You’re listening to “Infusion of generative artificial intelligence: boosting organizational agility and effectiveness,” by L.Y. Koh and K.F. Yuen. Published in 2026. ISSN: 1369-118X (Print) 1468-4462 (Online) Journal homepage: the linked source Le Yi Koh & Kum Fai Yuen To cite this article: Le Yi Koh & Kum Fai Yuen (29 Mar 2026): Infusion of generative artificial intelligence: boosting organizational agility and effectiveness, Information, Communication & Society, DOI: 10.1080/1369118X.2026.2648697 © 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 29 Mar 2026. Submit your article to this journal Article views: 1066 View related articles View Crossmark data Infusion of generative artificial intelligence: boosting organizational agility and effectiveness Le Yi Koh and Kum Fai Yuen School of Civil and Environmental Engineering, Nanyang Technological University, Singapore ABSTRACT. Organizational agility is vital when responding to turbulent market environments, and effectiveness to ensure long-term competitiveness in the market. Generative Artificial Intelligence tools have been increasingly incorporated and infused into organizational processes due to their promising functions. This study has applied the Technology-Organization-Environment framework to examine various perspectives influencing the infusion of generative artificial intelligence tools and subsequently linked them to organizational agility and effectiveness. Data was collected through surveys, and 615 valid responses were collected. Structural equation modeling was applied to examine the data, and all eleven hypotheses are supported. Additionally, the total effect analysis revealed the top predictors of organizational effectiveness to be the infusion of generative artificial intelligence tools, market agility, and organizational learning. These findings stress that organizations must foster positive work culture and environments that embrace the integration of generative artificial intelligence into work processes while encouraging employees to work synergistically, such that the benefits from generative artificial intelligence can be translated into tangible outcomes that satisfy stakeholders and enhance the organization’s long-term competitive position. 1. Introduction. ARTICLE HISTORY Generative artificial intelligence; infusion; organizational agility; organizational effectiveness; technology-organization-environment framework Technologies have long been present and prevalent in forward-looking organizations. Among the technologies, Generative Artificial Intelligence (GenAI) is at the forefront. With proper and informed usage, GenAI is capable of creating revolutionary effects on workplace dynamics due to its ability in content production, data processing, and handle customer service, hence supporting various functional aspects such as operations, project management, and customer relationship management. Despite the potential benefits, the infusion of GenAI faces hurdles such as employees’ lack of preparedness and engagement to use GenAI tools effectively, heightened job insecurity of being replaced by GenAI, and insufficient business strategies to support the effective infusion of GenAI tools. Consequently, the successful infusion of GenAI is contingent on more than just the GenAI tools themselves; it requires widespread participation and alignment within the company. Current research predominantly focuses on behavioral intention to adopt GenAI, employing frameworks such as the Unified Theory of Acceptance and Use of Technology, the Technology Acceptance Model, Diffusion of Innovation Theory, and the Self-Determination Theory. While these studies provide useful insights regarding adoption intentions in workplaces, there is already widespread adoption of GenAI tools in the workplace, and these studies do not look further into whether GenAI is seamlessly integrated into the organizations’ operations and fully leveraged for better organizational outcomes. Thus, there should be more focus on the infusion of GenAI into organizations to realize their potential. This study will address this gap by identifying the constructs that influence the successful infusion of GenAI in organizations. Secondly, the existing studies focus on user perspectives and neglect other important considerations that could affect adoption intentions, such as organizational and environmental perspectives. A successful infusion of GenAI requires synergy between employees and management. This stresses the importance of organizations supporting their employees and ensuring that the organization’s culture embraces change. This paper will apply the Technology-Organization-Environment (TOE) framework, which addresses technological, organizational, and environmental factors that influence the infusion of GenAI into organizations. The technological factor will be the employees’ perspectives of how GenAI aids them in the workplace, while the organizational factor focuses on how organizations plan to upskill employees and cultivate work environments embracing GenAI and its changes. Lastly, the environment factor will address the competitive pressure of how an organization’s competitors can influence their intentions to use and infuse GenAI tools into their organizations. Thirdly, existing research falls short of addressing the next steps of GenAI integration, specifically how the infusion of GenAI translates into tangible organizational benefits, especially given that in the age of disruptions and fast-paced environments, prompt decision-making may determine the success of an organization. This study posits that organizational effectiveness in today’s changing business climate is dependent on an organization’s capacity to remain agile across market, workforce, and operational agility. GenAI improves market agility by offering timely insights from massive datasets, allowing businesses to anticipate market changes and capitalize on new possibilities. GenAI promotes workforce agility by automating mundane tasks and enhancing employee decision-making, allowing employees to respond rapidly to changing demands. Finally, GenAI improves operational agility by streamlining processes, allocating resources more efficiently, and allowing for the speedy implementation of crucial changes. A market-agile organization can recognize and seize new opportunities, but if its workforce is unable to adapt quickly, the organization may miss out on these opportunities. Similarly, even with a responsive workforce, ineffective operational processes can impede the adoption of critical changes. As a result, a comprehensive approach that promotes alignment and integration across markets, workforces, and operational agility is required. This paper aims to examine how GenAI infusion can influence organizational effectiveness through these three forms of agility. Specifically, the study uses the TOE framework to analyze how technological, organizational, and environmental factors influence GenAI infusion, hence filling a fundamental gap in understanding the methods by which GenAI contributes to organizational success, with both theoretical and practical implications. 2. Literature review. 2.1. Technology-Organization-Environment (TOE) framework. The TOE framework is especially well-suited for investigating complex organizational technologies since it considers not only technological aspects, but also internal organizational capabilities and external environmental influences that influence technology adoption and use. Existing studies have applied the TOE framework to examine the adoption of numerous technologies such as cloud computing and other information and communication technologies, information security solutions in organizations, and augmented reality for e-commerce. As GenAI is also a technology that is employed in organizations and requires significant organizational change and learning, the TOE framework is suitable for examining the infusion of GenAI tools and their eventual influences on organizational agility and effectiveness. The technology factor considers technological attributes that affect adoption decisions, such as technological competence, perceived benefits, relative advantage, and compatibility. The proposed framework builds on these constructs by specifically addressing the value added by generative AI and the believability of AI-generated outputs. Value-added provides a more precise and comprehensive assessment of the distinct benefits and perceived value that GenAI provides to employees and relates more closely to better employee performances. This better expands on the constructs of perceived benefits and relative advantage in the context of GenAI implementation in organizations. Furthermore, believability refers to the trustworthiness of AI-generated results, which combines the concepts of compatibility and technological competency. It underscores the credibility and accuracy of the content generated by GenAI and is a major criterion when making decisions. The organizational factor considers organizational characteristics that influence technology adoption, such as technological knowledge, human resources, and top management support. The proposed framework in this study refines this by emphasizing two critical organizational factors in the context of GenAI: employee management and organizational learning. Employee management considers both technological knowledge and human resource considerations. It covers performance schemes that motivate employees while checking on their mental health. By creating a supportive environment, these strategies ensure that employees have the essential skills and well-being to accept the infusion of GenAI in the organizations. Meanwhile, organizational learning upskills employees and creates alignment throughout the company, including both employees and management. This comprehensive approach ensures that the entire organization is ready for the infusion of GenAI, successfully addressing the responsibilities of technology knowledge and top management support. Through continual learning and improvement, organizations can construct proactive plans for GenAI infusion. The environmental factor comprises external aspects such as market conditions that influence technology adoption. However, this study proposes competitive pressure as a more thorough and relevant component in GenAI adoption because organizations must use GenAI solutions to be competitive and avoid obsolescence. This emphasis on competitive pressure delivers context-specific insights that are essential in today’s continuously changing business scene. Overall, this study bridges this gap by connecting the infusion of GenAI to market agility, workforce agility, and operational agility and, ultimately, organizational effectiveness. This integration provides a more comprehensive understanding of how GenAI infusion leads to concrete organizational achievements in dynamic and highly competitive business landscapes, where agility is crucial for success (Figure 1). 2.2. Hypotheses development. 2.2.1. The positive effect of value-added on the infusion of GenAI. Value-added refers to the tangible and intangible benefits that GenAI provides to employees and the organization, while infusion refers to technology integration to its fullest potential in an organization’s operations, to facilitate a higher level of performance rather than mere adoption into an organization’s systems. GenAI has various value-adding mechanisms, such as the ability to generate text and code, analyze data, manage inbound and outbound sales, projects, and operations, and handle customer service. Consequently, GenAI tools value-add organizations in terms of improving employees’ task efficiency, performance, productivity, and overall work effectiveness, leading to the increased perceived value of GenAI. Organizations that recognize the value of GenAI are more likely to actively seek out and investigate new and novel applications of GenAI and encourage widespread acceptance and use of GenAI tools for a variety of work functions such as marketing and product and service development, thereby embedding them into routine job tasks and cementing these tools as essential components of the organizational operations: H1: Value added by GenAI tools positively influences the infusion of GenAI in organizations 2.2.2. The positive effect of believability on the infusion of GenAI. Believability refers to the trustworthiness of GenAI-generated content. Higher believability of GenAI-generated content indicates higher trustworthiness and credibility and translates to better quality and effectiveness of decisions made. To sustain market share in a dynamic business environment, organizations must have faster access to resources and make real-time judgments that are efficient, effective, and accurate (wael Al-khatib, 2023). Therefore, when the GenAI-generated content is perceived as credible, reliable, accurate, and true, it sparks user trust and employees may feel more inclined to embrace and incorporate the GenAI tools for decision-making, problem-solving, and creative tasks, therefore integrating GenAI into their job tasks and business functions, making the GenAI tools intrinsic parts of organizations’ strategic initiatives, and fostering strategic infusion: H2: The believability of GenAI tools positively influences the infusion of GenAI in organizations 2.2.3. The positive effect of employee management on the infusion of GenAI. Employee management refers to the measures initiated by organizations to train, motivate, and encourage the use of GenAI tools among employees in the workplace. The measures can include implementing employee recognition and performance measurement schemes that specifically highlight GenAI-related achievements and regular training and development programs focused on GenAI skills. Thus, there is positive reinforcement of improving their ability to exploit GenAI tools and achieve superior work performances. Moreover, an efficient performance review system will provide employees with clear job objectives as well as chances for feedback and conversation. Thus, organizations should continuously monitor employee well-being related to GenAI workloads and provide feedback channels for employees to share their experiences and suggestions on GenAI processes. Overall, this cultivates a work environment that is supportive of innovation, skill development, and open communication, hence facilitating GenAI infusion. H3: Employee management positively influences the infusion of GenAI in organizations 2.2.4. The positive effect of organizational learning on the infusion of GenAI. Organizations may face various obstacles to the infusion of GenAI tools, such as employees’ lack of GenAI literacy, communicating how GenAI tools match with organizational goals, and dealing with employees who are resistant to change. It can be solved through organizational learning, which supports generating new knowledge and insights from employees’ work experiences; this knowledge can be used to develop GenAI upskilling programs to disseminate GenAI knowledge within the organization and encourage employees to learn from each other. Furthermore, when employees are encouraged to discuss and share new ideas and tactics, and the organizations are open to new ideas and tolerant of occasional blunders, it fosters a learning environment that supports the experimentation of GenAI, allowing employees to be more open and innovative in testing novel applications without fear of failure. This collaborative culture catalyzes the discovery and implementation of GenAI tools in novel and imaginative ways, and their integration into daily operations, making them an essential component of the organizational workflow. H4: Organizational learning positively influences the infusion of GenAI in organizations. 2.2.5. The positive effect of competitive pressure on the infusion of GenAI. Competitive pressure reflects the pressure imposed by external forces such as competitors from the same industry that push an organization to adopt novel technologies such as GenAI tools to remain competitive and relevant in the industry. Typically, organizations under intense competitive pressure are more inclined to adopt and innovate with new technologies to prevent obsolescence and increase market responsiveness, highlighting the importance of competitive pressure in promoting technological innovation and adoption, notably in the case of GenAI, which provides significant benefits in crucial aspects such as data processing, decision-making, and creative jobs. As market competition grows, organizations must see the strategic importance of implementing GenAI tools or risk losing market share and industry competitiveness. Hence, organizations feel compelled to respond to pressures from competitors who have already adopted GenAI by investing in new solutions and innovations to gain a competitive advantage and ensure competitive parity. As organizations internalize this pressure, they begin to incorporate GenAI into their operations, deploying it in novel ways to support job duties, use a greater range of functions, and constantly explore new applications. Consequently, the competitive pressure organizations face results in the promotion and infusion of GenAI tools to stay relevant in the market. H5: Competitive pressure positively influences the infusion of GenAI in organizations 2.2.6. The positive effect of infusion of GenAI on market agility. Market agility refers to an organization’s capacity to react to changing market conditions. GenAI tools such as ChatGPT can track client demands and concerns and analyze client data, which aids employees in determining whether clients enjoy certain types of products or prefer specific product characteristics. This allows employees to obtain better knowledge of changing client preferences and utilize GenAI as a marketing tool for focused marketing efforts that give quicker turnaround times and more personalized and innovative client experiences. Thereafter, the organization can quickly redesign and realign its products and services to the clients’ expectations in real-time with greater accuracy and reliability, ensuring that current clients are satisfied while also giving the organization more flexibility in responding to competitor actions and turbulent market conditions. Hence, the infusion of GenAI tools cultivates an organizational mindset that is dynamic, change-embracing, and growth-oriented with regard to strategic direction and decision-making under uncertain circumstances. This enables proactive planning instead of reactive measures and facilitates the organization’s agility in responding to market turbulence. H6: Infusion of GenAI in organizations positively influences market agility 2.2.7. The positive effect of infusion of GenAI on workforce agility. An agile workforce is defined by employees’ prowess in problem-solving, acceptance of change and new technology, innovative thinking, willingness to take on responsibility, learning, and drive to grow personally. The capacity of GenAI to produce text, photos, and videos that are easily customizable provides employees with a wealth of effective tools for streamlining job tasks and operational procedures. By embracing these GenAI tools and actively engaging them in their job tasks, employees will be able to save a lot of time and effort on their first drafts; hence, GenAI helps employees to complete their tasks more quickly and leaves more time for them to complete other value-adding activities. Hence, the infusion of GenAI tools results in a more productive workplace, increasing workforce agility. Moreover, an agile workforce will have the capacity to leverage GenAI to quickly modify workflows and processes in response to market disruptions or competitor actions, which can provide a major competitive advantage. Thus, the infusion of GenAI tools within the organization enables employees to smoothly leverage them to increase their flexibility and adaptability, showing enhanced agility and responsiveness of the workforce. H7: Infusion of GenAI in organizations positively influences workforce agility 2.2.8. The positive effect of infusion of GenAI on operational agility. Operational agility refers to the ability of an organization’s business operations to achieve speed, precision, and cost efficiency, and is crucial for supporting innovation and developing competitive action to exploit market opportunities. For example, the use of ChatGPT possesses significant operational advantages, as the GenAI tool enhances decision-making procedures by automating tedious operations such as inventory management, purchase orders, invoices, and delivery. Thus, the infusion of GenAI tools enables the automation of routine procedures, which streamlines operations and decreases the time and effort necessary to execute them, thereby speeding up the operational timeline of product and service delivery. Additionally, the increased adoption and exploration of GenAI tools in organizations lead to improved operational reliability and efficiency since AI-driven insights and automation enable organizations to allocate resources optimally and make well-informed decisions through adaptive decision-making based on ongoing learning from operational data. Organizations can quickly adapt their operations to minimize operational bottlenecks and redundancies, streamline internal procedures, increase operational cost efficiency, and react quickly to both internal and external developments. H8: Infusion of GenAI in organizations positively influences operational agility 2.2.9. The positive effect of market agility on organizational effectiveness. Market agility refers to an organization’s ability to respond to fast-changing market conditions. GenAI tools allow for greater flexibility in service customization, allowing organizations to better redesign their products and services to match the unique needs of individual clients in real-time; for instance, in the tourism industry, GenAI tools can be used to develop unique and tailored travel plans for each traveler. With market agility, organizations can rapidly and efficiently revamp their products and services to comply with evolving market demands. This increased agility and responsiveness to market changes enable organizations to anticipate market opportunities, quickly adjust goals and objectives, anticipate client needs, respond effectively to new market demands, and capitalize on new opportunities to maintain a competitive advantage in their respective industries, thereby enhancing their competitive edge and organizational effectiveness. H9: Market agility positively influences organizational effectiveness 2.2.10. The positive effect of workforce agility on organizational effectiveness. Workforce agility is centered around the proactivity, adaptability, and continuous development of employees, and requires them to embrace changes in turbulent environments. Employees who actively seek opportunities to acquire knowledge and experiment with new generative AI tools have a more nuanced grasp of its capabilities and possible applications, as well as more proficiency in using these technologies to drive innovation. This proactive approach ensures that employees who are skilled at using generative AI are better able to innovate and effectively switch between different generative AI tools to quickly modify workflows and processes in response to changing business needs, such as market disruptions or competitor actions. This forward-thinking approach enables the organization to anticipate market transitions, thereby preserving a competitive advantage and enhancing organizational effectiveness. Furthermore, when presented with problems, a workforce that embraces generative AI tools with tenacity and optimism is better able to manage them. Such employees are capable of not only learning new technology but also improving overall job performance and morale. This enthusiastic and adaptable workforce enables the organization to quickly adjust its plans and objectives in response to industry or market changes. H10: Workforce agility positively influences organizational effectiveness 2.2.11. The positive effect of operational agility on organizational effectiveness. Operational agility improves an organization’s ability to adapt with speed and precision, allowing it to better predict and act on changing market dynamics and emerging possibilities. GenAI tools provide greater operational agility by optimizing system performance and improving quality control; thus, the organizations that infuse GenAI tools into their operational processes enjoy improved operational reliability and efficiency and are better positioned to respond quickly to market shifts and client demands. This boosts organizational dynamism and flexibility, and the dynamic adjustment capacity allows organizations to effortlessly switch between multiple generative AI tools, ensuring that internal operations stay efficient and optimal. Overall, the infusion of GenAI tools into key operational processes leads to faster delivery of high-quality products and services, improved decision-making, and competitive advantage, leading to higher organizational effectiveness. H11: Operational agility positively influences organizational effectiveness 3. Methodology. 3.1. Measurement items. The measurement items are presented in Table 1. 3.2. Survey administration process. The survey started by informing respondents about the increasing infusion of GenAI tools and their contributions towards job tasks. The survey also listed some examples, including ChatGPT, to help respondents better relate the use of GenAI tools to their organizations. There was also a screening question, and respondents whose organizations do not use any form of GenAI were screened out because they would not be able to give accurate responses regarding how GenAI tools aid in organizational agility and effectiveness. Demographic questions were asked to better understand the respondents’ backgrounds and ensure that they come from diverse backgrounds. Subsequently, respondents then rated the measurement items based on a 7-point Likert scale, ranging from ‘1’ strongly disagree to ‘7’ strongly agree, which enables more sensitivity and flexibility in recording respondents’ opinions and attitudes. Data quality was ensured by screening for speeding, straight-lining, and responses that failed any of the two attention checker questions were removed before the analysis. The survey was administered by Qualtrics, which has a 115,000-member Singapore panel. Stratified random sampling was employed using the Qualtrics Singapore panel, with strata defined based on key population demographics. This method improves representativeness by ensuring proportional inclusion across demographic categories. The surveys are distributed based on convenience sampling, with panelists recruited via six channels, including e-mail and social media. A pilot test was conducted. Confirmatory factor analysis and Cronbach’s alpha test were conducted to identify poorer-performing measurement items, and they were rephrased. After modifying, Qualtrics administered the main survey and collected 615 complete responses. To assess potential common method bias, Harman’s single-factor test was conducted, and it accounted for less than 50% of the total variance, suggesting that common method bias is not a major concern. 3.3. Demographic profile. The profile of the respondents is detailed in Table 2. There is a balanced gender representation, with 54.5% males and 45.5% females. The age distribution of the respondents is also broad, with substantial groups falling between 25 and 34 years old (27.8%) and 35 and 44 years old (29.9%). The respondents hold different job positions, predominantly in middle management (26.3%) and professional or technical roles (24.7%). Furthermore, 22.6% of respondents possess over 20 years of experience, and 53.3% have between 6 and 20 years of working experience. This demonstrates that the respondents possess substantial professional experience and come from diverse organizational roles. Moreover, only 1.1% of respondents have never used generative AI tools, indicating that the sample is well-qualified to offer significant insights on the use of GenAI tools in their organizations. Organizational-wise, a variety of job scopes are represented, with the highest representation for Information Technology/Software Development (18.5%), Finance/Accounting (12.0%), and Operations/Logistics (10.9%). This diversity enhances the survey’s overall comprehensiveness. The respondents are also employed in organizations of varying ages, with 5.0% in newly established organizations, 28.1% in younger organizations, 30.2% in middle-aged organizations, and 36.6% in older organizations. These differences ensure that the survey captures the implications of generative AI across the organization’s lifecycle. GenAI usage in these organizations is relatively new, with 51.2% adopting during the previous year and 48.8% adopting for more than a year. This blend of adoption timeframes provides a balanced perspective on both early and more mature GenAI use cases. Overall, the respondents’ organizational characters cover diverse aspects, making them well-suited to deliver comprehensive insights. 4. Results and discussion. 4.1. Measurement model analysis. The measurement model fit indices are χ2 /df = 2.518 (p <0.05); comparative fit index (CFI) = 0.932; Tucker – Lewis index (TLI) = 0.927; root mean square error of approximation (RMSEA) = 0.050; standardized root mean square residual (SRMR) = 0.0425. CFI and TLI values are above 0.90, and the RMSEA and SRMR values are also below 0.08 and 0.10, respectively, indicating that the measurement model has a good model fit. Next, the validity and reliability of the measurement items are examined. The values of composite reliability, standardized factor loadings, and average variance extracted should be above 0.70, 0.70, and 0.50, respectively. These thresholds are satisfied as detailed in Table 3. Additionally, discriminant validity should be considered. The average variance extracted from each construct should be greater than its respective squared correlations with other constructs. The analysis is detailed in Table 4, the average variance extracted is bolded, and the squared correlations are italicized. It can be observed that the requirements are met, hence indicating discriminant validity. 4.2. Structural model analysis. The parameter estimates of the theoretical model are detailed in Figure 2. The structural model fit indices are χ2 /df = 2.451 (p <0.05); CFI = 0.930; TLI = 0.924; RMSEA = 0.049; SRMR = 0.049, which are satisfactory as they are within the thresholds. Additionally, the explanatory powers (R2) of the dependent variables are 0.861 for infusion of GenAI, 0.851 for market agility, 0.793 for workforce agility, 0.889 for operational agility, and 0.745 for organizational effectiveness. The explanatory powers are much higher than 0.50, which indicates that the dependent variables have satisfactory explanatory powers. Both control variables (organization age and GenAI use period in organizations) have non-significant impacts on organizational effectiveness, indicating that an organization’s age or the period that they have used GenAI does not have a significant influence on how well an organization performs. Generally, older organizations have more resources, more established procedures, and accumulated knowledge and experience in the industry, while younger organizations may have more strategic agility and creative thinking. However, in the case of GenAI tools, the organization’s age does not have a significant influence on organizational effectiveness indicates that rather than an organization’s age, effectiveness is frequently more closely correlated with the organization’s capacity to continuously improve and adjust to changing conditions in turbulent environments. Hence, a culture of constant adaptation and progress is essential; age is less important than these cultural characteristics. As for the period of GenAI usage not having a significant influence on organizational effectiveness, this indicates that how well the GenAI is incorporated and applied within the organization may be more important than the duration of GenAI use. GenAI is a quickly developing field that allows for new capabilities that result in increased productivity and the creation of new products and services, thus the advantages of using GenAI depend less on how long an organization has been using it and more on how current it is with best practices and the most recent breakthroughs. Longer usage of GenAI alone does not guarantee better performance if the GenAI tool is not successfully integrated into the organizational procedures and culture and used to its fullest potential. As a result, the length of use and effectiveness are not directly correlated. Among the main hypotheses, H1 to H5 are accepted because the independent variables have significant effects on the infusion of GenAI in organizations, as depicted in Figure 2. Specifically, the acceptance of H1 emphasizes the importance of presenting the advantages of GenAI tools clearly and tangibly to promote their widespread infusion in the organization. Organizations are more inclined to completely integrate GenAI tools into their operations when they recognize how much these tools may contribute to process enhancement, efficiency improvements, or new opportunities. Next, the acceptance of H2 suggests that the dependability and credibility of GenAI tools are important considerations. Organizations are more likely to infuse the GenAI tools when they are confident in the precision and reliability of GenAI outputs, emphasizing the importance of establishing and preserving the organizations’ trust and faith in GenAI tools. The acceptance of H3 suggests that the successful infusion of GenAI tools depends on efficient employee management; organizations that proactively resolve issues and offer guidance to employees foster a positive GenAI environment that encourages employees to embrace GenAI tools and embrace their use in organizational process, resulting in a greater degree of infusion of GenAI tools into routine work tasks. The acceptance of H4 indicates that organizational learning plays a major role in facilitating the infusion of GenAI, stressing the importance of cultivating a strong learning culture that values knowledge exchange, teamwork, and continuous development, whereby employees work together in synergy. The acceptance of H5 indicates that organizations facing pressure from their competition are more likely to implement GenAI tools more thoroughly into their processes to retain or improve their market position in fiercely competitive markets; hence, it is important to consider the depth and strength of competition in the market as well. Overall, the results confirm that to fully reap the benefits of GenAI, organizations must ensure thorough and successful integration of these GenAI tools by maintaining a responsive and supportive internal environment while also being aware of the dynamics of the external market. The infusion of GenAI tools within organizations has a profound impact on various dimensions of organizational agility. To illustrate, the significant standardized estimates are 0.922 for market agility, 0.890 for workforce agility, and 0.943 for operational agility (resulting in the validation of H6, H7, and H8). These results highlight how GenAI tools may drive agility across various organizational dimensions in a transformational way. Hence, organizations that effectively infuse GenAI tools into their operations will be better equipped to respond in volatile markets. The results also indicate a significant association between market agility, workforce agility, operational agility, and organizational effectiveness, leading to the acceptance of H9, H10, and H11. This result emphasizes how important agility is to the overall effectiveness and success of an organization. With market agility and organizational effectiveness being significantly correlated, this indicates that organizations with the ability to quickly adjust to changing market conditions are more likely to establish and maintain competitive advantages. By better matching their products and/or services to changing market demands, these organizations can improve client satisfaction and strengthen their competitive position, which will eventually increase their organizational effectiveness. Next, the significance of workforce agility on organizational effectiveness stresses the importance of a responsive and adaptable workforce in carrying out its strategies. Agile employees are more equipped to adopt new procedures, GenAI tools, and approaches, which help the organization innovate, increase productivity, and remain resilient in the face of difficulties. Therefore, organizations with highly flexible workforces are better able to accomplish organizational objectives, which improves output, boosts employee happiness, and contributes to the success of the organization. Lastly, operational agility has a significant effect on organizational effectiveness, indicating that organizations with operational agility are better equipped to optimize their workflows, minimize inefficiencies, and react promptly to changes in demand, supply chain interruptions, or other operational difficulties. This flexibility helps to increase cost-effectiveness, operational efficiency, and the capacity to continuously provide value to clients, all of which boost organizational effectiveness. Collectively, the findings imply that market, workforce, and operational agility are important factors that influence an organization’s effectiveness. Organizations that invest in improving these facets of agility will be in a better position to handle the challenges presented by the fast-paced business climate of today. 4.3. Total effect analysis. The analysis of the total effects is presented in Table 5. Infusion of GenAI has the highest effect on organizational effectiveness, followed by market agility, organizational learning, workforce agility, value-add, operational agility, believability, competitive pressure, and employee management. This is most likely because firmly infusing GenAI into operations improves responsiveness, flexibility, and efficiency, resulting in increased worker agility, operational performance, and organizational effectiveness. In contrast, believability and competitive pressure have weaker effects, indicating that simply trusting technology or responding to external competition is insufficient without genuine integration into essential operations. The findings show that organizational effectiveness is more dependent on how GenAI tools are integrated into processes, facilitate value creation, and enable continuous learning and innovation than on external or perceptual characteristics. 5. Conclusion. 5.1. Summary. This study focuses on the use of GenAI tools in organizations and adds value to existing research by specifically linking the infusion of GenAI to organizational agility, i.e., market agility, workforce agility, and operational agility. Thus, this study has applied the TOE framework to examine the various factors influencing the infusion of GenAI in organizations and linking it to organizational agility, and subsequently to organizational effectiveness. Surveys were conducted, and 615 valid responses were collected. SEM was also conducted, and all hypotheses were confirmed to be significant and accepted. Additionally, the total effect analysis revealed the infusion of GenAI as the biggest determinant of organizational effectiveness, followed by market agility, organizational learning, workforce agility, value-add, operational agility, believability, competitive pressure, and employee management. 5.2. Implications. One important theoretical contribution is the development of reliable measurement items tailored to the use of GenAI in organizational contexts and adding to existing constructs, as presented in Table 1. These new measurement items have satisfactory factor loadings and pass the validity and reliability tests conducted, indicating that they are valid and reliable representations of the constructs and capable of effectively capturing the nuances of GenAI in organizational settings. Future studies can make use of these enriched measurement items by including them when investigating the same constructs of GenAI infusion and impact in greater detail. Additionally, this also standardizes the measurement items used for GenAI in organizational contexts, which allows for consistency and comparability of findings amongst future studies. Another theoretical implication is the updating of TOE constructs to be tailored and contextualized towards the use of GenAI in organizations and the expansion of the TOE framework to connect to organizational agility and effectiveness contexts for a better understanding of how GenAI tools support organizations in turbulent business environments. Firstly, this study updates the constructs traditionally used in the TOE framework, such as relative advantage, compatibility, and top management support, by introducing new variables, i.e., believability, value-added, competitive pressure, employee relations, and organizational learning, that are more pertinent to comprehending the distinct opportunities and problems posed by GenAI tools. They provide a clearer, more accurate picture of the constructs that affect the effectiveness of GenAI tools, and greatly enhance the corpus of existing GenAI research by offering new perspectives. Thereafter, this study broadened the theoretical framework by linking GenAI infusion to organizational agility and effectiveness. It enhances academic knowledge about how GenAI infusion can enhance agility in dynamic and demanding contexts while facilitating sustained growth and success. Overall, the proposed theoretical framework is holistic and comprehensive in explaining the factors that influence GenAI infusion, and enhances understanding of organizational agility and performance, providing a foundation for researchers interested in examining the outcomes that can be garnered from GenAI infusion. Additionally, the findings have practical implications for managers. Consistent with the findings in Section 4, the non-significant effects of organizational age and GenAI use period suggest that managers should prioritize strategic integration of GenAI tools, organizational learning, and value creation over traditional metrics such as organizational longevity or tool tenure. Effective GenAI infusion improves market, workforce, and operational agility, hence increasing organizational effectiveness, as indicated by the substantial total effects reported in Table 5. Managers should establish specific goals for GenAI deployment, convey GenAI’s future role in molding work scopes, address employee concerns about job security and skill shortages, and offer focused training programs. This encourages employee engagement and facilitates the integration of GenAI products into key processes. Secondly, based on findings from the total effect analysis, it is recommended that managers prioritize aspects that have larger impacts on organizational agility and effectiveness. In this case, the aspect refers to the infusion of GenAI tools in the organization, which is driven by organizational learning and value-add, and supported by competitive pressure, believability, and employee management. Managers must encourage a learning-oriented culture that values organizational learning and creativity. This will require them to be open to ideas and encourage employees to work together synergistically instead of against one another. On top of that, managers should examine the ramifications for external stakeholders. Customers benefit from effective GenAI integration, which improves service response, product quality, and overall satisfaction. Regulators and platform providers may profit from transparent, ethical GenAI deployment, which ensures compliance and fosters confidence. Furthermore, broader societal ramifications, such as labor reskilling and ethical AI use, must be addressed to maintain social legitimacy while seeking a competitive advantage. By improving these external factors, GenAI infusion promotes not just internal performance but also stakeholder value and responsible innovation. 5.3. Limitations. The GenAI scope in this study is broad. It covers a variety of industries and GenAI tools to get a general understanding of the effects of GenAI due to their increasing prevalence in organizations. However, by doing so, this study may have missed some details unique to a certain industry. To acquire a more thorough grasp of their effects, future studies can narrow their focus to particular industries, job scopes, or GenAI tools. This will make it possible to analyze the infusion of GenAI tools into specific industries or job types with greater precision, offering deeper insights into the mechanisms by which they affect organizational agility and effectiveness. Another limitation is that the distinctions between organizations that pay for GenAI tools and those that utilize free versions are not taken into consideration. These variations may have impacts on how GenAI is seen to benefit organizations and how it affects organizational reactions to GenAI evolve over time. Future studies should compare free and paid tool usage and use longitudinal methods to capture temporal dynamics, such as how employee attitudes, workflow integration, and organizational outcomes change as experience with GenAI grows. CRediT: Le Yi Koh: Conceptualization, Visualization, Writing – original draft, Writing – review & editing; Kum Fai Yuen: Supervision, Writing – original draft, Writing – review & editing. Disclosure statement No potential conflict of interest was reported by the authors. Notes on contributors Le Yi Koh is a Ph. D. graduate from the School of Civil and Environmental Engineering, Nanyang Technological University, Singapore. Her research interests include the commercial usage of last-mile delivery innovations, as well as the infusion of technology in organizations. Kum Fai Yuen is currently an Associate Professor and Assistant Chair (Students) at the School of Civil and Environmental Engineering, Nanyang Technological University, Singapore. Notably, Dr. Yuen is recognised among the world’s top 2% most influential scientists in both career-long and single-year categories.