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One Size Fits Some: How Journalistic Roles Shape the Adoption of Generative AI — Møller et al. (2026) · 1 More Paper

• You’re listening to “One Size Fits Some: How Journalistic Roles Shape the Adoption of Generative AI,” by L.A. Møller, H. Cools, and M. Skovsgaard. Published in 2026. ISSN: 1751-2786 (Print) 1751-2794 (Online) Journal homepage: the linked source Lynge Asbjørn Møller, Hannes Cools & Morten Skovsgaard To cite this article: Lynge Asbjørn Møller, Hannes Cools & Morten Skovsgaard (2026) One Size Fits Some: How Journalistic Roles Shape the Adoption of Generative AI, Journalism Practice, 20:9, 2796-2817, DOI: 10.1080/17512786.2025.2484622 © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 31 Mar 2025. Submit your article to this journal Article views: 5677 View related articles View Crossmark data Citing articles: 18 View citing articles One Size Fits Some: How Journalistic Roles Shape the Adoption of Generative AI a,b and Morten Skovsgaard a, Hannes Cools a,c Lynge Asbjørn Møller aDigital Democracy Centre, University of Southern Denmark, Odense, Denmark; bAmsterdam School of Communication Research, University of Amsterdam, Amsterdam, The Netherlands; cCentre for Journalism, University of Southern Denmark, Odense, Denmark ABSTRACT. The rise of generative artificial intelligence (AI) has sparked debate about its implications for journalism and the roles of journalists. Yet, the interplay between journalistic roles and AI adoption remains underexplored. Drawing on a survey of Danish journalists (N = 299), our study addresses this gap by exploring how journalists’ professional role conceptions influence their adoption of generative AI. The results reveal role-specific patterns that align with traditional understandings of the respective role conceptions, suggesting that professional identities shape how journalists engage with new technologies. Journalists adhering to mobilisation and entertainment roles express heightened concerns about job security and work meaningfulness, while those adhering to watchdog and detached observer roles rather emphasise ethical and operational implications of generative AI for journalism. Despite these concerns, entertainment journalists actively employ generative AI to enhance content quality and audience engagement, and watchdog journalists recognise its potential to boost efficiency and accuracy. These variations across journalistic roles underscore the need for academia and news organisations to avoid oversimplified one-size-fits-all narratives about the adoption of generative AI in the news industry. Technology is simultaneously shaped by and shapes the journalists who use it, highlighting how professional identities and technological innovation co-evolve in modern journalism. Introduction. ARTICLE HISTORY Artificial intelligence; generative AI; journalistic roles; role conceptions; journalistic work; news automation The emergence of generative artificial intelligence (AI) marks a significant shift in how AI interacts with journalism. Earlier AI applications in news organisations were tailored for specific, often hidden tasks such as data analysis, template-based news generation, or personalised news distribution. Despite their impact on journalism, these AI systems often remained opaque to much of the journalistic community due to their complexity and behind-the-scenes nature (Jones, Jones, and Luger 2022). In contrast, generative AI tools are accessible and easy to use. Generative AI refers to machine learning systems that produce outputs mimicking human creativity by learning from extensive training data. The most common of these tools, such as ChatGPT, Gemini, DALL·E, and MidJourney, allow users to generate text or images based on simple prompts. This accessibility and versatility distinguish generative AI from earlier systems, bringing AI directly into journalists’ workflows. Journalists are already employing generative AI across various stages of the reporting process, and many news organisations have begun systematically integrating the technology into their daily news operations. Generative AI’s potential to transform journalistic practice is likely to evoke a wide range of perspectives and experiences among journalists. With generative AI seemingly promising to streamline news production in ways previously unimaginable, some journalists may view the technology as an opportunity to enhance their work. However, as generative AI begins to undertake tasks traditionally performed by journalists, others may worry not only about job displacement but also about losing the sense of purpose and satisfaction that comes with their work. Moreover, generative AI introduces ethical challenges that further complicate its adoption, including concerns about its lack of accuracy and potential for bias. This suggests that generative AI is not a one-size-fits-all solution for journalism. Instead, its adoption is likely to be shaped by the diversity of journalists’ professional identities and experiences. However, academic literature has yet to explore the influence of these factors on how journalists respond to and use generative AI. Without a deeper understanding of these differences, there is a risk of oversimplifying the responses to AI, creating a binary narrative of threat versus opportunity that ignores the profession’s inherent diversity. Our study addresses this gap by exploring the influence of journalists’ varying role conceptions on their attitudes towards and usage patterns of generative AI. The study of professional role conceptions in journalism is a longstanding body of research that examines the different ways in which journalists perceive their normative roles in society. These role conceptions shape journalists’ work processes, ethical considerations, and overall approach to journalism. Consequently, journalists with differing role conceptions will face distinct ethical and operational challenges when integrating AI into their work. By examining how these professional identities shape the adoption of generative AI, this research offers a more nuanced understanding of the complex dynamics at play within the profession. Such an understanding is important not only as a contribution to academic literature but also as a foundation for navigating the ongoing integration of AI in newsrooms. Failure to recognise the diverse perspectives of journalists could lead to ineffective implementation strategies and unaddressed ethical dilemmas, with broader implications for journalism’s role in society. Specifically, we explore the relationship between generative AI and journalistic roles by drawing on a survey of Danish journalists that measures their role conceptions, self-reported uses of generative AI, and their views on benefits and concerns related to the adoption of generative AI. This approach enables us to identify and compare patterns of AI use and attitudes across four different role conceptions that emerged from the survey responses: the mobilisation role, the watchdog role, the detached observer role, and the entertainment role. This analysis finds distinct differences between the roles that largely align with traditional understandings of the respective role conceptions, suggesting that journalistic roles influence how journalists interact with generative AI. The survey is conducted in Denmark, a small but diverse media market characterised by full digitisation and an institutionalised professionalism rooted in strong public interest traditions. Danish news organisations are already investing heavily in generative AI tools that are currently being implemented in newsrooms. This provides an interesting setting to explore the early adoption of generative AI among journalists who share a commitment to high standards of journalistic practice, offering insights into variations within the profession that extend beyond the national context of the study. Generative AI and News Automation The public release of the text generation tool ChatGPT in November 2022 drew significant attention to the capabilities of generative AI as a highly accessible AI technology. Generative AI systems typically feature conversational interfaces that allow users to ask questions and receive responses in ways that mimic human interaction. Unlike the often hidden AI technologies in journalism, generative AI can therefore be seamlessly integrated into regular journalistic workflows, with recent research revealing use cases at every stage of the reporting process, from the gathering to the production, verification, and distribution of news. To understand the potential uses of and attitudes towards generative AI in journalism, this section links its applications to literature on journalistic responses to previous implementations of algorithms and AI in newsrooms. One of generative AI’s core strengths lies in its ability to produce complex narrative text at scale, significantly expanding the potential for automated news writing compared to earlier template-based approaches. Consequently, generative AI may amplify concerns among journalists about job displacement. Studies of newsroom innovation show a trend towards increasing efficiency under financial pressure, with automation frequently adopted to reduce costs. As a result, many journalists perceive news automation as a threat to their job security and satisfaction (van Dalen 2012). Research highlights an underlying fear among journalists that automation technologies could either replace their roles entirely or undermine the meaningfulness of their work by reducing task and skill variation. Rather than being seen solely as a threat to jobs, AI and algorithms are sometimes portrayed as opportunities to improve journalism through the automation of routine tasks. Generative AI effectively handles repetitive tasks such as transcription, translation, and summarisation, thereby freeing journalists to concentrate on the traditional but time-consuming tasks that define their profession (Møller, Skovsgaard, and de Vreese 2024). Previous studies suggest that journalists generally welcome automation technologies that reduce this kind of routine work, enabling greater emphasis on in-depth reporting and investigative journalism. In line with these studies, Moran and Shaikh (2022, 1765) have found AI’s role in journalism to be pitched as a “technological liberator of journalists which can untether them from clutches of menial tasks that deprive them from producing quality work”. Generative AI’s capacity to process, interpret, classify, and identify patterns in large and complex datasets also demonstrates its potential as an augmentation tool that enhances rather than replaces journalistic work. Such an augmentation approach has been found to deflate automation-related fears among journalists by ensuring that the technologies remain under human control. Moreover, it mitigates ethical concerns prevalent in the literature on automated news writing, such as maintaining standards of verification and balance. However, generative AI introduces significant new ethical challenges. For instance, generative AI technologies are often trained on outdated data, they are known to consistently produce falsehoods, and they reproduce biases in their training data. Recent research highlights growing awareness among journalists of these ethical challenges. This literature underscores the numerous opportunities and challenges that generative AI presents for journalists. However, a significant gap remains in understanding how generative AI might be viewed and utilised differently across various segments of the journalism profession. Current literature tends to approach its implications in broad terms, overlooking the nuanced ways in which individual journalists’ values and beliefs shape their perspectives and practices. Recognising these differences is essential for comprehending the broader implications of AI integration in newsrooms. To address this gap, our study explores how journalists view and use generative AI depending on their professional role conceptions. Generative AI and Journalistic Roles The ways in which journalists think about their societal roles is an important area of research in journalism studies. Central to this understanding is the concept of journalistic roles, which describe how journalists define their relationship with society and their professional responsibilities within it. Role conceptions are significant because they shape journalists’ professional behaviours and practices, bridging journalism as an institution with the individual choices and actions of its practitioners (Mellado and van Dalen 2014). Therefore, the concept is often used to study variations in journalists’ professional identities across different contexts. Research into journalistic roles often relies on surveys or interviews, asking journalists to articulate the normative roles they believe they should fulfil within society. Building on Cohen’s (1963) distinction between “neutral” and “participant” journalistic roles, this approach has enabled scholars worldwide to identify and categorise distinct role conceptions that journalists consider important for their work. Some role conceptions are rooted in traditional normative notions of how journalism should serve the public interest. For instance, the “neutral” and “disseminator” roles depict journalists as detached observers, dedicated to reporting facts objectively and impartially. Other normatively rooted roles adopt a more antagonistic stance toward power, such as the “watchdog” and “monitorial” roles. These roles emphasise holding authority accountable and safeguarding the public interest, often through investigative and critical reporting that uncovers hidden truths. In contrast, other roles are more audience focused. For instance, the “infotainment” or “accommodative” roles frame the audience as spectators to be entertained, addressing their needs through appealing and relatable content. Meanwhile, the “populist mobiliser” or “interventionist” roles seek to mobilise the audience toward political, social, or cultural change. Journalists adhering to these roles aim to influence public opinion, advocate for change, and facilitate development. Given their impact on behaviour, role conceptions also influence how journalists adopt emerging technologies. Previous research demonstrates that journalistic roles shape online journalist–audience relationships and significantly affect how journalists respond to audience metrics. Similarly, role conceptions may explain how readily journalists adopt generative AI. For instance, journalists aligned with watchdog or detached observer roles might resist generative AI due to concerns about editorial independence and the accuracy of AI-generated content. However, journalistic role conceptions are not always reflected in practice (Mellado and van Dalen 2014). External factors such as organisational routines, technological pressures, and precarious working conditions often constrain journalists’ ability to act in line with their values and beliefs. In an era of increasing precarity in journalism, resisting new technology based on normative ideals for instance poses a greater risk of obsolescence or job displacement. Consequently, journalists adhering to more audience-oriented roles may feel compelled to adopt generative AI to meet increasing content production demands. These relationships between journalistic role conceptions and the adoption of generative AI remain largely unexplored in current literature. This article utilises journalistic role conceptions as independent variables to examine and explain variations in both journalists’ attitudes towards and usage patterns of generative AI. Specifically, we ask the following research questions: RQ1: How do journalistic roles influence journalists’ attitudes towards generative AI?. RQ2: How do journalistic roles influence journalists’ use of generative AI?. Methods. To explore these research questions, this study employs a survey of Danish journalists conducted from March to June 2024. The Danish media system shares characteristics with those of the other Nordic countries, such as inclusive and diverse press markets, strong public service traditions, high journalistic professionalism, and full digitalisation. Moreover, the news industry in Denmark is under increasing financial pressure, and many news organisations are investing heavily in the implementation of generative AI in journalistic practices. These characteristics make Denmark a well-suited case for studying the influence of journalistic roles on the adoption of generative AI because tensions with professional ideals are more likely to emerge in a highly professionalised media system. Surveying journalists is generally challenging due to journalism’s ill-defined boundaries complicating the definition of a target population, limited access making it difficult to reach sufficient members of that population, and time pressures inherent in journalistic work reducing their availability to participate. To mitigate these challenges, we carried out the sampling and distribution in collaboration with several major Danish media organisations, including regional media organisations Jysk Fynske Medier and Nordjyske Mediehus; national commercial television station TV2 Denmark; and national newspapers Jyllands-Posten, Ekstra Bladet, B.T., Weekendavisen, and Kristeligt Dagblad. Managers and editors within these organisations distributed the survey to their journalists. This approach allowed us to gain access to a large number of journalists and increased the visibility of our survey, ensuring that we captured the adoption of generative AI among a wide range of professional journalists in Denmark. The survey was distributed to approximately 1,300 journalists, based on estimates provided by the managers overseeing its distribution. However, the exact number of journalists invited remains uncertain due to restricted access to the internal mailing lists of the news organisations. Of those contacted, 299 journalists responded, with 211 fully completing the survey and 88 partially completing it, resulting in a response rate of 26.3% according to AAPOR’s Response Rate 2. The sample consists of 60% men and 40% women, with most respondents being reporters (55%), followed by editors (14%) and interns (7%). Their experience in the news industry ranges from fewer than 3 years (14%) to more than 20 years (35%), with 26% having 3–10 years of experience and 25% reporting 11–20 years. While this does not constitute a representative sample of Danish journalists, it serves as a non-probability sample designed to explore variations in the adoption of generative AI. This approach is justified in cases where obtaining a representative sample is unfeasible, particularly given the substantial variation observed within our final sample on the key variables of interest in the study. Measures Journalists’ attitudes towards and uses of generative AI are employed as dependent variables in this study. We measure the concerns and benefits related to the adoption of generative AI in journalistic work through 12 items developed based on qualitative research into journalists’ responses to recent advancements in generative AI. Six items measure concerns by asking respondents to indicate on a five-point Likert scale the extent to which they are concerned that generative AI will “cause job cuts in the news industry”, “take my job in the future”, “take away specific tasks that I enjoy”, “make my job less meaningful”, “make my job more boring”, and “compromise my journalistic integrity”. The other six items measure benefits by asking respondents to indicate on a five-point Likert scale the extent to which they believe that generative AI will “enhance my creativity”, “improve my efficiency”, “enable me to pursue tasks that I enjoy”, “enhance the accuracy of my output”, “improve the quality of my output”, and “help my output reach larger audiences”. The survey presents all 12 items in a single battery in a randomised order. An exploratory factor analysis indicates that the six items measuring concerns load onto one factor, while the six items measuring benefits load onto another factor. Therefore, we have created two index scores with high internal reliability by averaging the score across the six items measuring concerns (α = 0.83, M = 2.73, SD = 0.92) and benefits (α = 0.86, M = 3.28, SD = 0.84). The survey also asks respondents about the importance of different ethical and operational issues related to the use of generative AI in journalism. On a five-point Likert scale from 1 (not important) to 5 (extremely important), respondents are asked to rate the importance of the following items: “determining who is accountable for mistakes”, “creating fair data sharing policies between news organisations and tech companies”, “being transparent with the audience about the use of generative AI”, “fact-checking the output of generative AI”, “training journalists in the use of generative AI”, “creating internal guidelines for the use of generative AI in journalism”, “regulating the use of generative AI in journalism”, “developing in-house generative AI tools rather than relying on external companies”, and “ensuring that journalists are involved in developing and implementing generative AI tools within news organisations”. In addition to measuring the importance of the individual issues, we also combined them into an index score by averaging the scores across all items to measure an overall score of the rated importance of ethical and operational issues (α = 0.79, M = 4.17, SD = 0.57). The use of generative AI is measured by asking respondents how often they use generative AI in their capacity as journalists in general and for specific applications on a five-point Likert scale from 1 (never) to 5 (all the time). Specific applications include 12 different uses of generative AI found in existing research, such as brainstorming ideas, processing data, and generating text. Additionally, the survey asks respondents to rate the future potential of generative AI for different aspects of the journalistic work process on a five-point Likert scale from 1 (no potential) to 5 (huge potential). These include idea generation (brainstorming stories, sources, interview questions), information analysis (summarising documents, processing data), content creation (generating text and images), content editing (proofreading, feedback, image editing), reformatting content (news personalisation, reformatting for platforms), and optimising content (audience metrics analysis, search engine optimisation). Journalistic role conceptions are employed as independent variables to investigate their influence on the dependent variables described above. To measure role conceptions, we draw on a well-established battery of items from the Worlds of Journalism survey. In total, the survey asks respondents to evaluate the importance of 15 statements about the role of the news media on a five-point Likert scale from 1 (not important) to 5 (extremely important). An exploratory factor analysis with varimax rotation identified four underlying dimensions with distinct loadings and minimal cross-loadings. We grouped items into these dimensions based on factor loadings and employed Cronbach’s alpha to ensure the reliability of each dimension, evaluating the contribution of each individual item. Four items that did not contribute positively to a higher Cronbach’s alpha for their respective dimensions were excluded. This process resulted in four different role conceptions for which index scores were created by averaging the scores across the items that constituted each role conception. The role conceptions include: the mobilisation role (α = 0.74, M = 2.28, SD = 0.85) including four items (influence public opinion, advocate for social change, support national development, motivate people to participate in political activity), the watchdog role (α = 0.77, M = 3.34, SD = 1.19) including two items (monitor and scrutinise political leaders, monitor and scrutinise businesses), the detached observer role (α = 0.73, M = 4.20, SD = 0.87) including two items (be a detached observer, report things as they are), and the. entertainment role (α = 0.55, M = 2.93, SD = 0.78) including three items (provide entertainment and relaxation; provide the kind of news that attracts the largest audience; provide advice, orientation, and direction for daily life). Analytical Approach We analysed the data using the statistical analysis software STATA. All 299 responses were included in the analysis where applicable. First, we conducted a descriptive analysis to gain a more detailed account of journalists’ overall attitudes towards and use of generative AI. Next, we employed ordinary least squares (OLS) linear regressions to examine the relationship between journalistic role conceptions and the adoption of generative AI, while controlling for potential confounding factors that might influence this relationship. Specifically, we included control variables measuring personal and professional characteristics of the journalists and their environments. These include gender, which may influence attitudes towards and adoption of technology in general; professional experience (a four-category variable ranging from “Under 3 years” to “More than 20 years”), which could shape perspectives on generative AI due to factors such as established routines or hierarchical dynamics; and news organisation size (a five-category variable ranging from “1–10 journalists” to “100+ journalists”), which may affect the resources available for adopting generative AI. These controls help isolate the effects of journalistic role conceptions on generative AI adoption, accounting for other factors that may independently influence journalists’ attitudes and practices. Results. The section presents the findings of the survey, focusing first on the journalists’ attitudes towards the adoption of generative AI in journalism and secondly on their uses of generative AI in their work. In each subsection, we begin with an overview of descriptive statistics before exploring the influence of journalistic role conceptions. Coefficient tables for all regression models can be found in the appendices. Attitudes Towards Generative AI The findings reveal that journalists generally perceive the benefits of adopting generative AI more favourably than their concerns, a difference that is statistically significant. This suggests a cautiously optimistic outlook among journalists towards the integration of AI technologies in their profession. Figure 1 unpacks the responses to the individual aspects of benefits and concerns. The highest-rated concern is that generative AI will cause job cuts in the news industry in general, while concern regarding generative AI replacing the journalists’ own individual jobs is notably lower. This result clearly indicates a stronger concern for the broader impact of AI than for a direct threat to personal employment. The concern that generative AI might compromise journalistic integrity is also highly rated, while the concern that generative AI will make journalistic work boring is rated the lowest. When it comes to benefits, the belief that generative AI will improve efficiency receives the highest rating, followed by its ability to improve output quality, while its potential to enhance output accuracy is rated the lowest among the benefits. This pattern suggests that while journalists see generative AI as a tool for improving efficiency and quality, they recognise the need for human oversight to ensure accuracy. Turning to the first research question, the regression analysis indicates that different journalistic role conceptions influence attitudes towards generative AI (see regression tables in Appendices 1 and 2). Focusing first on concerns, Figure 2 illustrates that journalists who align with the mobilisation and entertainment roles tend to have a higher level of concern about generative AI than those in the watchdog and detached observer roles. This suggests that journalists adhering to the mobilisation and entertainment roles are more apprehensive about AI’s impact on their work. Unpacking specific concerns, the entertainment role is significantly associated with fears that generative AI will take over their jobs, while the mobilisation role shows a borderline significant relationship with concerns about job losses both generally and individually. The potential of generative AI to automate tasks that are typically tied to these roles may pose a greater threat to their job security. In contrast, journalists showing stronger adherence to the watchdog and detached observer roles appear to feel relatively secure in their positions, though the negative association with job security concerns is only borderline significant. Watchdog journalists likely view their investigative and accountability-focused work as less susceptible to automation, while detached observers may see their role in delivering objective analysis as similarly safe from the influence of generative AI. Job meaningfulness also appears to be affected by the adoption of generative AI, particularly for certain journalistic roles. The entertainment role shows a significant positive relationship with concerns that AI will take away the enjoyable aspects of their work. Similarly, the mobilisation role is borderline significantly associated with fears that AI will make their work less meaningful and more boring. This suggests that generative AI’s automation potential may disrupt roles where journalists strongly identify with the tasks at risk of replacement. For instance, if generative AI automates or augments the writing process, these roles might become less fulfilling and more monotonous, leading to reduced job satisfaction. Further, journalistic roles shape positive attitudes towards generative AI (see Figure 3). While no significant relationships exist between the benefits of generative AI and the mobilisation or detached observer roles, both the watchdog and entertainment roles show a significant association with higher-rated benefits. This indicates that journalists adhering to these roles see greater advantages in incorporating AI technologies into their work. However, the nature of the specific benefits differs notably between the two roles. Specifically, the entertainment role shows a significant positive relationship with beliefs that generative AI will improve the quality of their work and enable them to reach larger audiences. This suggests that journalists aligning with this role see value in generative AI assisting them in producing engaging content, which is arguably an important part of entertainment journalism. Their relationship with the belief that generative AI can enhance creativity, although only borderline significant, also fits within this understanding, as these journalists may see generative AI as a tool to enrich their content. Conversely, the watchdog role is significantly linked to beliefs that generative AI will improve efficiency and accuracy. Watchdog journalists may view generative AI as a means to enhance the precision and speed of their investigative work by allowing them to, for instance, process and analyse large datasets more effectively. The survey also measures the importance of various issues for news organisations planning to implement generative AI (see Figure 4 for descriptive statistics). Overall, respondents assign high importance to all issues, with fact-checking the output of generative AI rated the highest, followed by the development of internal guidelines for its use. The issue considered least important was building in-house generative AI tools instead of relying on external providers. Examining the influence of journalistic roles on these issues reveals significant relationships (see coefficient table in Appendix 3). The mobilisation role, watchdog role, and detached observer role are all significantly associated with higher index scores for the importance of ethical issues (see coefficient table in Appendix 3). This indicates that these roles share a common concern about the ethical challenges posed by generative AI in journalism. Conversely, the entertainment role does not show a significant relationship with this index, suggesting that ethical implications are considered less immediately critical among journalists showing stronger adherence to this role. Responses to the specific issues reveal different priorities among the roles (see Figure 5). The watchdog role exhibits significant positive relationships with a wide range of issues, including accountability, data sharing policies, the development of in-house tools, and journalist involvement in AI development. This underscores an emphasis on preserving journalistic autonomy and traditional values in the adoption of generative AI, perhaps driven by a desire to maintain professional distance from powerful platform and technology companies. The detached observer role shows significant positive relationships with priorities such as transparency, fact-checking, and the establishment of internal guidelines. These findings reflect the detached observer role’s commitment to objectivity and impartiality, which translates into a heightened sensitivity towards maintaining public trust and upholding the integrity of journalistic practices in the adoption of generative AI in journalism. For the mobilisation role, significant relationships are observed only with the importance of accountability and guidelines. Similarly, the entertainment role only shows a borderline significant positive relationship with the importance of generative AI training and a borderline significant negative relationship with the importance of transparency. These findings suggest that journalists adhering to these roles may place less emphasis on ethical considerations in implementing generative AI, potentially prioritising audience engagement and content creation over issues like accountability or transparency. Use of Generative AI Turning to the second research question, we examine how journalistic role conceptions shape the use of generative AI. Descriptive statistics show that over half of respondents use generative AI at least once per month in their capacity as journalists, with a quarter never using it and just over 10% using it daily. The most common applications involve brainstorming potential headlines and story ideas, while generating or editing illustrations is the least common use. The regression analysis indicates that journalistic roles influence the use of generative AI with particularly contrasts between the detached observer and entertainment roles. These contrasting patterns likely reflect varying professional norms and values associated with each role. To illustrate these differences, Figure 6 displays only the regression coefficients for the two specific roles. All regression results can be found in a coefficient table in Appendix 4. The entertainment role shows a significant positive relationship with the current use of generative AI. Journalists aligning with this role are significantly more likely to use AI for brainstorming story ideas, brainstorming headlines, generating text for articles, and generating or editing illustrations. These findings suggest that while entertainment journalists are among the most concerned about the adoption of generative AI in their work, they are actively integrating the technology into their workflow. This readiness to adopt AI likely reflects the role’s focus on reaching the largest possible audiences, viewing generative AI as a valuable tool for boosting their content. In contrast, journalists who identify with the detached observer role show a borderline significant negative relationship with the current use of generative AI. Specifically, they are significantly less likely to use AI for brainstorming story ideas, researching background information, developing interview questions, brainstorming headlines, and generating text for articles. This reluctance likely stems from concerns that generative AI may undermine the role’s commitment to objectivity, impartiality, and balanced reporting. Looking ahead, journalists see the highest potential of generative AI in information analysis and content optimisation. The lowest rated potential lies in content creation tasks (see Figure 7 for descriptive statistics). As Figure 8 illustrates, the regression analysis of potential applications highlights role-specific expectations (see coefficient table in Appendix 5). The entertainment role shows significant positive relationships with the potential of generative AI in the idea generation phase as well as for news personalisation and platform formatting. These findings align with the current reliance on generative AI for brainstorming among journalists adhering to the role and reflect their belief in AI’s capacity to enhance the appeal and reach of journalistic content. In contrast, the watchdog role is significantly positively related to the potential of generative AI in the content creation and editing phases and shows a borderline significant positive relationship for information analysis, such as summarising documents and processing data. This is consistent with watchdog journalists’ belief that generative AI can improve efficiency and accuracy, aligning with their focus on enhancing the precision and efficiency of their investigative work. Discussion. In this article, we have explored how journalists’ alignment with different role conceptions influences their adoption of generative AI, finding significant variations across roles. The most notable differences emerge between the mobilisation and entertainment roles on the one hand, and the watchdog and detached observer roles on the other. These differences largely align with traditional conceptions of the respective roles, suggesting that journalists’ professional identities play a key role in shaping their approach to generative AI. Broadly speaking, journalists adhering more to the mobilisation or entertainment roles seem more concerned about generative AI’s impact on their work than about the various ethical and operational issues related to its adoption in journalism. Specifically, they are worried about generative AI causing job losses and diminishing job meaningfulness. The mobilisation and entertainment roles are both inherently audience-oriented, focusing on motivating and entertaining broad audiences. This sort of journalism has been found to be more susceptible to economic influences than traditional news journalism. Consequently, these journalists may feel more vulnerable to the economic shifts driven by the automation of content creation through generative AI. Despite this, entertainment journalists, unlike mobilisation journalists, also recognise benefits in generative AI, such as enhancing content quality and reach, and they actively incorporate the technology into their work. This reflects the professional values of entertainment journalists, who have been found to be increasingly profit-oriented and geared towards audience engagement. In contrast, journalists adhering more to the watchdog or detached observer roles exhibit less concern about generative AI’s direct impact on their job security and the meaningfulness of their work, likely feeling more confident that their investigative and analytical tasks cannot be as readily automated. However, they are significantly more concerned with the ethical and operational implications of generative AI for journalism, emphasising the need to uphold journalistic values and standards. Consequently, detached observer journalists do not see much potential in generative AI and largely avoid its use. These journalists may fear that the biases inherent in these technologies will interfere with the values of objectivity and neutrality that their role is closely associated with. On the other hand, journalists showing stronger adherence to the watchdog role are more open to the integration of generative AI in their work, recognising benefits such as improving efficiency and accuracy. However, its use appears to be conditioned on the preservation of journalistic autonomy, with these journalists advocating for news organisations to develop and control generative AI tools with active journalist involvement. This reflects the professional legitimacy of the watchdog role, which is strongly anchored in journalism’s independent institutional position vis-à-vis power structures. These results illustrate that technological advancements impact journalists differently based on their individual role conceptions. This is particularly important to consider in the context of generative AI, which more accurately replicates the craft skills at the core of the journalistic profession compared to previous automation technologies. Roles closely associated with these craft skills view generative AI as a greater professional threat than roles requiring deep investigative work and critical analysis, which tend to view generative AI more as a tool to enhance these processes. However, the results reveal that these differences are more complex. For example, those in entertainment roles, who express the greatest concern about the adoption of generative AI in their work, also use the technology the most. There could be several reasons for this. Perhaps it is the journalists who are most at risk of AI replacement that are primarily encouraged to use it by management, which pushes for AI adoption to boost productivity and efficiency. Or perhaps these journalists have more direct and tangible experiences with the technology, allowing them to better recognise its immediate benefits and potential drawbacks compared to those with more theoretical knowledge of AI. Future research could investigate the nuances of the relationship between AI attitudes and usage through qualitative approaches. Ultimately, this study offers a deeper understanding of the varied and nuanced ways in which journalists engage with AI, moving beyond binary tales of acceptance or resistance to technology. By acknowledging the heterogeneity of the journalistic workforce, we can better grasp the complex dynamics that shape journalism as it adapts to technological advancements. Not all journalists are the same, and their interactions with new technologies vary in ways that both reflect and reinforce their professional identities. Mapping these interactions reveals how technological innovation and professional identities are deeply intertwined in modern journalism. Technology does not merely shape journalism from the outside; it evolves through its interaction with the identities and practices of the journalists who use it. This reciprocal relationship highlights the active role journalists play in the co-evolution of technology and their profession. However, this perspective alone does not capture the full picture of the issue. Journalists’ ability to act in line with their role conception is influenced by the organisations they work for. Despite the widespread accessibility of generative AI tools, their procurement and deployment within newsrooms are often determined by upper management, whose decisions shape what technologies are available and how they are used. These decisions are often influenced by broader strategic goals, such as cost reduction, audience engagement, or competitive positioning, which may not always align with journalistic values. To balance these considerations, many news organisations have introduced ethical guidelines that promote the responsible use of AI-based technologies, offering journalists best practices and structured frameworks to navigate AI tools. However, many of these guidelines are strikingly similar, potentially lacking the specificity needed to address the distinct challenges and opportunities that arise for different journalists (de-Lima-Santos, Yeung, and Dodds 2024). This speaks directly to the practical implications of our study. The complexity highlighted by the varied attitudes towards and uses of generative AI across different journalistic roles demonstrates that a one-size-fits-all approach to its integration is insufficient. Tailored solutions are necessary to address the specific concerns and needs of different journalists. For instance, role-specific training programmes and ethical guidelines could equip journalists with knowledge and skills to understand how generative AI aligns with their own practices, bridging potential knowledge gaps and enabling informed use of AI tools across journalistic roles. Furthermore, fostering cross-role collaboration in the development and implementation of AI tools could help ensure that these technologies complement rather than disrupt the diverse practices and identities of different roles. By embracing a nuanced and role-sensitive approach, news organisations can better navigate the integration of generative AI, balancing efficiency gains with the preservation of journalistic values and role diversity. This study has certain limitations that should be acknowledged. Due to its focus on Danish journalists and the sampling method employed, the findings may not be fully applicable to journalists in other countries with different media environments. To address this, future research could employ larger and more diverse samples to explore whether the findings differ across various national and cultural contexts. Moreover, the study captures a snapshot of attitudes and practices during a specific period, and the rapid evolution of generative AI technologies suggests that these may evolve over time. Longitudinal approaches that track changes in attitudes and practices as AI technologies progress would provide valuable insights into these dynamics. Despite these limitations, this research adds novel insights into the previously underexplored intersection of journalistic roles and AI, highlighting significant variations within the journalism profession that are relevant beyond the national context of this study. These variations are important to consider in further academic explorations of the dynamic co-evolution of traditional journalistic roles and technological innovation. Disclosure Statement No potential conflict of interest was reported by the author(s). Appendices. Appendix 1. OLS regression coefficients predicting how varying role conception scores influence concerns related to the adoption of generative AI, controlling for gender, journalistic experience, and organisation size Appendix 2. OLS regression coefficients predicting how varying role conception scores influence benefits related to the adoption of generative AI, controlling for gender, journalistic experience, and organisation size p <.10, p <.05, p <.01. Cell entries are OLS regression coefficients. p <.10, p <.05, p <.01. Cell entries are OLS regression coefficients. Appendix 3. OLS regression coefficients predicting how varying role conception scores influence the rated importance of ethical and operational issues related to the adoption of generative AI, controlling for gender, experience, and organisation size Appendix 4. OLS regression coefficients predicting how varying role conception scores influence the use of generative AI for various journalistic tasks, controlling for gender, experience, and organisation size p <.10, p <.05, p <.01. Cell entries are OLS regression coefficients. p <.10, p <.05, p <.01. Cell entries are OLS regression coefficients. p <.10, p <.05, p <.01. Cell entries are OLS regression coefficients.