby elevating work’s meaning? Cazzaniga et al. (2024) argues that AI represents another wave of skill-biased technological change, increasing demand for high-skill workers while reducing demand for routine cognitive tasks. Historical analysis suggests that education and training systems must adapt rapidly lest human workers find themselves replaced by AI (e.g., Zirar, Ali, & Islam, 2023). Taking a different view are those arguing that the key is not specific skills but rather the ability to perform non-routine tasks that complement AI capabilities (Upreti & Sridhar, 2024). They emphasize problem-solving, creativity, and interpersonal skills as enduring human advantages. Others argue that the future of work requires continuous learning and adaptation. For example, Poquet & De Laat (2021) advocates for organizational learning systems that help workers continuously upgrade their skills alongside evolving AI capabilities.
The Ethical AI and Governance Debate. What exactly constitutes the ethical adoption and use of AI? Are there tasks that simply should never be left to such technologies? Authors such as Cathy O’Neil (2017’s Weapons of Math Destruction) and Safiya Noble (2018’s Algorithms of Oppression) have been reporting on the dangers of algorithmic bias and emergent discrimination in workplace AI systems that may perpetuate and amplify existing inequalities. On the other hand, Timnit Gebru (see Qumer, 2023) and Joy Buolamwini (2024) advocate for institutional reforms and technical solutions to ensure AI systems are fair and transparent, feeling that capabilities in bias detection and mitigation in AI systems will be sufficient.
And then, what is the role of government in controlling AI development? Calo & Citron (2021) is among a growing literature stream looking into how legal and regulatory frameworks, including liability and workers rights, should adapt to AI. Other scholars have looked into how governance frameworks should be developed to address issues such as bias, discrimination, and accountability in AI applications (Mäntymäki et al., 2022).
Some Contemporary Areas of Research
The following are just a sampling of contemporary areas of research. Suffice to say that scholars have been approaching AI from nearly all directions given its novelty and almost universal scope in adoption. Have any other areas of research to suggest? Please provide your ideas through our suggestion page.
Occupational Health and Safety Implications. Researchers in this area are investigating AI’s influence on occupational health and safety (OHS). For example, can advancements in AI tools enhance safety protocols, or could they help better integrate or enhance health and occupational safety standards in various workplaces (Pishgar et al., 2021)? In what ways could AI foster beneficial relationship with workers, such as sustaining equitable employment practices or avoid compromising worker health and safety (Jetha et al., 2023)?
Risk Perception and Acceptance of AI Technologies. To what extent does the public perceive and accept AI technologies or understand their benefits and risks? And what causes those perceptions to change for the better or worse? Or how does the context influence such perceptions? Scholars have been exploring these questions in various settings. For example, one study in an educational setting found that initial exposure often leads to increased familiarity, which can subsequently influence acceptance (Isaksen et al., 2025). But others have found that while discourse around AI may emphasize its benefits, the associated risks are often undervalued, leading to a one-sided understanding of AI’s impact (Toll et al., 2020). Mixed results have been found in many other settings as well, suggesting that this is an area ripe for further research.
In human resource management (HRM), researchers have proposed that alignment in policies, strategies, and cultural readiness within the HRM landscape is an important formula for successful AI adoption (Agustono et al., 2023). AI technologies can augment HRM practices by enhancing efficiency and productivity through data-driven insights, thereby transforming traditional HRM functions (Rooshma et al., 2024). Notably, effective integration of AI can optimize talent management and engagement strategies, which are helpful in today’s competitive environments (Alsheibani et al., 2020).
Decision-making processes within organizations also face transformation due to AI’s influence. This is where the promises of AI could be great. A major challenge for managers is how the proliferation of data and information overload can adversely affect decision-making quality (Dietzmann & Duan, 2022). AI technologies have proven very successful at synthesizing and making sense of vast amounts of information, which has shown potential for easing the burden on managers, so long as they are careful and do not over-rely on AI. Thus leveraging AI must be done properly and effectively (Dietzmann & Duan, 2022). How to do this across contexts is open for debate.
Enhancing Employee Experience and Skills Development. Researchers also addresses the idea of “AI readiness” of employees — to what extent do they have the skills to fully leverage AI, and how can such skills be developed? Is success measures on the basis of the individual or of the organization, or both? One study showed that an organization that methodically introduces AI can develop essential competencies among faculty and staff (Saidakhror, 2024). This suggests that through proper support and training, organizations can ensure that their employees are prepared to work alongside AI technologies and capable of harnessing them to boost productivity. AI systems paired with robotics could also help foster better human-technology interaction (Rogozińska‐Pawełczyk, 2020).
There is also evidence of the importance of culture. Researchers are finding that agile cultures that can adapt to rapid changes are beneficial for successful AI integration. Research indicates that AI’s integration can enhance organizational agility, enabling firms to respond more effectively to market dynamics and operational challenges (Alshamsi et al., 2024). Also, accepting and integrating AI technologies within the workforce presents challenges. As companies introduce AI into their operations, understanding the subjective experiences of employees—such as perceived usefulness and ease of use—become important (Kelm & Johann, 2024).
Addressing the Challenges of Inclusivity and Fairness. Inclusivity and fairness in AI deployment are rapidly becoming focal points for researchers. Recent research has uncovered gender disparities in attitudes toward AI, which may impede broader acceptance of the technologies (Borwein et al., 2024). To what extent can AI technologies bedeployed in ways that benefit all employees, mitigating risks related to discrimination and reinforcing the need for policies that support productive and satisfying work environments?
Related TAOP Episodes, Events, and Notes
126: Labor and Monopoly Capital — Harry Braverman
125: Institution and Action — Steven Barley
118: Organizational Structures & Digital Technologies – AoM 2024 Symposium
96: Informating at Work – Shoshana Zuboff
51: The Tyranny of Light — Hari Tsoukas
40: Symposium on the Gig Economy LIVE
22: Human-Machine Reconfigurations – Lucy Suchman
18: Gig Economy, Labor Relations and Algorithmic Management
Related Resource Pages
Rack CA – Organizational Agility & Adaptability
Rack CD – Digital Transformation and Future of Work
Rack CE – Employee Well-Being & Mental Health
Rack CI – Inequality and Justice
Rack CL – Leadership in the 21st Century
Rack CR — Resource Management
Rack CS – Sustainability and Corporate Social Responsibility (CSR)
Rack CW – Meaningful Work
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