Generative AI risks: are European communication professionals ready? A study on individual and organizational READINESS

Generative artificial intelligence (GenAI) encompasses machine learning models that synthesize novel data outputs, including text, images, or audio, by learning from extensive datasets and probabilistic structures underlying human-like content generation (Baek and Kim, 2023). While GenAI technologies have been increasingly used by individuals and organizations alike (Oh and Ki, 2024), a good understanding of this innovation among communication professionals is lacking (Duckett and Westrick, 2024). In the meantime, communication professionals are increasingly recognizing the profound impact that GenAI is poised to have on their profession (Yue et al., 2024). Interviews with 30 chief communication officers (CCOs) representing the Top 300 listed and private companies in Europe revealed that more than half of these companies already use AI-based tools for monitoring and analysis as well as content creation (Zerfass et al., 2024). A recent survey among nearly 400 communication leaders across the U.S. revealed that approximately 80% of them anticipate AI as a game-changer for shaping the industry's future (USC Annenberg Center for Public Relations and WE Communications, 2023). The integration of GenAI technologies is expected to fundamentally transform the communication profession by augmenting creative processes, personalizing consumer interactions, and optimizing operational efficiencies (e.g. Bowen, 2024; Chintalapati and Pandey, 2022; George and Wooden, 2023; Haleem et al., 2022). Reeves (2016) asserted that GenAI's ability to alter the nature of human-machine communication and serve as “social actors” can potentially devalue the professionals working in such roles and threatening their positions. The interviews with European CCOs revealed several tensions and hurdles including a lack of suitable competencies and acceptance among staff as well as concerns about technical and ethical risks (Zerfass et al., 2024).

In light of this emerging trend, it is crucial for communication professionals to understand how to foster READINESS for GenAI-related risks at both the individual and organizational levels, as well as to develop strategies for identifying and mitigating these risks within the workplace. This aligns with Gehl and Bakardjieva (2017)'s call for empirical research to gain new insights on the implications of human-machine communication, as GenAI technologies “disrupt theories and norms of communication developed around anthropocentric assumptions” (p. 81). While scholars in other fields have attempted to address this call, only limited research has been conducted in the field of public relations (Bowen, 2024; Oh and Ki, 2024; Yue et al., 2024; Zerfass et al., 2024).

Our research aims to fill existing gaps by exploring how communication professionals perceive GenAI-related risks in the workplace, and what is considered important for READINESS at individual and organizational levels in the context of GenAI adoption. Following Jin et al. (2024a, b), we define READINESS as a mindset, a multilevel efficacy, and a dynamic process. This approach allows us to distinguish between individual READINESS, which pertains to “an individual-level belief in one's ability to complete tasks” (Jin et al., 2024a, p. 7), and organizational READINESS, which “reflects perceptions of the organization's ability to cope with the uncertainties created by a crisis” (Jin et al., 2024a, p. 7). Although these two levels of READINESS are interconnected, with individual READINESS laying the foundation for organizational READINESS (Jin et al., 2024a, b), each level has unique implications for GenAI adoption and management efforts, and thus needs to be examined separately. By utilizing the READINESS concept to enhance the understanding of GenAI-related risks, this study expands the READINESS framework (Jin et al., 2024a) by situating it within the challenges posed by GenAI, thereby contributing to strategic communication research. Also, this study advances the growing body of research on human-machine communication (Guzman and Lewis, 2020) by examining the dual nature of GenAI as both a functional tool and an autonomous communicator in workplace environments. Furthermore, this study contributes to the theory of disruptive innovation (Christensen et al., 2018; Si and Chen, 2020) by exploring how GenAI is reshaping professional roles and organizational dynamics, with a focus on the professional area of communication. By bridging insights from risk and crisis communication and AI governance, this research provides a nuanced perspective on how organizations can cultivate both individual and organizational READINESS for the challenges posed by GenAI.

We conducted written interviews with open-ended questions in a survey format to gather input from communication professionals in various workplace settings across three European countries—Italy, the Netherlands, Romania. The final sample was analyzed using thematic analysis to understand: (1) the types of risks communication professionals anticipate in relation to GenAI adoption in the workplace, (2) how they conceptualize individual and organizational READINESS in response to these risks, (3) the personal and situational factors deemed critical for enhancing individual READINESS, and (4) the aspects of digital and physical work environments essential for building organizational READINESS. By identifying key themes and patterns, this research aims to provide insights into the critical factors that communication professionals can consider to enhance their individual and organizational READINESS in dealing with GenAI-related risks within the workplace.

Historically, communication has been conceptualized as first and foremost a human process (Dance, 1970; Guzman and Lewis, 2020), with the paradigms of communication theories drawing on human-human communication (Craig, 1999) mediated by technologies (Schramm, 1972). Today, advances in GenAI have not only narrowed the gap between technologies and communication research, but also called for devoting “great attention to understanding increasingly life-like and communicative AI technologies, people's interactions with them, and their implications” (Guzman and Lewis, 2020, p. 71). Scholars argue that beyond facilitating communication, GenAI has also played a significant role in automating communication (Reeves, 2016) and the social processes dependent on it (Gehl and Bakardjieva, 2017). Particularly, since GenAI is designed with human social cues, people often perceive it as “social actors” (Guzman and Lewis, 2020). Consequently, Guzman and Lewis (2020) developed the human-machine communication framework, outlining three dimensions for researching GenAI technologies, including how people make sense of the devices and their applications as communicators, how people connect with them and relate to others, and what constitutes communication in contemporary contexts given the blurred ontological divide between human and machine. Their framework provided a theoretical lens for communication scholars to shed new light on human-machine communication research and advocated for more studies in this discipline.

Scholars widely recognize GenAI as a disruptive technology (Si and Chen, 2020), a concept that builds upon the disruptive innovation theory proposed by Christensen et al. (2018) and emphasizes the duality of disruptive innovation—as a process and an outcome. Rather than assuming that innovation is inherently disruptive, it posits that the process of changing the market reflects the disruptive aspect (Dogru et al., 2019). In this vein, GenAI technologies fundamentally changed how industries operate in various aspects, including but not limited to introducing new efficiencies through automating complex tasks that traditionally required human intelligence (e.g. data analysis, decision optimization, customer service), and transforming professional roles such as creating new positions in data science and GenAI ethics, as well as requiring existing workers to upskill and reskill (Bowen, 2024; Brougham and Haar, 2018; Păvăloaia and Necula, 2023).

Recognizing the power of GenAI technologies to disrupt the status quo through developing new sets of values, scholars argued that innovation can cause broader societal transformation by changing the existing sectors, working principles, manufacturing processes, consumption behaviors and more (Bongomin et al., 2020). Research in marketing (Huang and Rust, 2021), management (Shrestha et al., 2021), organizational studies (van Esch et al., 2019), and information systems (Bawack et al., 2021) has demonstrated some attempts to examine this ongoing transformation. Through reviewing 97 articles studying disruptive technologies, Păvăloaia and Necula (2023) concluded that GenAI is recognized as the most prominent disruptive technology and “The debate over how to handle the development of disruptive technologies is urgent and probably inevitable since the future is unforeseeable” (p. 9).

Taking ChatGPT as an example, it is an advanced language model that “utilizes transformer-based neural network architecture to understand complex language structures and generate contextually relevant and coherent responses” (Hu et al., 2023, p. 22). Its superiority lies in its conversational capability and the fact that it is trained on a diverse range of text data from various sources. In addition to providing professionals with a wealth of information and insights, ChatGPT can improve through feedback offered by users, enabling it to deliver more desirable solutions. This has led people to perceive GenAI like ChatGPT as a “source” of communication rather than merely a “medium” of communication, aligning with the human-machine framework recently proposed by Guzman and Lewis (2020). This shift marks the beginning of a new AI era since 2022, with GenAI technologies evolving from performing administrative tasks to becoming cutting-edge tools—termed the AI Boom or AI Spring (Yue et al., 2024). However, it is important to note that GenAI may “propagate false information in a seemingly trustworthy manner” (Sundar and Liao, 2023, p. 174), prompting scholars to call for further research on this and other possible risks of utilizing GenAI-generated content (e.g. Chintalapati and Pandey, 2022; Haleem et al., 2022; Ma et al., 2023).

Given the growing popularity of ChatGPT and GenAI technologies in general, scholars have emphasized the need to adopt these innovations in the workplace safely by not only considering performance metrics but also assessing risks (Bowen, 2024; Getchell et al., 2022). It is crucial for communication scholars to understand how communication professionals perceive GenAI-related risks at work and what constitutes their READINESS in response to these risks. Thus, our first research question (RQ) in the current study is:

Following the notion that one must earn a place in the market every day, communication professionals in various workplace settings need to be prepared to face AI risks (López Jiménez and Ouariachi, 2021; Shekhar, 2019). For example, in the communication sector, public relations (PR) are regarded as the discipline responsible for managing reputation and enhancing trust. However, the adoption of GenAI may lead to a diminished perception of transparency and authenticity (Galloway and Swiatek, 2018; López Jiménez and Ouariachi, 2021). This challenge is further intensified by the scrutiny of GenAI's influence on institutional ethics and organizational governance, which, in turn, impacts leadership decisions and shapes stakeholder perceptions (Gregory and Willis, 2022). Bowen (2024) argued that public relations is placed “at the apex of responsibility for AI use and AI mistakes”, resulting in “a dual role of AI users and AI explainers for the field” (p. 2). To navigate complex contexts and utilize GenAI tools in the workplace safely and effectively, PR professionals must acquire a solid understanding of “the technological affordances embedded within the technologies, the norms and expectations of the technologies by their strategic publics, and employ critical thinking in deciding communication outreach protocols” (Yue et al., 2024, p. 2). In other words, rather than blindly endorsing the adoption of GenAI technologies, it is crucial for professionals to adopt critical perspectives on GenAI to ensure that this disruptive technology prioritizes human values and ethics (Swiatek et al., 2024).

The critical approach aligns with the READINESS concept introduced in risk and crisis communication literature (Jin et al., 2024a), which encompasses three dimensions—mindset, multilevel efficacy, and dynamic processes. First, Jin et al. (2024a) posited that a READINESS mindset entails having “the optimal mentality and willingness to adapt” (p. 7). This refers to the belief that the willingness to learn and openness to new ideas will enable an organization to emerge stronger after the crisis. To foster GenAI risk READINESS, such a belief is essential for communication professionals to identify risks related to utilizing GenAI at workplace, and thereby resist against the temptation of blindly supporting the adoption of GenAI technologies or simply celebrating the positive outcomes of the innovations. Jin et al. (2024a) argued that a mental adaptability is embedded in the READINESS mindset and built on individuals' psychological resilience—how mental processes and behaviors protect an individual from the potential negative effect of stressors (Nindl et al., 2018). Individuals being protected by a strong psychological resilience are better equipped to mentally adapt to uncertain situations such as handling GenAI-related risks.

Second, the multilevel efficacy dimension of READINESS draws on the ability of individuals and organizations to achieve desired outcomes (Bandura, 2006; Jin et al., 2024a), such as completing a task or reaching a goal. Its importance lies in the belief that “exerting effort on the job does matter” (Jin et al., 2024a, p. 7), so that individuals and organizations can create an effective response to an uncertain situation. Linking this READINESS dimension to the context of GenAI adoption, multilevel efficacy not only entails confidence in managing GenAI risks, but also the decision-making agility. On the one hand, multilevel efficacy fosters confidence in professionals' and organizations' ability to assess and respond to GenAI-related risks, such as data privacy concerns and ethical challenges (Getchell et al., 2022). Higher multilevel efficacy implies that communication professionals and organizations are more likely to approach these challenges proactively. On the other hand, with strong multilevel efficacy, communication professionals are more capable of making timely and informed decisions when facing GenAI-related risks. They trust their judgment and capacity to adapt, which enhances agility in high-stakes situations.

Third, the dynamic process dimension of READINESS emphasizes that communication professionals must be prepared to face risks, a fundamental aspect of effective crisis management (Jin et al., 2024a). In the context of GenAI-related risks, communication professionals are expected to not only anticipate the types of risks they may encounter but also rapidly recognize the warning signs associated with each risk type (Coombs, 2023). However, certain GenAI-related risks, particularly those that are more abstract or difficult to quantify, such as ethical dilemmas, algorithmic biases, or unintended social consequences, pose a significant challenge to achieving full READINESS. These less tangible risks make it harder for communication professionals to develop preemptive strategies, as their potential impacts are often unclear and can evolve over time. Thus, it becomes critical to explore how GenAI risk READINESS can vary depending on the specific types of risks communication professionals are likely to face. Jin et al. (2024a) argued that when risks are easier to quantify, professionals exhibit stronger READINESS, as these risks are more straightforward to predict and mitigate. Conversely, for risks that are more complex or difficult to measure, READINESS tends to be weaker, as uncertainty increases the difficulty of developing concrete preparedness measures. Accordingly, advancing GenAI risk READINESS requires the development of adaptive strategies that enable communication professionals to respond effectively to ambiguous or evolving risks. This leads to the second research question of the current study:

Jin et al. (2024a) emphasized the importance of distinguishing between individual READINESS, grounded in self-efficacy, and organizational READINESS, rooted in collective efficacy. This distinction is supported by findings from Yue et al. (2024), which indicate that communication professionals anticipate GenAI's impact on both organizational and personal levels. Individual READINESS forms the foundation for organizational READINESS, as communication professionals must believe that their actions will lead to positive outcomes. This belief enables them to persist in the face of challenges, including difficult-to-quantify GenAI-related risks. In addition to individual and collective efficacy, the broader organizational context–including digital infrastructure, physical workspaces, and formal support systems–can also play an important role in shaping READINESS. Recent discussions on AI adoption highlight the significance of such environmental factors in influencing both individual adaptation and organizational responses (Bowen, 2024; Yue et al., 2024). However, individual READINESS alone is insufficient for fully mitigating such risks. Without organizational READINESS (i.e. the collective capability to address and manage uncertainties; see Jin et al., 2024a), GenAI-related risks may overwhelm even highly capable individuals, as the lack of coordinated action, shared strategies, and unified decision-making can hinder effective organizational responses. Moreover, organizational READINESS is not merely the sum of individual READINESS levels within a company (Jin et al., 2024a, b). It reflects the organization's collective capacity to cope with uncertainties and achieve its objectives, as well as its structural and cultural ability to overcome obstacles. If an organization achieves a high level of collective efficacy but its professionals lack individual READINESS, it may struggle with execution, as its employees may not have the confidence or perseverance to implement strategies effectively.

In both scenarios—whether individual READINESS is achieved without organizational READINESS, or vice versa—GenAI-related risks cannot be fully assessed and mitigated. Therefore, a holistic approach is required, where individual and organizational READINESS are aligned to foster both personal and collective resilience, enabling organizations to effectively address the complex and evolving risks posed by GenAI technologies. Understanding what drives READINESS at both levels is essential for creating comprehensive strategies to handle GenAI-related risks across the organization. To understand the factors that contribute to individual and organizational READINESS, we present the following research questions:

To provide an answer to these four research questions, we conducted written interviews with open-ended questions in a survey format between May and July 2024 with open-ended questions aimed at gathering input from communication professionals in various workplace settings (both private and public organizations) including corporate communications, marketing, PR, advertising and information technology. Communication professionals in these workplace settings share a range of common skills and qualities due to the overlapping nature of these fields, including client-centric focus, strategic thinking, problem-solving, and the essential role of adaptability at work. A nonprobability technique of purposive sampling was used to recruit participants among the researchers' social networks (Silverman, 2014) in their respective countries. This technique made it possible to specifically identify and recruit participants who belonged to the professional field of interest (i.e. communication or communication consultancies), who had relevant knowledge about the phenomenon of interest given that GenAI has been introduced and was actively implemented within their respective workplaces, and who were willing to articulate their perceptions and practical experiences in a reflective manner as the online written interview requires greater commitment and motivation than the face-to-face interview (Bryman, 2012). Regarding the sampling criteria, participants had to be employed in companies or communication consultancies adopting GenAI and had to have professional experience and expertise in the professional field of communication (3 years or more). Those working in communication consultancies were approached in their role as communication professionals in their own working environment, without taking into account their experience with external clients. Ethical approval (ETH2324-0704) was obtained from the internal review committee of the lead researcher's institution. In total, we collected 84 responses: 33 from Italy, 18 from the Netherlands, and 33 from Romania. While these are the countries in which the researchers work, in their selection we have primarily considered the increasing use of AI technologies by companies in these same European countries (Eurostat, 2025). Moreover, the three countries were chosen to capture variation across different geographic regions of Europe, providing a broader contextual basis for the study. Following qualitative research standards (Braun and Clarke, 2012), data collection was stopped upon reaching thematic saturation, where additional responses no longer generated substantially new insights and no new major themes emerged. Therefore, the final dataset of 84 written interviews was considered adequate to explore the research questions with sufficient depth and variation across participants, resulting in a corpus of approximately 14,000 words. Responses varied in length, ranging from concise to extensive and reflective.

The written interview began with a consent form, followed by the main section which included eight open-ended questions. This question format was chosen as the best fit for this qualitative research, useful for exploring both new areas and areas of limited knowledge. Respondents can answer in their own terms and unusual responses not considered by the researchers can still be inferred (Bryman, 2012). Additionally, the asynchronous nature of the online written interview gave participants the opportunity to provide more considered and reflective responses compared to real-time face-to-face interviews (Bryman, 2012). Questions revolved around the four research questions derived from the literature review, i.e. the types of risks that communication professionals anticipate in relation to the adoption of GenAI in the workplace, individual and organizational READINESS in response to GenAI-related risks, the personal and situational factors that they see as critical for improving individual READINESS, and the aspects of the digital and physical work environments that they see as essential to building organizational READINESS. The written interview concluded with several demographic questions gathering contextual information about the respondents. The instrument can be found in the  Appendix.

As is common in cross-cultural research (Bryman, 2012), each of the three researchers was responsible for conducting the study in their respective country while working closely together to ensure comparability of procedures for administering the research instrument and analyzing the data. The interview questions were initially developed jointly in English and the interview was administered in this language. For Italy only, the questions were back-translated into Italian to give participants the opportunity to answer in their preferred language and then translated back into English for the analysis phase.

We adopted a thematic analysis method (Braun and Clarke, 2012) for systematically identifying, organizing, and reporting repeated patterns of meaning (themes) across the entire dataset. The thematic analysis in this study was developed through a combination of deductive and inductive approaches (Braun and Clarke, 2006): On the one hand, the study's four research questions represented the basis for establishing the core themes, which allowed us to transfer a number of concepts and ideas derived from the literature review to the data coding and inform our perspective; on the other hand, during the coding process, the subthemes were developed according to a data-driven approach to analysis, taking into account the unique experiences and contexts of the participants.

A coding table was used by the two researchers involved in the analysis in the iterative process of generating codes, searching, reviewing, defining and naming themes and related subthemes with illustrative extracts (Braun and Clarke, 2006). An intercoder reliability test (O'Connor and Joffe, 2020) between the two researchers generated a satisfactory average percentage agreement (above 80%) and enabled the coding team to eliminate redundant or overlapping themes and subthemes.

Table 1 summarizes the seven core themes identified as central in the literature review, nuanced by subthemes that emerged inductively during the data analysis. These themes are organized to address the research questions by covering the main areas of focus: (1) factors influencing individual READINESS, (2) factors influencing organizational READINESS, (3) perceived risks associated with GenAI adoption, and (4) support mechanisms needed to enhance READINESS. The core themes align closely with these dimensions, offering a conceptual framework for understanding how communication professionals perceive and navigate GenAI-related challenges. While the core themes were guided by insights from the literature review, the sub-themes emerged inductively from participants' written responses, allowing a more nuanced and context-specific exploration of the topics. Together, the themes and subthemes structure the presentation of the findings, which are further detailed in the following sections with illustrative examples and participant quotes from the written interviews.

Participants identified several potential risks associated with the use of generative AI technologies in the workplace. First, data security and protection issues: Participants pointed to the risks of data leaks and security breaches, confidentiality and privacy. This concern was expressed with words such as: “When giving it data, you can never be sure that it won't be used afterwards” (R26) or “With employees using these tools, what are you opening up to?” (R6).

A second area of concern was the over-reliance on AI leading to mental laziness and dependency, loss of creativity, banalization and homogenization of content, erosion of human communication, excessive automation of work, and ultimately a decline in cognitive and technical skills. For example, respondents said “There is a risk of being tempted to leave all the dirty work to AI without making the effort to think for yourself” (R77) and in the end communicators risk being “less professional, less informed and less capable to do research” (R53).

Third, information integrity was frequently mentioned when framing the negative impact of the use of AI on the accuracy and quality of results, information overload, the relativization of truth and reputational risks. In particular, participants emphasized that AI information is often “false” (R3), “unreal” (R1), “misleading” (R33) and “not source-based” (R2), and that AI is prone to the “risk of hallucinations” (R64 and R81). Concern about the impact on the company's reputation was also central as “AI-generated assets may create an inaccurate representation of the company” (R5) and/or “a flattening of communication” where the corporate identity is no longer differentiated from others.

Forth, the impact on employment and on decision-making was also highlighted. In the words of R5: “Companies will deem communication professionals unnecessary and simply invest in AI, which may provide consistency, but which I believe may reduce or completely eliminate the human touch and the potential for creative genius”.

Fifth, another important risk area was related to the preparedness of the organizational culture: participants mentioned the inadequacy of training, education and guidance to ensure the integrity of decision making. For example, R12 emphasized: “Employers are lagging behind in providing their employees with the necessary education and training to keep them on track. For me personally, this represents a way bigger risk than the generated knowledge itself”.

Sixth, the ethical and legal implications of using generative AI technologies in the workplace was a key topic, highlighting a number of challenges related to bias, irresponsibility, copyright, bureaucracy and confidentiality, and overall reiterating the need for guidelines and policies for the proper use of AI.

Overall, R30 was a voice out of the chorus: “I don't really see a major risk at the moment (…). We use AI mainly to make our work easier, to help us, and it's pretty good at it, to summarize, to synthesize a lot of information. It can give us more time to think about the tasks that really matter. (…) What AI gives us at the moment is not good enough yet, it's raw and needs a human touch to be delivered further.”

Participants elaborated on their own conceptualization of individual READINESS, detailing a number of adaptive individual characteristics and strategies that enable professionals to respond effectively to AI-related risks. First, participants linked individual READINESS to cognitive awareness and the propensity for adaptive learning. They emphasized the importance of developing an awareness of AI and the associated risks and maintaining solid and up-to-date knowledge in this area through adaptive learning attitude and behavior towards technological change. For example, R82 stated “I believe that the decisive issue is reflection. To be ‘ready’ you need to understand what you are talking about, only then can you focus clearly on opportunities and risks”. In the words of another participant: “Individual READINESS in the face of the risks associated with generative AI involves preparing oneself to effectively understand, navigate and mitigate these challenges through a combination of personal skills, knowledge and proactive strategies” (R22).

Second, participants also highlighted critical and ethical conscience to be the pillars of individual READINESS, i.e. the capacity to apply critical thinking and ethical responsibility in AI engagement. This indicates the need for a reflective use of AI through “awareness of the medium, verification of content and sources, in-depth preparation” (R74) and “constant vigilance” (R64). Linking these specific aspects to the conceptualization of individual READINESS, R84 defined it as “a meta-skill of reading complexity and systemic analysis of what is happening in change”.

Third, individual READINESS was linked in some way to organizational preparedness as a framework to guide adaptive individual traits and strategies. Therefore, individual READINESS also depends on the organizational thinking and strategies related to AI, including strategic plans, rules, methods, support teams and coordinator roles. However, the participants clarified that most organizations today are not ready to consciously and systematically engage with AI, as “there is no structured READINESS” (R20) and “many companies are still lagging behind”.

Participants also explained their own conceptualization of organizational READINESS and offered their perspective on the adaptive organizational characteristics and strategies that, in their experience, enable organizations to respond effectively to AI-related risks. The first characteristic they cited was organizational awareness of AI uses and implications including a collective understanding and assessment of both the benefits and risks of AI. Weighing the benefits and risks is not just about the impact on organizations, but goes beyond that: “Organizations should obviously also look at the way AI can be a benefit and a threat to them, but they should also look broader: what role could and should they play in mitigating the risks to their stakeholders?” (R1). This led to strategic planning for AI integration and risk management as the second characteristic. R57 explained that the best preparation for companies is to “plan the introduction of these technologies into (communication) processes, define when to use them, for what activities, with what rules to protect themselves”. Proactive risk management of AI therefore requires the organization's ability to “intercept ongoing developments (…) and possible negative or critical feedback” (R84). In this regard, R72 posed a challenging question: “Organizations will have to remodulate control practices. It is one thing to verify the correct use of a process, another thing to entrust a complex task to an AI: How will the control be carried out?”.

Third, governance in the implementation of AI was another important aspect cited in all interviews. The participants associated it with all the technical, ethical and legal policies and guidelines that should form “the right frameworks and controls” or “guardrails” (R3) for the introduction of AI in the workplace and ensure its correct application in the professional fields of communication. In the words of R25, such policies and practices should “prioritize transparency, fairness and the well-being of employees. This includes supporting measures to prevent job displacement, ensuring that AI systems are transparent and explainable, and promoting an environment where human creativity and judgment are valued alongside technological innovation”.

Forth, in addition to top-down strategy and governance, the participants emphasized that the development of AI-related skills is essential for the creation of a healthy AI culture in a cultural change perspective. Employee training and education allow, as R58 put it, “to consider the complexity and long-term of a transformation by working as a system that facilitates the change of mindset, not just learning how to use tools”. In this regard, organizational READINESS was associated with “adaptability” (R31) and “flexibility” (R14), which can be fostered through education and training. Interestingly, R14 mentioned that “the makeup of organizational READINESS is not very different from personal READINESS (…). The main differences (…) are flexibility and speed. (…) The smaller the company, the more straightforward its operations and procedures will be” when it comes to exploiting the opportunities associated with AI and mitigating the risks associated with it.

Participants identified both internal (individual) and external (organizational) factors for developing individual READINESS, referring to the personal attributes, skills, and external support systems that enable professionals to respond effectively to the risks associated with AI adoption. First, individual awareness emerged as a critical factor, emphasizing the importance of recognizing the potential risks posed by generative AI and understanding how these risks could impact one's professional and personal life. Respondents frequently discussed the need to actively seek knowledge about these risks and their implications. For instance, one participant stated “I need to know why these risks exist and what their potential consequences are” (R2), while another highlighted that “awareness is a very important factor when considering the risks associated with artificial intelligence. Getting information from as many credible sources as possible can help raise awareness of the risks” (R31), particularly because “generating content can generate opinions” (R70). Similarly, the ability to understand both the opportunities and limitations of AI was noted as essential, namely “understanding how and at what level the AI can support the work and its degree of reliability” (R79). Ethical and critical thinking was another component of individual awareness. Respondents stressed the importance of evaluating AI outputs critically and ethically, of questioning “the accuracy and bias of AI-generated content” (R27).

Second, individual skills development, including technical and analytical skills, an openness to continuous learning and information gathering, as well as cognitive flexibility and resilience, was also seen as a key factor for developing individual READINESS. Respondents emphasized the necessity of continuous learning to stay on top of advancements in AI technologies, also highlighting the importance of being “mentally prepared, accepting that the world could change drastically between now and 5 years” (R1). As one participant articulated, a way to mitigate AI risks through individual READINESS is “to stay informed about how AI is evolving, what new tools are launched, how it can be used, and how others are using it” (R30).

Third, peer support through peer learning and knowledge sharing was deemed essential for fostering individual READINESS, providing a supportive environment for individuals to address AI-related challenges effectively; some respondents went as far as saying they also need social proof when it comes to AI use: “I need to see that other people are taking genAI seriously in order to start acting upon my preparedness for it too” (R2).

Participants identified several structural, cultural and strategic factors they deemed essential for developing organizational READINESS. First, perceived organizational support was seen as a relevant factor enabling organizational READINESS, with respondents underscoring the value of management guidance and support (R11), no matter the form: “a dedicated crisis management team or, at least, a professional/consultant” (R43).

Second, support for skills development was widely regarded as a foundational factor. Respondents emphasized the necessity of providing employees with training programs that focus on AI technologies and their use: “implementing comprehensive training programs for employees to effectively use AI tools” (R24). Another respondent highlighted the importance of adapting training programs to the pace of AI advancements, noting that “staying updated on the latest AI technologies and trends is crucial. This involves regularly engaging in training programs, workshops, and courses” (R25).

Third, clear guidance through ethical policies and governance structures was also seen as important for fostering organizational READINESS. Participants stressed the need for transparent and enforceable policies to guide AI adoption and mitigate risks. As one respondent put it, “It must be clear what the rules are, there must be clear policies and ethical guidelines” (R41). Others pointed to the role of governance in establishing trust, referring to “clear roles and responsibilities regarding the use of AI” (R3) as well as “key processes put in place, explaining the role of AI” (R33).

Forth, risk management and preparedness were frequently mentioned as critical factors, with respondents highlighting the importance of scenario planning and proactive risk assessments: “the organization must constantly monitor and control the impact on the work, on the individual and on the system” (R83). Another mentioned that a proactive risk management approach ensured that organizations could adapt quickly to challenges posed by AI, but for this the organization must have a “vision” (R15, R70) regarding the role AI can play in organizational change and innovation.

Fifth, investments in resources and infrastructure were also emphasized as key to organizational READINESS. Respondents pointed to the need for advanced technological tools and financial resources to support READINESS initiatives and to “effectively integrate and utilize AI technologies” (R42).

Lastly, respondents emphasized the importance of organizational culture and change management, particularly in relation to adaptability, flexibility, collaboration, and team dynamics. Participants noted that an adaptive culture is necessary to effectively integrate AI into existing workflows. Collaborative team environments were also highlighted, with another respondent stating that “collaboration (…) environments favor team work and dynamism” (R77).

When referring to the aspects of the physical work environment necessary for building organizational READINESS, collaborative and creative spaces emerged as a relevant aspect. These encompass open and flexible spaces for teamwork and collaborative learning. Respondents highlighted the significance of these spaces in fostering collaboration and innovation, the need for “workspaces–open and flexible areas that encourage interaction and brainstorming [and] creative zones–spaces equipped with tools and resources that stimulate innovation and creative thinking” (R24). Another respondent mentioned the value of spaces with collaborative labs, noting that they provide opportunities “to experiment, get to know, and exchange with colleagues” (R73).

Participants also pointed out the importance of creating safe spaces, which address ergonomics, safety, and health, as well as promote a positive and welcoming atmosphere. Safe spaces were described as essential for ensuring employee well-being and fostering a sense of security. One respondent highlighted the need for “comfortable and safe spaces for aggregation and individual work” (R58), emphasizing the importance of designing environments that balance functionality with comfort. Another focused on the broader impact of safety and health on READINESS, noting that “a bright, welcoming, and well-organized place promotes employee comfort and sense of belonging and … must be safe and guarantee the psycho-physical health of those who work there” (R83). However, while several participants stated that the physical environment does not play such an important role with respect to AI risks and READINESS, they also pointed out the need to maintain some form of “physical workplaces” (R65, R67, R71).

When referring to the aspects of the digital work environment necessary for building organizational READINESS, several aspects came up, such as technological infrastructure, training processes, collaboration and communication tools, and organizational culture and governance. First, in relation to technological infrastructure, the participants mentioned the need for access to efficiency and productivity tools and platforms: “access to useful programs that assist employees–this helps streamline tasks and improves job performance. Access to high-performance equipment–this enables employees to work more efficiently and produce higher-quality results” (R24). Flexibility in remote work was another crucial aspect, as one participant noted the importance of “powerful and connected digital tools, usable anywhere regardless of the physical work location” (R71). Additionally, respondents mentioned the need for strong network and internet connection, as well as for digital security and data protection: “strong cybersecurity protocols are essential to protect sensitive data and maintain trust. This includes firewalls, encryption, multi-factor authentication, and regular security audits” (R25).

Second, in addition to technological infrastructure, an adapted (often hybrid) educational infrastructure was highlighted as a relevant aspect of digital work environments. Respondents emphasized the importance of training on digital literacy, emerging technologies, and AI, as well as encouraging continuous learning and knowledge sharing: “comprehensive training programs [provide] resources for upskilling employees in the effective use of AI and other technologies to maintain motivation and a strong work ethic” (R24). Another participant noted the need for ongoing updates and shared knowledge: “staying updated with what's new regarding AI is the most important thing that we can do. Maybe assigning someone to monitor the news about AI daily and, at a specific amount of time (weekly, monthly, etc.) to make a sum-up and give it to the rest of the team, a sort of newsletter” (R30).

Third, collaboration and communication tools were also seen as playing a relevant role, with respondents highlighting the importance of seamless connectivity and access to user-friendly platforms for interaction and collaboration: “quick, barrier-free access to the network and the ability to use remote work sharing tools” (R66).

Forth, respondents highlighted the role of organizational culture and governance in fostering READINESS by establishing clear governance and policy frameworks to ensure secure and efficient digital operations. For example, one participant stated that “AI strategy and governance, adaptation of AI tools and platforms, governance and data management” are essential for aligning digital initiatives with organizational goals (R42). Additionally, participants emphasized the need for organizational READINESS and adaptation, with one respondent observing that “building an organization's readiness virtually requires constant training and adaptations” (R9).

Through advancing the understanding of GenAI-related risks and the concept of READINESS at both individual and organizational levels, this study addresses several gaps in the literature. First, drawing on Jin et al. (2024a, b), we expanded the multilevel framework of READINESS by contextualizing it within the emerging challenges posed by GenAI. Our findings highlight the distinct, yet interconnected nature of individual and organizational READINESS, emphasizing how psychological resilience (Nindl et al., 2018) and collective efficacy (Bandura, 2006) form the foundation of adaptive responses to GenAI-driven disruptions. At the individual level, psychological resilience is reflected in employees' cognitive awareness and propensity for adaptive learning which are essential for their successful adaptation to GenAI-integrated work environments. Their ability to apply critical thinking and uphold ethical responsibility in GenAI engagement also contributes to enhancing individual READINESS. At the organizational level, strategic planning for GenAI integration and risk management are found to play a crucial role in developing collective efficacy, thereby shaping an organization's overall READINESS to respond proactively to technological shifts. Throughout this process, communication professionals recognize AI governance as crucial for organizations in consolidating the foundation for adaptive responses to GenAI-driven disruptions. Drawing on these insights, our study highlights the importance of ensuring that GenAI adoption aligns with employees' expectations and ethical standards, in order to foster strong individual and organizational READINESS for ensuring a responsible integration of GenAI into workplace settings.

Moreover, this research supports Guzman and Lewis's (2020) framework for human-machine communication, illustrating the duality of GenAI as both a tool and a communicator in workplace settings. In this vein, participants identified GenAI's potential to compromise data integrity (Sundar and Liao, 2023) and organizational transparency (Bowen, 2024), which aligns with existing concerns in public relations and communication ethics (Galloway and Swiatek, 2018). By linking individual awareness and adaptive learning to organizational policies and cultural frameworks, our study echoes the call for integrating technological affordances with ethical considerations in a systematic way (Yue et al., 2024).

Additionally, the findings also contribute to the theory of disruptive innovation (Christensen et al., 2018; Si and Chen, 2020), particularly the role of GenAI in reshaping professional roles and organizational dynamics. As organizations integrate GenAI into their workflows, traditional job functions are being redefined, requiring new skill sets and adaptive capabilities among employees. This shift emphasizes the dual nature of GenAI—as both an enabler of efficiency and a source of uncertainty. It requires organizations to carefully balance the automation process with the value of human oversight. For instance, the emphasis on risk management and proactive governance highlights the necessity of aligning technological advancements with human-centric values to ensure GenAI's ethical and sustainable adoption. Without clear guidelines and governance frameworks at the organizational level, the rapid deployment of GenAI risks can intensify ethical dilemmas, particularly bias in decision-making and breaches of transparency (Wei et al., 2025).

For practitioners, the study implies actionable strategies to enhance READINESS for GenAI-related risks while acknowledging that not all organizations or communication professionals are currently utilizing or integrating GenAI into their workflows. Companies that do adopt GenAI should prioritize cultivating a culture of adaptability and openness, as emphasized by Jin et al. (2024a), through targeted training and skill development initiatives. However, for organizations that are hesitant to implement GenAI, understanding the broader industry trends and potential implications remains crucial for strategic decision-making. Providing structured guidance, such as ethical GenAI policies (Gregory and Willis, 2022), can bolster both individual confidence and collective efficacy, fostering a READINESS mindset to GenAI challenges. Also, the findings highlight the importance of strengthening psychological resilience among communication professionals to address uncertainties associated with GenAI adoption. Companies that engage with GenAI can implement peer-support systems and continuous learning platforms to encourage knowledge sharing and adaptive learning (Brougham and Haar, 2018). Meanwhile, those who have yet to integrate GenAI can focus on building fundamental digital literacy to ensure future preparedness. The emphasis on data security, transparency, and ethical governance further suggests that companies should invest in robust technological and procedural safeguards to mitigate risks related to GenAI deployment (Bowen, 2024; Sundar and Liao, 2023), while also considering whether and to what extent GenAI aligns with their strategic goals (Zerfass et al., 2024). Moreover, managers must recognize the nuanced nature of GenAI's impact on employees and the broader communication profession (Yue et al., 2024; Zerfass et al., 2024). Not all communication professionals will experience the same level of exposure to GenAI-driven changes, and its adoption should not be assumed as a universal requirement for success. By integrating collaborative tools and fostering open communication channels, managers can better align technological innovation with human creativity, minimizing the risk of over-reliance on GenAI while maximizing its potential for operational efficiency. This balanced approach ensures that GenAI serves as a complement rather than a replacement for strategic communication efforts, allowing organizations to remain flexible and adaptive in the face of GenAI-related risks.

Despite its contributions, this study has several limitations. First, the sample size and geographical scope limit the generalizability of the findings. While the study focused on communication professionals and communication consultants, who play a critical role in shaping organizational responses to GenAI, the sample, although diverse in workplace settings, does not account for perspectives from other professional domains where GenAI adoption and impact may differ. Expanding the sample to include professionals from various industries – such as healthcare, finance, and education – could provide a more comprehensive understanding of how GenAI is perceived and integrated across sectors, as different industries may have distinct regulatory frameworks and ethical considerations regarding GenAI use. Also, incorporating participants from different culture backgrounds would allow for cross-cultural comparison, shedding light on how societal norms and regulatory frameworks shape attitudes toward GenAI. Cultural values can shape trust in GenAI, perceptions of its ethical use, as well as expectations regarding transparency and governance. For instance, societies with stronger data privacy regulations may exhibit higher concerns about GenAI-driven automation, while those with a more innovation-driven mindset might focus on its economic potential. By broadening the scope of research to include a more diverse sample, future studies can offer deeper insights into the global implications of GenAI, ensuring that findings are not only reflective of the communication profession but also applicable to a broader spectrum of perceptions and practices.

Second, while this study focused on GenAI technologies, emerging tools and applications may present new challenges and opportunities. Longitudinal research (e.g. Bowen, 2024) can examine how communication professionals' perceptions and READINESS evolve as GenAI technologies continue to advance. In addition, although this study initially focused on aspects of the digital and physical work environments when examining organizational READINESS, the findings highlight that organizational culture–including values, leadership practices, and change management processes–also plays a significant role. Since these aspects emerged inductively from the data, future research should explicitly examine the influence of organizational culture in shaping READINESS for GenAI adoption. Building on these insights and addressing these limitations can help advance communication theories and support the development of practical frameworks for a safer and more equitable integration of GenAI in professional settings.

• Q1.

  What applications of generative AI do you observe being utilized in today's workplaces?

• Q2.

  What forms of risk do you anticipate in relation to generative AI adoption within workplace environments? Please list at least 3 risks that come to your mind, and provide a short explanation for each risk.

• Q3.

  What is your understanding of individual READINESS in the face of these risks? (i.e. think of your own resources and abilities)

• Q4.

  What factors are important for developing your individual READINESS in the face of these risks? Please list at least 3 factors, and provide a short explanation for each factor.

• Q5.

  What is your understanding of organizational READINESS in the face of these risks?

• Q6.

  What factors are important for developing an organization's READINESS in the face of these risks? Please list at least 3 factors, and provide a short explanation for each factor.

• Q7.

  What aspects of physical working environments are essential for building an organization's READINESS?

• Q8.

  What aspects of digital working environments are essential for building an organization's READINESS?

Source(s): Created by authors.