AI in innovation: impacts on management functions Open Access

The imperative to integrate artificial intelligence (AI) into organizational workflows to enhance operational efficiency is intensifying rapidly. However, the adaptation of organizations to technological innovations that fundamentally shape existing processes is often full of challenges (Carter, 2020). For example, when new disruptive technologies influence work and organizational procedures, building trust and managing conflicts are important managerial team leader skills (Turesky, Smith, & Turesky, 2020). If AI is to significantly improve outcomes of organizational processes (Lopez Flores, Belaud, Le Lann, & Negny, 2015), we should also better understand what the adoption and use of these technologies mean for these types of interactive processes in terms of management, traditional “human” tasks in organizations and organizational outputs (Mariani & Dwivedi, 2024; Philip, 2022,Haefner, Wincent, Parida, & Gassmann, 2021). One such process is innovation, which typically requires the integration of different knowledge. In this regard, AI has already shown potential (Lopez Flores et al., 2015) to support these integrative efforts. However, the current research landscape on this topic remains fragmented and underdeveloped.

Therefore, this study focuses on innovation processes and examines how artificial intelligence influences the management functions of innovation processes. Innovation processes can produce new technologies as outputs, but new technologies have also significantly influenced the development of the innovation processes themselves (Szymańska & Berbel Pineda, 2024; Füller, Tekic, & Hutter, 2024). As processes encompassing all organization levels, innovation processes require management of various organizational resources, organization of workflows and leadership. This research provides insights into how AI can transform and enhance traditional management practices to foster innovation. This study seeks to answer the following research question:

By narrowing the focus on innovation processes, we can achieve more nuanced results and proposals for further research and management practices.

As a research method, this study utilizes a systematic qualitative literature analysis covering the publication period 2019–2024 to identify relevant academic literature on AI and innovation processes and to analyze the content of the articles. We focus primarily on AI’s impact on decision-making, resource allocation, leadership and performance control by applying the Planning, Organizing, Leading and Controlling (POLC) framework (Cropanzano & Lehman, 2023) to analyze the contents of 77 articles addressing the use of AI tools in an innovation management context. The POLC framework provides a structured lens through which we can examine the human-centric dimensions of management, even in highly technology-driven contexts, as each of the elements of the POLC framework centers on human judgement, interaction and the roles of managers and teams in shaping organizational processes and outcomes. This approach helps ensure that the impacts of AI on innovation are not viewed solely as a technical shift, but as a change that is deeply influenced by human behavior, values and ethical considerations and leadership.

This study suggests for future research and practical insights for innovation process management and contributes to organization management literature by bridging technological advancements with foundational management principles. The results increase our understanding of the management functions where AI is rapidly evolving and, on the other hand, where its benefits and applications have not yet been identified.

At its core, innovation encompasses opportunity identification, idea generation and value creation, involving multiple organizational functions and interactions among diverse stakeholders. Innovation research has evolved through a variety of concepts and classifications, including incremental versus radical innovations (Henderson & Clark, 1990), market pull versus technology push (Rothwell, 1994) and closed versus open innovation models (Chesbrough, 2003). The perspectives varied from a linear to a systemic approach and by expanding the focus of innovation from products and services to social, organizational and business model innovations (e.g. Tidd & Bessant, 2018). Rather than being a singular breakthrough, innovation is typically a continuous process of planned, organized and managed activities (Tidd & Bessant, 2018). For contemporary companies, this process is inherently nonlinear and iterative, demanding ongoing learning and adaptation (e.g. Verganti, Vendraminelli, & Iansiti, 2020).

From the management perspective, innovation means coordinating different organizational functions and knowledge flows (Füller, Hutter, Wahl, Bilgram, & Tekic, 2022). This coordination involves goal setting, decision-making, resource allocation and creating feedback mechanisms that activate the interaction of exploration and exploitation (Wu, Hitt, & Lou, 2020; Pietronudo, Croidieu, & Schiavone, 2022). For better knowledge flows, some companies operate in cross-functional teams, are active in ecosystems and networks or have adopted technologies such as AI to support tasks like trend analysis, customer insight evaluation, idea screening and risk forecasting (e.g. Füller et al., 2022).

Although modern perspectives on innovation emphasize agility and interaction (Cubric & Li, 2024; Cimino, Felicetti, Corvello, Ndou, & Longo, 2024), effective execution is still based on foundational principles of management. Therefore, AI-powered innovation management also benefits from being researched from the perspective of classical management theories. History is filled with management theories, which have advanced our understanding of organization management and provided both specific and general frameworks for contemporary research. Previous studies have compared classical management theories, identifying similarities and differences stemming from various schools of thought (e.g. Pryor & Taneja, 2010; Smith & Boyns, 2005; Fells, 2000). Acknowledging that one all-encompassing framework for organizational management functions does not exist but rather that they all somewhat complement each other and provide slightly different focuses for the basis of empirical research, we base our study on the framework of the management functions derived from Henri Fayol’s original work from 1916.

Despite its age, Fayol’s framework and management functions, i.e. planning, organizing, command, coordination and control (translated from French by Storrs, 1949), still form a strong foundation for contemporary research (Fells, 2000; Wren, Bedeian, & Breeze, 2002; Pryor & Taneja, 2010). Fayol’s work has also been recognized as paving the way for and tapping far in advance into contemporary innovation management aspects (Hatchuel & Segrestin, 2019). Although Fayol’s framework for the five management functions has been criticized for being insufficient or irrelevant in today’s unstable and highly digitalized business environments (e.g. Phillips & Su, 2013), it still provides a sufficiently general organization management framework for analysis in this study.

The phenomenon under discussion, AI in innovation, is related to a source of comprehensive, transformative change within organizations and in shaping the work usually done only by humans. Thus, Fayol’s management functions categorization provides a flexible enough method theory to approach this new phenomenon from the organization and process management perspective, enabling comparability across studies and synthesization of heterogeneous findings. Fayol’s theories have also been integrated with the 5Ps of strategic leadership model, showcasing that Fayol’s work is not just about planning and managing resources but also relevant from the leadership perspective, which is of high importance also from the innovation process management point of view (Pryor & Taneja, 2010; Adams, Bessant, & Phelps, 2006). For example, 5Ps organization’s purpose-related strategic elements (e.g. vision, goals and strategies) are compatible with planning function in POLC, and people and processes are compatible with POLC’s organizing and leadership functions (Pryor & Taneja, 2010). By assessing the impact of AI on innovation using this framework, the analysis remains grounded in human agency and decision-making also from the strategic management perspective.

Following the prior work by Cropanzano & Lehman (2023), Fayol’s five management functions can be summarized into four parts: planning, organizing, leading and controlling. The key characteristics of the POLC framework based on previous literature are presented in Table 1. Planning involves looking ahead, anticipating future trends and analyzing external and internal organizational environments. This analysis helps in decision-making and defining the scope and objectives of the organization’s actions, resulting in a plan that aligns efforts with the organization’s overall goals. Ultimately, experience is what eventually determines the value of the plan for the organization. Organizing involves building and maintaining a structure that meets the organization’s needs and ensures effective functioning. This includes material organization, which pertains to physical resource allocations and human organization, which focuses on the management and coordination of personnel. Fayol also notes that the organization’s function should not only be focused on distributing duties but also on how to gain knowledge of how to adapt the organization to different requirements and how to find essential personnel and make sure that they are placed where they can be of most service to the organizational whole (Storrs, 1949). Fayol’s original categorization includes commanding and coordinating as separate functions related to the supervisors’ and their subordinates’ relationships. In contemporary understanding, this relationship is linked to leadership (Cropanzano & Lehman, 2023) and understood less hierarchically, not merely as a direct command chain link between a supervisor and individual employee but also as the relationship between teams and their leaders, creating an organizational culture that fosters effective collaboration, providing employees with clear vision and direction and putting the plan into action. Finally, controlling involves monitoring and adjusting processes to ensure they conform to the established plan in the planning phase. This function includes motivating and monitoring employee performance to maintain high standards and productivity. Controlling operates across all aspects of the organization, encompassing things, people and actions, to achieve the desired outcomes and control and evaluate the overall organizational performance.

While this categorization of four management functions may seem to reflect a relatively rigid and linearly proceeding process, Fayol also discussed the need to maintain flexibility in applying his theories, considering the changing operational circumstances and changing plans (Pryor & Taneja, 2010). This is also an important consideration when the POLC framework is applied in the context of innovation processes. Although innovation processes are often presented as proceeding linearly from the ideation phase towards the commercialization of products and services, they are often – as discussed above – rather systemic processes requiring, for example, opportunity recognition and network orchestration in addition to traditional management of organizational activities and resources. Therefore, Fayol’s work provides a comprehensive yet flexible enough theoretical framework to help understand the impacts of AI on innovation processes from the organization management perspective.

To gain a deeper understanding of the impacts of AI on management functions in the context of the innovation process, we conducted a comprehensive literature review on AI in innovation processes. We utilized a qualitative systematic literature review approach with a domain-based focus by developing and conducting a systematic process to search and collect relevant articles and using a qualitative approach to analyze their content (Kraus et al., 2022; Snyder, 2019; Palmatier, Houston, & Hulland, 2018). Following the systematic literature review process steps, we started by defining the aim and research questions for the study and continued by developing a systematic search plan and screening process (Kraus, Breier, & Dasí-Rodríguez, 2020; Okoli, 2015). The literature search was conducted using the Web of Science and Scopus databases. For both databases, we used the following search phrase, including title, abstract and keywords and limited the search only to English publications: “AI” or “artificial intelligence” and “innovation process”.

The literature search resulted in 202 articles in the Scopus database and 85 in the Web of Science (Figure 1). After identifying and removing overlapping articles, the remaining data set consisted of 227 articles. We narrowed the data further by limiting the publication period to 2019–2024 for its publication intensity and only to journal articles, book chapters and papers published in conference proceedings that were available publicly or through our university library services. We contacted the authors to receive documents we did not otherwise have access to and ended up with 150 documents.

The start of a chosen period was motivated by the notable expansion of AI research literature in 2019. Before that, only a handful of publications (5 or fewer; year 2016 with seven publications being an exception in SCOPUS and Web of Science) addressing AI and innovation processes were released annually. In 2019, there were already nine, and from 2020 onwards, more than 20 publications annually. Literature is analyzed until 2024, representing the most recent full calendar year for which the publications are available.

We continued with full-text screening to evaluate whether the articles used an organizational perspective, which was defined as an inclusion criterion (Kraus et al., 2022). Of the remaining 150 articles, 77 focused on AI in organizations. The excluded 73 publications dealt with individual innovators, teams, or societies benefiting from AI. In this final data set of 77 articles, over one-third (27 articles) were published in 2024, whereas the first two years (2019 and 2020) of the selected publication period together covered less than 20% of the data set articles (14 articles). The data set includes articles published in a broad range of publication channels. Technological Forecasting and Social Change was the leading publishing journal with six articles, followed by Technovation (three articles) and Sustainability (three articles). IEEE Transactions on Engineering Management conference also stood out from the data set with four articles, as well as the book series Studies in Computational Intelligence (Springer) with three articles.

To derive meaningful conclusions about the article contents in the organization management context, instead of providing a mere descriptive summary of the literature, we conducted an in-depth qualitative content analysis for the final data set of 77 articles (Fisch & Block, 2018). The POLC framework was applied as a basis of the analysis (Table 1). In the analysis phase of a literature review, it is essential to ensure the quality of the process and the appropriate balance between breadth and depth (Snyder, 2019; Fisch & Block, 2018). We analyzed the articles one by one, identifying if and how they addressed the impact of AI on each of the four management functions (POLC) in the context of the innovation process. All three researchers participated in the analysis phase to enable researcher triangulation and to avoid individual researcher bias. To enhance credibility of the findings, the independently developed findings were compared and discussed regularly. Divergences were explicitly addressed, and consensus was reached through iterative dialogue. Our qualitative content analysis sought to maintain flexibility and interpret Fayol’s management functions in today’s organizational management context. Not all articles were forcibly linked to all four management functions; instead, the analysis focused on the part of each article that was most clearly related to management. Chapter 4 presents the findings of the content analysis, followed by a discussion on the implications of these insights for organization management research and practice.

AI is becoming an increasingly valuable tool for making sense of large amounts of data, especially when helping people make better decisions. However, making the most of AI may require companies to reorganize their tasks and processes. This aims to free human talent for more meaningful and often creative work.

Beyond decision-making, AI is great at monitoring company performance and identifying potential risks before they escalate into significant issues. However, research on using AI in leadership is still limited. Only recently have studies begun to explore how leadership cultures are evolving with the increasing integration of AI into the workplace.

In the following sections, we will break down AI research in innovation using the POLC framework.

AI enhances strategic planning by enabling rapid analysis of data on both internal and external environments, which enables organizations to make better-informed decisions based on current knowledge and future foresight. AI often exceeds human skills, especially in industries where large data sets are explored. For example, in drug development, Lou & Wu (2019) noted that AI accelerates the identification of novel drug candidates at the discovery and pre-clinical trial stages. They refer to industrial giant Novartis, which had accelerated the analysis of a large amount of data from medical records and clinical trials through AI and predictive analysis to identify new treatments. This search and evaluation for novel ideas is a well-studied phenomenon in the AI literature (e.g. Leka, 2024; Salminen, Pyykkönen, & Salminen, 2024). Also, Poser, Küstermann, Tavanapour, & Bittner (2022) reported the benefits of AI-powered conversational agents in idea screening and evaluation, and Rout, Swain, Sarangi, Agrawalla, & Dash (2024) reported the benefits of machine learning in uncovering patterns in customer behavior, markets and competitors, leading to breakthrough innovations.

Thus, AI facilitates the analysis of data collected by the company by focusing human attention on anomalies or other key features that can help understand the customers and their behavior (Vocke, Constantinescu, & Popescu, 2019; Hartman, Kvasnička, Čejka, & Pilař, 2024). This understanding is then used in data-driven decision-making either for current issues (Leszkiewicz, Hormann, & Krafft, 2022; Pietronudo et al., 2022) or scouting new opportunities in the technology (Piccialli, Giampaolo, Prezioso, Camacho, & Acampora, 2021; Jaheer Mukthar, Sivasubramanian, Ramirez Asis, & Guerra-Munoz, 2022; Füller et al., 2022).

These studies show that AI helps companies incorporate data from outside the company into their innovation planning processes. Some researchers have interpreted this power of AI to rapidly analyze data as co-development or co-creation with the customers (Vocke & Bauer, 2020; Jiang, Jiang, Sun, & Fan, 2023). For example, Cillo & Rubera (2024) constructed a roadmap for companies to involve customers through AI in every step of the innovation and marketing process. Interestingly, the survey study by Roberts & Candi (2024) brings forth that, contrary to common understanding, AI is mainly used to develop innovations rather than ideation.

In AI-driven organizing, AI assumes a more active role in driving changes in organizational structures (Vocke et al., 2019; Bahoo, Cucculelli, & Qamar, 2023). Füller et al. (2024) encourage organizations to rethink their strategies, organizational models and roles and responsibilities of the personnel, as fewer but more specified people are needed to act based on real-time data that AI collects and analyzes.

As it was often seen as a valuable resource for innovation management, organizations seeking to maximize the benefits of AI in innovation seem to opt for decentralized structures or cross-functional teams (e.g. Füller et al., 2024; Pietronudo et al., 2022). This helps organizations to accelerate knowledge flows (Tekic, Cosic, & Katalinic, 2019; Correia & Matos, 2021) with AI and even shift the innovation from the hands of the owners or top management to customers (Pietronudo et al., 2022). However, empirical research on organizational changes is still rare. More empirical research is done on AI’s impact on task automatization that seeks to free the human workforce for more meaningful and usually creative tasks (e.g. Roberts & Candi, 2024). For example, in Maione and Leoni’s (2021) study, respondents from the hospitality industry highlighted the value of AI in handling routine tasks such as accounts payable. This allowed the company to reorganize work in a way that enabled the workforce to focus on roles where their contribution was most valuable.

In addition to its impact on organizing human resources, AI also helps companies optimize processes and material flows (Rout et al., 2024; Vărzaru & Bocean, 2024), both as part of innovation planning and implementation.

Perhaps owing to the social nature of leading, the research on AI from this perspective was still limited. Although automatization and replacing humans in routine tasks improve organizational efficiency, Eisenreich, Just, Jiménez, & Füller (2024) remind us that we should not forget human beings, as they have social and experience-based knowledge that is still essential for innovation. Automatization may raise fears of losing one’s job (Cillo & Rubera, 2024). However, one way to motivate personnel to use AI is to involve them in organizing their work (Altepost et al., 2024).

AI seems to be steering organizations toward new and more agile cultures (Cimino et al., 2024), where AI necessitates reconfiguration of innovation teams and intensive AI-accelerated collaboration between different stakeholders (Füller et al., 2024). According to Füller et al. (2024), more empirical research is needed on how organizations can balance control and benefit-sharing with atypical stakeholders, including, e.g. hospitals, universities, researchers and system providers. They may provide essential data for innovations, forming an open innovation system that relies heavily on AI and human interaction. Such a system would challenge the conventional management and leadership practices.

AI can have a central role in monitoring and optimizing innovation activities. AI tools help monitor performance (Sedkaoui & Benaichouba, 2024; Adiguzel, Sonmez Cakir, Pinarbasi, Güner Gültekin, & Yazici, 2024; Jaheer Mukthar et al., 2022), controlling quality (Carvalho Proenca, 2024; Rout et al., 2024) and managing risks (Lou & Wu, 2019; Lee & Tajudeen, 2020; Guo & Polak, 2021; Roberts & Candi, 2024). However, Jiang et al. (2023) also point out in their literature review that although AI enables real-time risk identification, it also acts as a source of risk due to compromised data security or bias.

From the innovation management perspective, AI enables faster analysis of innovation prototypes through fast-fail (e.g. Pietronudo et al., 2022) or risk assessment (e.g. Lee & Tajudeen, 2020). This is particularly important in industries such as drug development (Lou & Wu, 2019), where generating ideas is inexpensive, but further development into final products is extremely costly. Self-assessment (Carvalho Proenca, 2024), quality control (Jaheer Muktha et al., 2022) and continuous monitoring of customer satisfaction (Adiguzel et al., 2024) may lead to direct cost savings in the long run. Jaheer Mukthar et al. (2022) also envision ethically questionable possibilities to monitor real-time customer mood and satisfaction using high-sensor cameras and facial recognition technologies installed at brick-and-mortar stores.

What seems to be missing from the current AI literature from the controlling perspective is the monitoring of personnel performance within the organization. The data is usually collected and analyzed from external perspectives, such as customer, market or future trend points of view. If internal company data is analyzed for control purposes, it is usually diagnostic data about prototype tests (e.g. Rout et al., 2024) or monitoring data that helps to cut costs through optimizing (e.g. Vărzaru & Bocean, 2024).

Table 2 summarizes our findings on this literature review, where the POLC framework was used to categorize AI-powered innovation management phases. Currently, the phases where AI acts as a tool complementing or replacing humans are more prevalent in the literature. Empirical studies on AI’s impact on organizational structures, innovation capabilities and business performance are still rare, in line with the fact that AI-driven innovation management literature is only in its initial phase. However, we can assume that the research literature will continue to grow in the coming years, leading to improvements in the quantity and quality of empirical research.

Innovation management focuses on leading people and creating effective practices and processes that guide their activities (Tidd & Bessant, 2018). While AI takes a more significant role in various functions (as summarized in Table 2), not only as a supporting tool but increasingly as a potential autonomous actor or managerial agent, the foundations of organization management are challenged. From a planning perspective, AI disrupts established assumptions and reshapes the innovation landscape through its superior capabilities in data analysis. Consequently, organizations increasingly need to revise their innovation processes and adopt new thinking methods as conventional models become obsolete (Roberts & Candi, 2024). From an organizing standpoint, leveraging AI and data analytics requires structural configurations and new skills, roles and resource allocation (Füller et al., 2024; Pietronudo et al., 2022) that enable personnel to focus on creative tasks, while routine tasks can be delegated to AI. This change challenges leadership which becomes more complex as it necessitates human-centric motivation and an organizational culture that is willing and capable of leveraging AI to its full potential (e.g. Eisenreich et al., 2024; Cimino et al., 2024). Finally, controlling the activities in AI-enhanced environments demands organizations to develop new metrics, feedback loops and evaluation systems to track performance, learn from behavior patterns and evaluate the potential risks in real-time (Sedkaoui & Benaichouba, 2024; Rout et al., 2024; Jiang et al., 2023).

Managing innovation processes in the current AI-driven business environment involves rethinking the decision-making processes and human–machine interaction. The main purpose is not to replace human judgment but to support it with AI technologies. This way, innovation processes within the organization become more data-driven, responsive and, ideally, error-free. At the moment, the wide-scale adoption of AI is still in its early phase. However, we can already expect it to become an integral part of planning, organizing, leading and controlling innovation processes. When viewed through the POLC framework, AI can be seen to impact these organizational management functions in different ways. In the planning phase, AI enhances strategic decision-making by enabling the analysis of large data sets and forecasting trends with better accuracy, especially in data-intensive industries. It is primarily used in the development phase of innovation rather than in ideation. In the organizing function, the use of AI can promote decentralized, agile structures and automate routine tasks and help optimize operational workflows and material flows.

Although based on the findings of our literature review, research on AI in the leadership function is very limited, it is linked to more agile and collaborative cultures and encourages employee involvement to ease concerns about automation. In the controlling phase, AI plays a key role in monitoring performance, ensuring quality and managing risks in real time. While its use in internal personnel monitoring seems still rare, AI significantly contributes to cost efficiency and innovation agility. Overall, AI supports data-driven innovation management by enabling opportunity identification, customer co-creation and continuous process improvement, which should encourage organizations to rethink their traditional management models.

Although the potential for using AI in quality control and risk management has already been well identified in the literature, employee performance control and monitoring have not been widely discussed. Using AI tools for employee performance control and monitoring includes fundamental ethical and legal considerations, which may be why it has not yet been seen as a particularly potential target for AI utilization. Also, country-specific legislation impacts human work-related control.

Fayol’s theory on industrial management has paved the way for later developments in management theories and innovation management (Hatchuel & Segrestin, 2019). The POLC framework can be viewed as a framework to study and understand not only a linear innovation process inside a company but a broader frame of planning, organizing, leading and controlling innovation functions in the intertwined systemic context of the current innovation management paradigm. The frame provides a holistic view of how AI could offer various benefits for organizations orchestrating different actors and collaborative activities with shared resources for a common goal under the ongoing “Creative Destruction” (see Schumpeter, 1975) in the AI era. In the literature review, insights on the impact of AI on the leading function were still limited. AI-based solutions enable more efficient working and workflow organization at individual and team levels, and perhaps increasingly in virtual working environments. However, in organizational settings where disruptive technologies such as AI influence work, human interaction-based leadership skills such as coaching, creating personal connections, negotiation skills and managing conflicts are essential (Turesky et al., 2020). Innovation management also involves the coordination of (cross-organizational) networks, a domain in which AI may still have its limitations due to restricted information flows and data availability across organizational boundaries but also because networked collaboration relies on leadership skills that inherently require human interaction.

The results of the literature review shed light on the management landscape in an era where AI influences organizational processes, decision-making, resource allocation, leadership and performance control. Based on the findings, AI can have and is having a significant impact on the innovation process planning and organization. It enables more efficient and timely decision-making based on data analysis and forecasting, aiding innovation development and helps by automating routine tasks and optimizing individual steps in the innovation process. Also, AI enables real-time performance tracking, quality assessment and better risk identification.

The findings of our study show that the leadership perspective of AI-enhanced management is still particularly limited. Could AI tools be used more to strengthen an organizational culture that encourages innovation, creativity and collaboration, as well as human-centered leadership? What aspects of these activities require an intuitive understanding of human-like behavior, which current AI is incapable of? In what tasks requiring human leadership skills can AI support leaders and where must they continue to rely primarily on human-based thinking? These questions could serve as a basis for future empirical research on the application of AI in leadership.

When investigating AI’s impact on organizational structures, management and innovation capabilities, conceptual and literature review papers are thus far the most common types of research publications, which highlights the significant need for more empirical research on this matter. New technologies that shape management functions, such as AI, may lead to the need to examine the functions and practices of organizational management in a new way (see, e.g. Cropanzano & Lehman, 2023; Phillips & Su, 2013). As Philip (2022) points out, we need more studies about how AI blends with human reasoning to advance strategic decision-making and other management functions. In the innovation context, much less researched management-related practices in the AI era include collaborative problem-solving, adaptive organizational learning, strategic orientation and opportunity recognition, which could be more extensively addressed, particularly in empirical studies in the future.

Based on the results of this study, the potential for using AI in the management of innovation processes is extensive but not unlimited for the time being. AI has and will have significant impacts on different management functions, which is why each organization should examine what AI means, particularly for them, how it can be accelerated and how it will affect their competitiveness and operational environment in the future, both at the operational and strategic management level. For organizations, it is essential to choose the tools and application methods that improve their relative competitiveness in the short and long term.

Our literature search was limited by selected publication period, publication sources and search terms. We acknowledge that non-English or unavailable publications may have also included valuable insights on this topic. The most significant limiting factor was the relatively narrowly defined search terms. However, within the scope of this study, it was necessary to limit the literature quite precisely to enable the in-depth qualitative analysis of the final data set rather than producing a quantitative method-based bibliometric analysis. A similar type of qualitative study focusing on cross-organizational innovation networks or ecosystems, instead of the process perspective employed in this study, would undoubtedly provide complementary insights into the relationship between AI and innovation management.