Abstract

This literature review explores artificial intelligence’s impact on professional identity at the individual and collective level. Analyzing 31 studies from different professions, it highlights AI’s dual potential: enhancing professional expertise, autonomy and service quality, while also threatening de-professionalization and loss of legitimacy. Furthermore, key factors influencing AI’s impact include deployment characteristics, individual professional traits, and organizational contexts. We finally discuss implications for the future of professions and professional organizations.

Keywords

Artificial intelligence, professionals, professionalism, professional organizations, identity.

Introduction

Artificial intelligence (AI) is transforming professional work, possibly replacing humans in several professions (Armour & Sako, 2020; Frey & Osborne, 2017). It is not simply a matter of automation of routine work, rather AI has the potential to substitute the specialized and reliable technical knowledge acquired through prescribed training that defines professionals. This may be impacting the very nature of professional role-identities, i.e. “the way that professionals see themselves in terms of who they are and what they do” (Reay et al., 2017), including their key competencies, the way they interact with clients, their educational pathways, as well as the professional jurisdictions and the boundaries between professional groups.

Technology has always been considered an important driver of professional role-identity change (Susskind & Susskind, 2015). For instance, in his fundamental work Barley (1986) has shown that after the introduction of CT scanners roles changes the relative distribution of expertise and autonomy of radiologists and radiographers. Prasad & Prasad (1994) studied how the adoption of a new administrative database enhanced the role and identity, expanding their occupational boundaries. Nelson & Irwin (2014) analyzed how, when the Internet arose, librarians managed to redefine their identity from being the ‘masters of search’ to ‘connectors of people and information’. These changes can impact professional identities at the individual level but also, more broadly, at the collective level (Reay et al., 2017) with respect to the role of an entire profession within society.

However, while it is clear that AI has the potential to disrupt individual and collective identities of professionals and professions, research evidence is still scarce, due to the novelty of the phenomenon and the (relatively) limited impact that professionals have already experienced. While in some sectors, such as journalism or medical diagnosis, AI based technologies have already taken ground, in other areas, such as law, architecture or accounting, are still being piloted. For this reason, we aim to explore existing academic literature in different professional fields in order to answer our question “what is the impact of AI on professional identities and professionalism?”.

Methods

To answer our research questions we conducted a scoping review. We formulated a search strategy for Scopus and Web of Science, finalized in November 2024, with the following search string: “professional identit*” or “professionalism” and “artificial intelligence” or “AI”. Eligibility criteria included studies on professionals (including professionals in training and students) and professions, focused on the adoption of AI solutions, and their impact on identities and professionalism. Publications were limited to journal articles and conference proceedings in English, excluding notes and editorials lacking empirical evidence or scientific rigor. Articles that focused on occupations that are not considered as professions were excluded, for instance papers on the impact of AI on work in general. Also, we did not include contributions that studied the performances or productivity of AI, but without directly dealing with professional identities.

Our initial search yielded 584 records, reduced to 430 after removing duplicates. The title and abstract screening process resulted in the selection of 78 relevant articles for full-text review. The final sample for data extraction included 31 papers. Appendix A illustrates the data screening and extraction process.

Results

Characteristics of the studies included in the scoping review

As shown in Appendix A, the literature search yielded 31 publications from 28 academic journals in different disciplines, including medicine or nursing studies (11), engineering or information systems (6), organization studies or sociology (5), journalism (3), education (2), etc. All studies but three were published after 2020, and a significant share (58%) in 2024. Empirical studies were conducted in various geographical contexts, including European countries, US, Japan, Malaysia, Iran, etc. Thirteen studies were focused on the medical profession from different disciplines, including neurology, nuclear medicine, radiology, etc. Threefocused on journalists, two on teachers, two on lawyers, and the others on various professions including auditors, nurses, public officers, IT, HR, etc. The prevalence of studies from medical and science journals is linked with a paucity of theoretical underpinnings in our sample papers.

In terms of methodological approaches, 16 articles were empirical (10 qualitative studies, four quantitative, one mixed methods and one action research) while 15 were argumentative works. Seven empirical studies examined prospectively the perceived impact of a future AI adoption on professional identities, asking respondents with limited exposure of AI solutions. The combination of these elements underlines that research evidence is still limited, and confirms the appropriateness of conducting a scoping review. Nevertheless, we retrieved 9 empirical studies studying retrospectively professionals with direct experience of AI in their daily work.

We identified common themes in the literature with reference to the impact of AI adoption on professional identity and the factors influencing this relationship, as outlined in the following paragraphs.

What is the impact of AI adoption on professional identity?

We divided our findings between the outcomes for professionals’ individual identities and those for the identity of the profession in general because professional identity is a multi-level phenomenon (Goto, 2021). Also, we distinguished between positive identity developments versus identity threats and tensions. Indeed, for almost all the actual or potential implications of AI on professional identity that are mentioned by the studies in our review we have found both positive and negative consequences. Table 1 presents a synthesis of these results.

Table 1: An ambiguous impact: Professional identity outcomes of AI adoption.

Level of analysis	Theme	Identity developments	Identity threats
Individual	Expertise	Shift to higher value tasks (reduction of repetitive tasks): – More time to high value activities – Development of expertise – Support of creativity – Professional development /reskilling – Challenge emotions – Achievement emotions – Reinforces training and supports less experienced professionals	Substitution of professional expertise: – Deskilling / overreliance on AI can lead to loss of expertise – Less work meaning, more boring work – Less pride/self-esteem – Loss emotions – Deterrence emotions
	Service	– More productivity – Availability – Accuracy – Speed in responding to customers’ needs – Personalization – Provision of easy-to-understand information to clients	– Lacks of “physical” presence – Lack of originality – Less human contact – Less client centricity – Less empathy – Lack of understanding of the real context
	Ethics	– Greater professional impartiality in decision making	– Lack of ethical sensitivity – Loss of confidentiality / privacy – Possible AI biases – Plagiarism – Limited accountability
	Autonomy	– Shift to more managerial tasks	– Loss of discretionary power
Collective	Legitimacy	– Higher professions’ legitimacy due to increased expertise, service orientation, ethics and autonomy	– Lower professions’ legitimacy due to loss of expertise, service orientation, ethics and autonomy
	Restratification	– Some professions’ expertise increases value because of AI	– Some professions’ expertise loses value because of AI
	Numerousness	– Lower cost of professional services lead to increased demand	– Job loss

At the individual level of analysis we identify four main themes in the reviewed studies: professional expertise, service, ethics and autonomy.

The first and clearer finding of the studies in our sample is that AI performs tasks previously conducted by human professionals and, consequently, substitutes for professional expertise. Since early studies on professionalism, professional authority has been considered to be based on the expertise of professionals (Parsons, 1939), and therefore the impact of AI introduction on individual professional expertise is highly relevant. Depending on the context and type of adoption, AI-performed tasks can be categorized as routine or non-routine and involve low or high cognitive complexity (Tinguely et al., 2023). When AI is perceived to assume routine and low-complexity tasks, professionals report feeling liberated to pursue other activities (Møller et al., 2025; Rozario et al., 2024). AI may also enhance training and support of less experienced professionals (Amann et al., 2023). The negative aspect of this phenomenon is the risk of deskilling, as an overreliance on AI can result in a loss of expertise (Amann et al., 2023). Professional expertise is so fundamental to professional identities that this positive or negative impact on expertise subsequently affects work meaning, self-esteem (Møller et al., 2025), and emotions (Eshraghian et al., 2024).

AI also directly impacts service provision by enhancing productivity (Goto, 2021; Lombi & Rossero, 2024), accuracy (Gao & Zamanpour, 2024), response time to customers’ needs (Gao & Zamanpour, 2024), personalization (Gao & Zamanpour, 2024), and the ability to provide comprehensible information to clients (Currie, 2023). For instance, it can assist health professionals in educating their patients by providing information on medical conditions, treatment options, and other related topics in accessible language (Currie, 2023). Conversely, numerous professions require relational, physical, or contextual understanding that AI is not (yet?) capable of, and therefore, the utilization of AI may be detrimental to the quality of service for these professional activities. In journalism, for example, AI cannot engage in “human contact” or “meet with people,” nor can it possess an emotional understanding of the story and its sources. A human journalist has “empathy for other people […] AI cannot “report from the field” […] and it cannot collect the sensory details necessary for the sort of narrative journalistic storytelling that can bring news stories to life (Møller et al., 2025). In numerous professions, tacit, embodied knowledge (Särmäkari & Vänskä, 2022) constitutes a fundamental type of expertise that AI-powered robots do not possess.

Another prevalent theme is ethics, which is also a core feature of professions according to classical sociological accounts of professions (Abbott, 1983). The majority of studies highlight the threats to professional ethics implied by AI. These include lack of ethical sensitivity (Hassan & El-Ashry, 2024), loss of confidentiality / privacy (Amann et al., 2023), potential AI biases (Gao & Zamanpour, 2024; Hassan 2024), and plagiarism (Currie, 2023). The implementation of AI also raises questions regarding accountability and oversight, as it remains unclear who should be held responsible for AI-powered professional services (Thornton, 2021). On a positive note, AI may support greater professional impartiality in decision-making, as highlighted by Davis (2024) in a study of public servants.

The final individual-level theme identified is autonomy, a fundamental aspect of professionalism (Freidson, 1984). The primary concern regarding this theme is that AI may diminish the discretionary power of individual professionals (Jussupow et al., 2022). Conversely, AI adoption may necessitate professionals, as monopolists of domain expertise, to coordinate an increasing number of members in other occupations such as statisticians, information systems experts, or administrative staff (Goto, 2021). In this regard, the scope of professional autonomy may undergo transformation rather than necessarily decreasing.

The erosion of monopoly of expertise and professional autonomy discussed at the individual level has clear implications for the legitimacy of professions at a collective level. Conversely, all the potential developments of expertise and autonomy, as well as the need to safeguard professional ethics, may increase the legitimacy of professional institutions and regulations. In some studies, the individual-level opportunity to develop new professional skills is perceived as a potential catalyst that creates opportunities for professional restratification. For instance, some radiologists, part of a healthcare profession that has utilized complex information systems for many decades, view proficiency in AI technologies as a means to enhance their professional status (Lombi & Rossero, 2024).

A final common concern is that, by supporting individual productivity, AI may induce job losses at the collective level over time (Cox, 2023; Goto, 2021; Lin, 2023; Møller et al., 2025; Sonni, Putri & Irwanto, 2024).

What influences the impact of AI on professional identity?

Based on the analysis of the literature review notes, the results of our study on the factors influencing the acceptance of AI implementation projects by professionals can be categorized into three main areas: 1) AI deployment characteristics; 2) Individual professional characteristics, 3) Organizational and industry characteristics.

AI deployment characteristics vary in the scope of AI use from implementations where AI is perceived as fully autonomous in performing a task that was previously performed by a professional to human-led implementation where AI is assistive to a professional (Amann et al., 2023; Armour & Sako, 2020; Cox, 2023; Lan, 2024; Rozario et al. 2024; Särmäkari & Vänskä, 2022; Tinguely, Lee, & He, 2023; Van Hees et al, 2024). Involving professionals (rather than imposing top-down projects designed by managers and IT experts) is evidently a strategy to design systems that professionals can accept (Svensson & Jotterand, 2022) and that effectively perform in the typically complex execution of professional services. Similarly, resistance will be higher if professionals perceive that business priorities such as profit maximization are the primary goal of the project rather than objectives aligned with typical professional values such as quality or accessibility of the services (Thornton, 2021).

Another important feature to consider in order to improve both effectiveness and acceptance of AI implementations in professional contexts is the nature of tasks performed by AI and previously performed by humans. The studies in our sample mention routine versus non-routine tasks (Tinguely et al., 2023), low versus high cognitive complexity tasks (Tinguely et al., 2023), and discuss the types of professional tacit knowledge used in the task (Mooradian, 2024; Lan, 2024), some of which cannot currently be delegated to AI.

Other implementation-specific features of AI that may affect effectiveness and acceptability are: scale of AI use (Särmäkari & Vänskä, 2022), technological sophistication of AI (Thornton, 2021); the presence of a clear locus of ethical responsibility and ethics audit trails and risk governance procedures (Svensson & Jotterand, 2022) and the availability and quality of training in AI use (Whitby, 2015; Lan, 2024; Lombi & Rossero, 2024).

The second group of factors to consider are related to individual professional characteristics. Acceptability has been found to be higher for professionals who already have an innovation-oriented professional mindset (Van Hees et al., 2024), technological skills (Särmäkari & Vänskä, 2022) and experience with AI (Lombi & Rossero, 2024). Interestingly, Jussupow and colleagues (2022) find that the impact varies based on experience level and perceived temporal relevance of AI: if AI is perceived as relevant in the near future, threats to professional capabilities are more profound; conversely, if AI is seen as relevant in the distant future, threats to professional recognition become more significant. Moreover, the same study finds that medical students (novices) experience stronger identity threats and resistance to AI than experienced medical professionals.

The third group of factors comprises organizational and industry characteristics. Numerous authors emphasize the significance of regulatory and ethical frameworks established by public institutions and industry associations (Armour & Sako, 2020; Lombi & Rossero, 2024; Shi & Sun, 2024; Thornton, 2021)). A well-defined legal and ethical framework enhances acceptance. Generally, AI is more culturally acceptable in certain industries than in others (Shi & Sun, 2024), indicating that industries may have varying cultural norms regarding innovation and automation. Furthermore, industries may differ in client literacy (Shi & Sun, 2024), and in some sectors, clients may substitute professionals with AI or demand AI-related innovations from professionals. Notably, Van Hees and colleagues (2024) observe that AI may reduce service costs, potentially increasing total demand for professional services. Additionally, AI may serve as a catalyst for inter-professional dynamics, and changes in technology utilized by professionals may present opportunities to renegotiate professional boundaries relative to other professions operating within the same industry (Lombi & Rossero, 2024).

At the organizational level, it has been observed that larger firms may possess more resources to invest in AI-related projects (Armour & Sako, 2020). Moreover, AI implementation may be more prevalent in hierarchical and investor-owned firms compared to flatter, professional-owned partnerships (Armour & Sako, 2020).

Discussion and implications

From the perspective of organization studies, AI can be considered another technological innovation, and the established literature on technology acceptance may prove valuable in understanding and planning organizational projects that introduce AI. But AI also represents the first large-scale technological change that potentially impacts professional work, and the prevalence of professional employees within organizations introduces multiple layers of managerial complexity (Raelin, 1984). This review may contribute to a better understanding of technologically-driven change in professional organizational settings.

Our findings suggest that the consequences of AI implementation may positively or negatively impact different professional domains, and that acceptance of AI implementation projects by professionals is a complex interplay of factors related to the AI technology itself, the individual characteristics of the professionals involved, and the broader organizational and industry context in which the implementation occurs. Furthermore, organizations may actively manage AI deployment characteristics and consider individual professional characteristics to facilitate AI acceptance and success.

One key specificity of professional settings is the relevance of professional identity for organizational commitment (Wallace, 1993). The perceived threat to professional identity or the perceived potential for positive development of professional identity may have a significant impact on AI acceptance in professional organizations and the success of AI-related projects. Professionals will support or reject AI collaboration depending on whether they view it as enhancing or undermining their status and professional identity. This perception in turns depends on many individual and industry-level characteristics  but also on factors that organizations can actively manage such as involvement of professionals in AI systems design and ethical standards.

Professional identities will also shape how professionals integrate knowledge claims generated by AI and whether they accept or ignore AI decisions (Lebovitz et al. 2022). Employees, particularly professional employees, identify with multiple entities in organizational contexts, and many of these identities may generate tensions, such as conflicting demands (Greco et al., 2022). These pre-existing identity tensions interact with the ambiguities inherent in the implementation of a complex technology such as AI. The body of literature reviewed demonstrates that the outcome of these interactions, in terms of acceptance and success of AI implementation, may depend on deployment characteristics, individual professional attributes, and organizational and industry factors. AI-related projects may trigger the reconfiguration of identities and, in some instances, professional hybridizations with managerial (Goto, 2021) or technical logics.

The significant advancements in AI tools such as ChatGPT in recent years have garnered considerable managerial and academic attention regarding the potential impact of these technologies on work, including professional work. Due to this novelty, the majority of the articles identified for this review are theoretical or based on interviews with potential AI users rather than actual users. Consequently, further empirical research is necessary to comprehend the impact on professional workers in organizational settings. The potential improvements in efficiency and effectiveness resulting from the utilization of AI-related technologies in professional settings appear highly promising. However, the magnitude of the required investments and the specific complexities of professional environments warrant a more thorough examination of strategies for managing organizational change associated with AI implementation in professional organizations.

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Appendix A – Data screening and extraction process

The data screening and extraction process was completed in adherence to the PRISMA-ScR guidelines (Tricco et al., 2018). Following the criteria outlined above we screened studies firstly by title and abstract, and then by full-text assessment. Disagreements in the screening process were resolved based on discussion between the two researchers. Thematic analysis was conducted on collected data to answer our research question, and data extraction and charting were based on a template that was piloted and then further refined to contain information on: theoretical framework; country; professions and context; respondents type and role; focus on individual vs collective identity; degree of experience with AI; key findings, highlighting positive developments and identity threats/tensions. The search strategy process and outcomes are depicted in Figure 1, while Table 2 shows a synthesis of the data extraction table.

Figure 1: Search strategy

Table 2: Final sample for data extraction

Authors	Journal	Year	Type	Profession	Experience with AI
Allen, M. R. et al.	BMC Primary Care	2024	Empirical – survey	Medicine	High
Amann, J. et al.	PLoS ONE	2023	Empirical – interviews	Medicine (neurology)	Low
Armour, J., & Sako, M.	Journal of Professions and Organization	2020	Argumentative	Legal services
Cornwall, J. et al.	Anatomical Sciences Education	2023	Argumentative	Medicine
Cox, A.	Journal of the Association for Information Science and Technology	2023	Argumentative	Library
Currie, G. M.	Seminars in Nuclear Medicine	2023	Argumentative	Medicine
David, G.	Canadian Public Administration	2024	Argumentative	Public administration
Eshraghian, F. et al.	Information Technology and People	2024	Empirical – content analysis	Information Technology	High
Gao, K., & Zamanpour, A.	BMC Psychology	2024	Empirical – mixed methods	Financial engineering	High
Goto, M.	Journal of Professions and Organization	2021	Empirical – interviews	Auditing	Low
Hassan, E. A., & El-Ashry, A. M.	BMC Nursing	2024	Empirical – interviews	Nursing (critical care)	High
Jussupow, E. et al.	JMIR Formative Research	2022	Empirical – survey	Medicine	Low
Karataş, F., & Yüce, E.	International Review of Research in Open and Distributed Learning	2024	Empirical – action research	Education	Low
Lan, Y.	Teaching and Teacher Education	2024	Empirical – survey	Education	High
Lin, G. S. S. et al.	BMC Oral Health	2023	Empirical – interviews	Dental technicians	High
Lombi, L., & Rossero, E.	Sociology of Health and Illness	2024	Empirical – interviews	Medicine (radiology)	High
Møller, L. A. et al.	Journalism Studies	2024	Empirical – interviews	Journalism	Low
Mooradian, N.	Interdisciplinary Journal of Information, Knowledge, and Management	2024	Argumentative	NA
Rozario, S. Y. et al.	Anatomical Sciences Education	2024	Argumentative	Medicine (anatomical pathology)
Särmäkari, N., & Vänskä, A.	International Journal of Fashion Design, Technology and Education	2022	Empirical – case studies	Fashion design	High
Sattar, K., & Yusoff, M. S. B.	Education in Medicine Journal	2024	Argumentative	Medicine
Shi, Y., & Sun, L.	Journalism and Media	2024	Argumentative	Journalism
Sonni, A. F. et al.	Journalism and Media	2024	Argumentative	Journalism
Svensson, A. M., & Jotterand, F.	Journal of Medicine and Philosophy (United Kingdom)	2022	Argumentative	Medicine
Thornton, M.	International Journal of the Legal Profession	2021	Argumentative	Legal services
Tinguely, P. N. et al.	Journal of Organization Design	2023	Argumentative	Human Resource Management
Umer, M. et al.	Annals of Medicine and Surgery	2024	Empirical – survey	Medicine	Low
Vale, M. D.	Socius	2024	Empirical – case study	Medicine (psychiatry)	High
Van Hees, S. et al.	Postdigital Science and Education	2024	Empirical – interviews	Medicine (radiology)	Low
Whitby, B.	Artificial Intelligence Review	1988	Argumentative	NA
Whitby, B.	Intelligent Systems, Control and Automation: Science and Engineering	2015	Argumentative	Medicine