ARTIFICIAL INTELLIGENCE IN ORGANIZATION DEVELOPMENT: A SCOPING REVIEW

OD

The field of OD has evolved significantly over the past two decades, driven by changing organizational needs and technological integration. Initially rooted in behavioral science, OD focused on planned, systematic interventions to improve organizational effectiveness and foster a healthy environment (Burke, 2004; French & Bell, 2005). Foundational theories such as Lewin’s (1951) change model and the action research model (Argyris, 1970) emphasized data-driven, planned change.

Since the early 2000s, OD has expanded to incorporate strategic management, aligning organizational systems and structures with broader organizational goals (McLean, 2006). Systems theory perspectives also gained prominence, viewing organizations as complex, adaptive systems (Anderson, 2008). By the mid-2010s, OD integrated digital transformation, including digital communication, data analytics, and process automation, to remain relevant in a digitized world (Worley & Mohrman, 2014). Dialogic OD emerged, focusing on emergent change and participative engagement over traditional diagnostic models (Bushe & Marshak, 2015).

These advancements reflect the shift toward adaptive approaches with complexity and chaos theories emphasizing nonlinear, emergent change (Cummings & Worley, 2019). Recently, OD has further expanded to include human–machine collaboration, digital frameworks, and AI in organizational strategies (Daugherty & Wilson, 2018; Singh & Ramdeo, 2020). Now positioned at the intersection of behavioral science, technology, and strategic management, OD is a comprehensive approach supporting planned, emergent, and continuous change. Frameworks such as sociotechnical systems theory and the technology acceptance model (TAM) guide the exploration of human–technological dynamics in OD (Pasmore, 2011). Table 1 illustrates OD’s theoretical evolution and its implications for academia and industry over the past 16 years.

TABLE 1Definitions of Organization Development (OD) 2008–2024

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AI

The development of AI has advanced alongside technology, greatly influencing organizational approaches to change and development. Initially, AI was conceived as automating intelligent behavior (Luger, 2004), focusing on computational processes that simulate human cognitive functions such as problem-solving, reasoning, and learning. Early AI models were rule-based, using symbolic methods for tasks requiring logical reasoning and knowledge representation (Barr & Feigenbaum, 2005).

The limitations of symbolic AI, particularly its inability to adapt to unstructured environments, led to the rise of machine learning (ML) and neural networks in the late 2000s. These approaches introduced frameworks such as connectionism, in which intelligent behavior emerges from the interaction of simple processing units, mirroring human brain functions (Russell & Norvig, 2014). ML-based AI began to leverage large data sets and complex algorithms for pattern recognition and autonomous decision making (Kaplan & Haenlein, 2019).

In recent years, AI theory has emphasized augmented intelligence, by which AI enhances human decision making and problem solving rather than replacing it (Davenport, 2018). Integrating AI into OD requires understanding its effects on human systems and organizational change with frameworks such as sociotechnical systems theory (Pasmore, 2011) and models such as TAM and the unified theory of acceptance and use of technology aiding in understanding AI adoption in organizations.

Recent AI advancements highlight responsible AI, focusing on transparency, bias mitigation, and societal impact (Alhosani & Alhashmi, 2024), which are essential as AI increasingly influences human decision-making roles. This study adopts a theoretical framework that integrates AI’s behavioral, strategic, and technological dimensions, positioning it as transformative for OD. Table 2 illustrates AI’s evolving theoretical landscape and its implications for OD practices over the past 16 years.

TABLE 2Definitions of Artificial Intelligence (AI) 2008–2024

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Search Criteria

In consultation with a reference librarian at a tier 1 research university, we developed eligibility criteria for literature published between 2019 and 2024:

1. Peer-reviewed publications or dissertations

2. Articles published in academic journals

3. Written in English

4. Accessible in full-text format

5. Focused on AI applications in OD

6. Empirical or nonempirical studies

Search Process

The scope of the search was defined by the first three criteria (a–c), focusing on peer-reviewed journal articles published since 2019. We selected five databases relevant to OD, organizational behavior, and related fields: APA PsycInfo, Human Resources Abstracts, Business Source Ultimate, Web of Science, and Academic Search Ultimate. The search used consistent keywords across databases—artificial intelligence, organization development, and organizational development—yielding 187 results as detailed in Table 3.

TABLE 3Number of Publications on Artificial Intelligence in Organization Development in the Past Five Years (2019–2024)

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Screening Process

The initial 187 results underwent multiple screening rounds. First, we reviewed abstracts based on criteria a–d, identifying and removing 114 irrelevant studies, leaving 73 articles. In the next stage, we excluded two articles due to inaccessible full texts (criterion e), resulting in 71 articles. A final assessment of each article’s focus (criterion f) led to the exclusion of additional studies with 49 articles meeting all criteria and selected for final review (see Table 4 and Figure 1).

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TABLE 4Final Inclusion for Scoping Review

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Data Management

For the final analysis of the 49 articles, we applied Garrard’s review matrix method (Garrard, 2020). Each article was coded in an Excel spreadsheet using the following categories: author(s), publication year, title, database, journal, research purpose/questions/hypotheses, methodology, methods, sample, and major findings. This setup allowed a comprehensive overview, enabling us to identify trends across the studies. The “major findings” column included both the researchers’ reported themes and our interpretations, facilitating a thematic analysis of the 49 studies.

Research on AI in OD: Current Trends

This review covers articles from 2019 to 2024 with 2019 marking the earliest publications on AI and OD. The final sample includes 49 articles from various academic sources: 24 empirical studies and 25 nonempirical papers. Among the empirical studies, 14 are qualitative and 10 are quantitative with no mixed-methods studies represented. The nonempirical papers include 20 conceptual or theoretical articles as well as reviews and reflections. Most articles were published in 2022 and 2023 (see Table 5), reflecting a recent increase in empirical research (see Table 6). Before 2022, studies were primarily conceptual, indicating a shift toward empirical exploration as AI and OD research has matured.

TABLE 5Number of Publications on Artificial Intelligence and Organization Development Per Year

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TABLE 6Methodological Characteristics of Included Journal Articles (n = 49)

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Research on AI and OD has been published across a wide range of outlets, highlighting the interdisciplinary nature of this field (see Table 7). This body of work appears in high-impact journals such as Academy of Management Journal and Journal of Business Ethics as well as practical outlets such as AI Practitioner and Consulting Psychology Journal. The field’s relevance to HRD is evident through publications in HRD-focused journals such as Advances in Developing Human Resources and Human Resource Development Quarterly. Specialized OD journals, including Organization Development Journal, Organization Development Review, Journal of Applied Behavioral Science, and Consulting Psychology Journal, account for four key publications in this area. Business and organizational psychology journals, such as Journal of Applied Behavioral Science and Journal of Business Research, represent 10 additional outlets. Additionally, six journals focus on information technology (IT) and systems, such as Cognitive Systems Research and Communications of the Association for Information Systems. Conferences such as the AIP Conference and the International Conference on Business Excellence also serve as important dissemination platforms for AI and OD research in both academic and practitioner settings.

TABLE 7Publications Outlets of Artificial Intelligence and Organization Development Research

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AI and Leadership Development

AI’s role in enhancing leadership development (Ågerfalk et al., 2022) is a central theme in the literature with multiple studies examining AI integration in leadership coaching and decision making. Khandelwal and Upadhyay (2021), for example, explored AI-enabled coaching systems that provide leaders with real-time feedback and personalized development plans, showing how AI supports continuous improvement through data-driven insights. Similarly, Morrison (2021) found that AI tools facilitate informed decision making through predictive analytics and scenario simulations, aiding leaders in navigating complex, high-stakes environments.

Despite these benefits, AI’s limitations in leadership are noted. Bankins and Formosa (2023) caution that, whereas AI enhances data-driven decisions, it cannot replace essential human traits such as emotional intelligence, empathy, and interpersonal skills. Thus, AI should complement rather than replace human leadership skills. Whereas interest in AI-assisted leadership development grows, most studies remain theoretical or qualitative, highlighting the need for empirical, longitudinal research to understand AI’s long-term impact on organizational performance and employee engagement.

AI and Organizational Structure

AI’s influence on organizational structure is another prominent theme with studies examining how AI reshapes workflows, roles, and processes. Krzywdzinski and Butollo (2022) found that AI’s capability to analyze real-time data enables leaders to optimize resource allocation and streamline workflows, leading to more efficient and adaptable designs. They noted that AI is especially beneficial in large organizations, supporting structural redesign by identifying bottlenecks and suggesting improvements. Kanitz et al. (2023) also highlighted AI’s role in enhancing flexibility by enabling cross-functional collaboration—essential in today’s fast-paced business environment.

However, challenges with AI-driven structural changes are also discussed. Morrison (2021) cautioned that, without proper governance, AI systems may reinforce biases in organizational design, particularly in workforce management and resource allocation. Additionally, few studies have quantitatively assessed the long-term impact of AI-induced structural changes. Future research should examine how these changes affect employee satisfaction, innovation, and overall business success.

AI and Performance Management

The integration of AI into performance management systems is a significant focus with numerous studies examining how AI technologies transform the evaluation and management of employee performance. Khandelwal and Upadhyay (2021) demonstrated how AI-based performance management systems provide managers with real-time insights into employee behavior and performance, enabling more data-driven decision making. These systems improve the accuracy of performance evaluations and allow for more personalized feedback, enhancing employee engagement and productivity.

However, ethical concerns regarding the use of AI in performance management are raised. Oswick (2024) argued that AI-driven performance monitoring systems might lead to feelings of surveillance among employees, potentially undermining trust and motivation, particularly when AI is used to track behavior in real time. Furthermore, many studies highlight the need for empirical research into the long-term effects of AI-driven performance management systems on employee outcomes. Existing literature focuses largely on short-term benefits such as increased efficiency and accuracy with limited evidence on how these systems impact employee well-being, career development, and job satisfaction over time. Future studies should conduct longitudinal research exploring the sustained impact of AI-driven performance management on organizational performance, employee sentiment, and employee retention.

AI and Human Resource Practices

AI’s role in human resource (HR) practices is well-established, particularly in recruitment, training, and development. Upadhyay and Khandelwal (2018) found that AI-driven recruitment platforms significantly reduce the time and cost associated with candidate screening by automating much of the process. These systems analyze large volumes of candidate data, allowing HR managers to make faster and more informed hiring decisions. Moreover, Soleimani et al. (2022) emphasized that AI-based recruitment tools can help reduce bias in the hiring process by focusing on objective data rather than subjective human judgment, potentially promoting greater diversity and inclusion within organizations.

Beyond recruitment, AI is also playing a growing role in employee training and development. Qiu et al. (2022) explored how AI-powered adaptive learning platforms create personalized training experiences tailored to individual employees’ needs. These platforms continuously assess employee progress and adjust training programs accordingly, ensuring that skill development is aligned with role requirements. Whereas the benefits of AI in HR practices are clear, the literature points to a need for more empirical research measuring the real-world effectiveness of these systems in improving long-term employee performance and retention. Most studies in this area are conceptual or qualitative, highlighting the need for quantitative research assessing the impact of AI-driven HR practices on key organizational metrics such as employee satisfaction, turnover, and organizational diversity.

AI and Organizational Culture

The integration of AI into organizational culture is a growing area of research, particularly in terms of its impact on communication, collaboration, and decision making. Einola and Khoreva (2023) argued that AI enhances collaboration by providing real-time insights into communication patterns and team dynamics, which is especially valuable in hybrid or remote work settings. In these environments, AI helps bridge gaps between dispersed team members, ensuring seamless communication. Similarly, Felder et al. (2023) found that AI-enabled communication tools can boost team performance by identifying and addressing collaboration bottlenecks.

However, concerns exist that AI’s integration could diminish human elements critical to a healthy workplace. Bankins and Formosa (2023) cautioned that, whereas AI-driven decision making may improve efficiency, it could reduce the interpersonal connections essential for trust and creativity within teams. The literature suggests that, although AI can foster a culture of innovation and efficiency, organizations must protect the social and emotional aspects of work that contribute to engagement and well-being. Additionally, more research is needed to understand AI-driven cultural changes’ long-term effects on organizational identity and employee satisfaction. Future studies should examine ways to integrate AI into organizational culture that support technological innovation while preserving essential human values.

Gaps in Existing Literature

Several research gaps emerged across the 49 studies reviewed, highlighting areas in which further investigation is necessary to fully understand AI integration’s long-term effects on OD.

First, more empirical, longitudinal studies need to examine the long-term impact of AI-driven interventions. Whereas studies such as Morrison (2021) and Khandelwal and Upadhyay (2021) focused on short-term benefits such as enhanced decision making and leadership coaching, few have tracked how these interventions affect organizations over time. Future research should investigate the long-term effects of AI integration on leadership development, employee engagement, and organizational performance.

Second, ethical considerations still need to be explored despite frequent discussion. Several studies, including Oswick (2024) and Bankins and Formosa (2023), raised concerns about data privacy, surveillance, and algorithmic bias associated with AI-driven systems. However, there is a lack of empirical research testing the effectiveness of governance frameworks designed to mitigate these ethical risks. This paucity presents a critical gap as AI technologies continue to proliferate without sufficient guidance on responsible implementation. Future research should develop and empirically validate ethical frameworks to ensure that AI enhances organizational practices while protecting employee privacy and promoting fairness in decision making.

Third, the impact of AI on human-centric processes within organizations needs to be explored further. Whereas much of the literature emphasizes AI’s role in improving efficiency and data-driven decision making, fewer studies examine how AI affects softer aspects of organizational life, such as collaboration, creativity, and emotional intelligence. Studies such as Felder et al. (2023) and Einola and Khoreva (2023) touched on this but highlighted the potential erosion of interpersonal relationships. More research is needed on integrating AI into OD in ways that complement rather than replace human dynamics.

Finally, the predominance of qualitative methodologies limits the generalizability of findings. Most studies, including Krzywdzinski and Butollo (2022), and Kanitz et al. (2023), used qualitative case studies or conceptual approaches. Whereas these studies provide valuable insights, there is a clear need for quantitative and mixed-methods research offering broader, statistically robust conclusions about AI’s impact across various organizational settings. Future research should diversify methodological approaches to provide a fuller understanding of how AI interventions affect organizational outcomes across different industries and contexts.

AI in Leadership: Supportive but Limited

AI has shown potential in leadership development by enhancing coaching methods, providing real-time analytics, and simulating decision-making scenarios (Ågerfalk et al., 2022; Morrison, 2021). These tools can boost leaders’ technical skills and support strategic planning with data-driven insights. However, AI’s inability to replicate qualities such as empathy and emotional intelligence—critical for effective leadership—poses a constraint (Bankins & Formosa, 2023). This limitation suggests that organizations should use AI as a complementary tool in leadership rather than a substitute, emphasizing human-centered skills such as mentorship and interpersonal guidance.

Organizational Efficiency and Bias: A Double-Edged Sword

AI’s capacity to enhance efficiency by streamlining workflows, optimizing resource allocation, and improving decision making presents both opportunities and challenges (Krzywdzinski & Butollo, 2022). Whereas these efficiencies can boost productivity and responsiveness, they also risk reinforcing existing biases if AI systems lack proper oversight (Morrison, 2021). When driven by historical data, AI may inadvertently perpetuate systemic disparities in resource distribution and workforce management. Without governance frameworks and human oversight, AI could undermine organizational inclusivity and responsibility, reinforcing structural inequalities instead of fostering equitable development.

Performance Management: Ethical Implications of Surveillance and Objectivity

AI-driven performance management offers granular, data-driven insights into employee productivity but introduces ethical and organizational challenges. Whereas improved data precision can enhance performance appraisals, it also raises privacy concerns that can erode trust (Oswick, 2024). Reliance on AI may foster a culture of surveillance, potentially stifling creativity and morale. Furthermore, AI’s emphasis on quantifiable metrics can devalue essential interpersonal skills and creativity, leading to a reductionist approach to performance evaluation. To balance efficiency with OD’s developmental goals, organizations must integrate AI thoughtfully, using strategies that maintain employee privacy, build trust, and appreciate diverse contributions beyond measurable outcomes.

HR Practices and Sustained Impact

AI has transformed HR functions such as recruitment and training by expediting candidate screening and reducing biases with data-driven insights (Upadhyay & Khandelwal, 2018). However, the long-term efficacy of AI-driven HR practices remains debated. Whereas AI can deliver short-term efficiency gains, evidence of its sustained impact on employee satisfaction, retention, and diversity is limited (Qiu et al., 2022). AI’s transactional nature may overlook human interaction and mentorship, which are vital for long-term engagement and satisfaction. For a positive contribution to HR practices, AI must be integrated with human-centered approaches that emphasize continuous learning, personal growth, and a supportive work environment.

AI and Organizational Culture: Innovation Versus Connection

AI’s influence on organizational culture presents a paradox. On one hand, AI can enhance collaboration by facilitating seamless communication in remote work settings (Einola & Khoreva, 2023). On the other, an overemphasis on efficiency can undermine the human connections foundational to a strong culture. Overreliance on AI may prioritize productivity metrics over fostering trust, empathy, and creativity (Bankins & Formosa, 2023). To avoid creating a purely transactional culture, organizations should use AI as a facilitator for collaboration rather than a replacement for relational dynamics, balancing innovation with human connection to sustain a resilient culture.

Future Research Agenda

Based on current trends and identified gaps, we propose a future research agenda that includes content and methodological recommendations to guide the integration of AI into OD.

Whereas AI offers short-term benefits in areas such as decision making and performance monitoring, the long-term sustainability of these tools in OD remains underexplored. Future research should prioritize longitudinal studies to assess how AI-driven interventions impact leadership effectiveness, employee engagement, and organizational performance over time, providing insights into AI’s potential for fostering enduring growth.

AI applications in OD raise ethical concerns around privacy, surveillance, and algorithmic bias. There is a critical need for research focused on developing and validating ethical governance frameworks that promote fairness, transparency, and data privacy. These frameworks should be empirically tested across diverse organizational contexts to guide responsible AI deployment that aligns with organizational values and maintains employee trust.

The integration of AI into human-centric processes, such as collaboration, creativity, and emotional intelligence, is another underexplored area. Future studies should investigate how AI-driven tools can support rather than replace these softer organizational functions, enhancing interpersonal dynamics while preserving essential human elements for team cohesion and innovation.

To broaden the generalizability of findings, future research should diversify methodologies. Moving beyond the current focus on qualitative and conceptual studies, researchers should incorporate quantitative and mixed-methods approaches. Large-scale surveys or experimental designs could offer statistically robust insights, comparing AI’s impact across industries, organizational sizes, and cultural contexts to provide a comprehensive understanding of its role in OD.

AI’s potential to advance diversity, equity, and inclusion (DEI) in OD functions also warrants further exploration. Future studies should examine how AI systems can be tailored to reduce biases in recruitment, evaluation, and promotion processes, fostering a more inclusive workplace culture. By identifying AI-driven mechanisms that support DEI, this research can guide the development of tools that enhance both performance and inclusivity.

Given the gaps identified in this study, future research should explore contextual differences in AI’s effectiveness and how AI can support organizational leaders in strategic decision making. To motivate HRD scholars to engage in this line of inquiry, we propose the list of questions below:

• What differences exist in the use of AI technology in OD functions across industries?

• How does AI technology impact the multigenerational workplace?

• How can organizations leverage AI for strategic planning initiatives?

• In what ways can AI support change management initiatives?

Additional Hidden Costs

AI tools can optimize organizational capacity and efficiency, but their success depends on user proficiency. The rapid evolution of AI presents challenges for users trying to keep pace with new features and advancements. Effective utilization requires ongoing training and practical opportunities, which add to organizational costs in terms of time, resources, and instruction. An organization’s awareness of and willingness to invest in these costs will determine the feasibility of AI. Moreover, employee resistance to AI in performance management may decrease if employees have had direct experience with AI tools themselves (Dahl, 2018; Oswick, 2024).

Stakeholder Involvement

Successful AI implementation requires interdisciplinary collaboration among key business functions, including HR management, HR development, research and development, IT, and legal services. Cross-functional support is essential, and AI strategy development should involve input from various stakeholder groups. Including personnel from different management levels ensures that employees feel represented in AI discussions, fostering a sense of belonging and agency across teams (Bankins & Formosa, 2023).

Infrastructure Requirements

Organizations must possess adequate infrastructure to support AI systems. HR functions often rely on complex workflows to adhere to policies, and even functions such as collecting approvals for skill enhancement raises often still require multiple human verifications. Such workflows can reduce the efficiency gains from AI. Thus, organizational infrastructure must align with AI capabilities to fully realize its benefits.

Data Storage and Integrity

Whereas free AI platforms are accessible and cost-effective, they introduce data storage, integrity, and security risks, especially for organizations exploring AI without investment. Loss of proprietary information or consumer data could harm or even ruin some businesses. To leverage AI effectively, organizations must implement security measures tailored to their industry, ensuring customer data protection and intellectual property security. Without secure data handling, AI may pose more risks than benefits.

Demographic Challenges

Organizational demographics, including generational diversity, regional variation, and differences in technology proficiency, affect AI’s success in OD. Human oversight is crucial to navigate these differences. Involving stakeholders from various functional areas helps organizations account for workforce diversity and decide on AI use that aligns with their specific operational and cultural needs.