Information Free Full-Text Investigating the Influence of Artificial Intelligence on Business Value

Investigating the Influence of Artificial Intelligence on Business Value in the Digital Era of Strategy: A Literature Review

For organizations, integrating artificial intelligence (AI) into business and IT strategies to develop new business models and competitive advantages holds great potential. While most companies struggle to capitalize on value creation opportunities, some pioneers have been successful in using AI. Based on the research methodology of Webster and Watson (2020), 139 peer-reviewed papers were reviewed. According to the literature, prior studies have highlighted the performance advantages, success criteria, and difficulties of AI implementation. The review revealed open challenges and topics that require further research/examination to develop AI's potential and integrate it into business/IT strategies to enhance various streams of business value. Only by adopting and implementing these new cutting-edge technologies will organizations succeed in shaping the digital transformation of the current era. Despite the revolutionary and dynamic benefits that AI capabilities bring, resource orchestration along with governance in this dynamic environment is still highly complex and we are in the early stages of research on the strategic application of AI in organizations. Keywords

It has become clear that both socio-technical and political-economic changes, along with demographic changes, have accelerated rapidly during the COVID-19 pandemic. Modern companies have had to hone their adaptability to manage changing market dynamics and customer behaviors under these challenging circumstances. The rapid evolution of organizations is enabled by adaptability, which is also the foundation of organizational transformation and digital transformation [1, 2], but strategic guidance to keep up with the exponential pace of modern technology is still lacking [3]. Incumbents are using cutting-edge technologies to improve and adapt their operations. Artificial intelligence, also known as the next wave of analytics, is one of these technologies [4, 5].

1. Introduction

The word "artificial intelligence" is a wide range of human behavior, decisio n-making, stat e-o f-th e-art analysis, applications, and logica l-based techniques, such as learning and problem solving. [6, 7]. However, as part of digital transformation, AI technology provides many opportunities to transform corporate operations in many fields. For example, the application of A I-led decisio n-making for loans, trusts, and sales prediction is listed [9, 10]. In addition, AI can automate manual processes [11], and enable human and AI to improve the process of collaborating in positive forms, which gives great profits.

According to a recent Gartner report [13], senior managers consider analytics and AI as an important game changer for companies to survive the current crisis. Despite the excitement related to the promising of AI, considerable scientific discussions are currently being conducted on the barriers of recruitment and the skills and competencies necessary for the results of AI, which is useful from a strategic perspective [14, 15]. 。 AI can make a great profit to business, but in order to introduce AI and enable high achievements that do not deny all costs and effort, the organization is persuaded when meaningful changes are needed. A powerful shared vision must be defined [16, 17, 18]. In addition, the organization uses many individual technologies such as artificial intelligence to stimulate innovation, strengthen customer service and experience, and create adaptive transformation and detection and ability to promote performance improvement. Must be done [5, 19, 20, 21, 22, 23].

Information systems (IS) and business research by academic and specialized institutions indicate that AI is a market driver [24, 25, 26, 27, 27, 28]. Since the 1950s, when the idea of artificial intelligence first appeared, research on this theme has been steadily progressing. However, in the last 10 to 15 years, the development and practical use of AI have been significantly accelerated by increasing the possibility of use of vast amounts of data, improving calculation processing capacity, new AI approaches, learning algorithms, and applications. There is a wide range of human behavior, decisio n-making, stat e-o f-th e-art analysis, applications, and logica l-based techniques, such as learning and problem solving, such as learning and problem solving [6, 7 ]. However, as part of digital transformation, AI technology provides many opportunities to transform corporate operations in many fields. For example, the application of A I-led decisio n-making for loans, trusts, and sales prediction is listed [9, 10]. In addition, AI can automate manual processes [11], and enable human and AI to improve the process of collaborating in positive forms, which gives great profits.

Information systems (IS) and business research by academic and specialized institutions indicate that AI is a market driver [24, 25, 26, 27, 27, 28]. Since the 1950s, when the idea of artificial intelligence first appeared, research on this theme has been steadily progressing. However, in the last 10 to 15 years, the development and practical use of AI have been significantly accelerated by increasing the possibility of use of vast amounts of data, improving calculation processing capacity, new AI approaches, learning algorithms, and applications. [29.] The word "artificial intelligence" refers to a wide range of human behavior, decisio n-making, stat e-o f-th e-art analysis, applications, and logica l-based techniques, such as learning and problem solving. [6, 7]. However, as part of digital transformation, AI technology provides many opportunities to transform corporate operations in many fields. For example, the application of A I-led decisio n-making for loans, trusts, and sales prediction is listed [9, 10]. In addition, AI can automate manual processes [11], and enable human and AI to improve the process of collaborating in positive forms, which gives great profits.

AI is expanding its impact in various social fields, including marketing, healthcare, and human rights. Allowing the creation of AI applications to proceed without any oversight could be harmful [30, 31]. As a result, it is important to support trustworthy AI that adheres to legal requirements and supports ethical standards (from technical and social perspectives). AI should be considered a dynamic computational frontier, so governance should not be limited to just its content but also include its analysis [25]. In addition to IT and data governance, analytics governance mechanisms are also needed to address issues such as a lack of alignment between business users and analytics professionals [32].

Other scholars share the same view, but many organizations are still experimenting with AI (e. g., using early pilots) and only a few have incorporated AI as part of the daily work routine across the organization [24, 33]. However, organizations are investing heavily in AI and its underlying machine learning algorithms to improve operations and support decision-making [34]. As mentioned earlier, technologies like AI bring new opportunities and advantages to enterprises, but also new challenges [31, 35]. AI solutions are adopted and used by enterprises to automate processes, increase productivity, reduce costs, and gain a competitive advantage over rivals [36, 37, 38]. A key element in achieving these goals is AI governance. Butcher and Beridze argue that AI governance can be “characterized as a collection of tools, methods, and levers that influence the development and application of AI” [39]. However, there are still research opportunities in how to implement AI governance in enterprises and how AI governance can help enterprises achieve their goals. AI is expanding its impact in various social fields, such as marketing, healthcare, and human rights. Allowing the creation of AI applications to proceed without any oversight could be harmful [30, 31]. As a result, it becomes important to support trustworthy AI that adheres to legal requirements and supports ethical standards (from a technical and social perspective). AI should be considered a dynamic computational frontier, so governance should go beyond just its content, but also include its analysis [25]. In addition to IT and data governance, analytics governance mechanisms are also needed to address issues such as the lack of alignment between business users and analytics professionals [32].

Other scholars share the same view, but many organizations are still experimenting with AI (e. g., using early pilots) and only a few have incorporated AI as part of their daily work routines across the organization [24, 33]. However, organizations are investing heavily in AI and its underlying machine learning algorithms to improve operations and support decision-making [34]. As mentioned earlier, technologies like AI bring new opportunities and advantages to companies, but also new challenges [31, 35]. AI solutions are being adopted and used by companies with the aim of automating processes, increasing productivity, reducing costs, and gaining a competitive advantage over rivals [36, 37, 38]. A key element in achieving these goals is the governance of AI. Butcher and Beridze argue that AI governance can be characterized as a collection of tools, methods, and levers that influence the development and application of AI.[39] However, there are still research opportunities in how to implement AI governance in enterprises and how it can help enterprises achieve their goals. AI is having a growing impact in various societal fields, such as marketing, healthcare, and human rights. Allowing the creation of AI applications to proceed without any oversight can be harmful.[30, 31] As a result, it becomes important to support trustworthy AI that adheres to legal requirements and supports ethical standards (from technical and social perspectives). Since AI should be considered a dynamic computational frontier, governance should go beyond just its content, but also include its analysis.[25] In addition to IT and data governance, analytics governance mechanisms are also needed to address issues such as the lack of alignment between business users and analytics professionals.[32] Other scholars share the same view, but many organizations are still using AI experimentally (e. g., with early pilots) and only a few have incorporated AI as part of the organization-wide daily work routine [24, 33]. However, organizations are investing heavily in AI and its underlying machine learning algorithms to improve operations and support decision-making [34]. As mentioned earlier, technologies like AI bring new opportunities and benefits to companies, but also new challenges [31, 35]. AI solutions are being adopted and used by companies to automate processes, increase productivity, reduce costs, and gain a competitive advantage over rivals [36, 37, 38]. A key element in achieving these goals is the governance of AI. Butcher and Beridze argue that AI governance can be “characterized as a collection of tools, methods, and levers that influence the development and application of AI” [39]. However, there are still research opportunities on how to implement AI governance in companies and how AI governance can help companies achieve their goals.

2. Methodology and Data

We know that the IS literature will develop difficult individual abilities by integrating and using various complementary resources at the corporate level [14, 40, 41]. Based on this series of studies, this research is considered one of such resources, which is necessary to develop AI technology, but is not enough. This indicates that the AI approach is easy to obtain in the market and can be duplicated, but that alone is unlikely to create a great competitive advantage. Furthermore, the data required for these methods alone is not enough to develop outstanding AI abilities. According to early reports from industry leaders in the introduction of AI, the organization is special for physical, human, and organizational resources to create AI capabilities that can truly add value by separating competitors. It is suggested that a combination is necessary [14, 42]. [43, 44] has little comprehensive theoretical and empirical knowledge regarding the method of developing and using AI abilities. We know that the IS literature will develop difficult individual abilities by integrating and using various complementary resources at the corporate level [14, 40, 40, 41]. Based on this series of studies, this research is considered one of such resources, which is necessary to develop AI technology, but is not enough. This indicates that the AI approach is easy to obtain in the market and can be duplicated, but that alone is unlikely to create a great competitive advantage. Furthermore, the data required for these methods alone is not enough to develop outstanding AI abilities. According to early reports from industry leaders in the introduction of AI, the organization is special for physical, human, and organizational resources to create AI capabilities that can truly add value by separating competitors. It is suggested that a combination is necessary [14, 42]. [43, 44] has little comprehensive theoretical and empirical knowledge regarding the method of developing and using AI abilities. We know that the IS literature will develop difficult individual abilities by integrating and using various complementary resources at the corporate level [14, 40, 41]. Based on this series of studies, this research is considered one of such resources, which is necessary to develop AI technology, but is not enough. This indicates that the AI approach is easy to obtain in the market and can be duplicated, but that alone is unlikely to create a great competitive advantage. Furthermore, the data required for these methods alone is not enough to develop outstanding AI abilities. According to early reports from industry leaders in the introduction of AI, the organization is special for physical, human, and organizational resources to create AI capabilities that can truly add value by separating competitors. It is suggested that a combination is necessary [14, 42]. [43, 44] has little comprehensive theoretical and empirical knowledge regarding the method of developing and using AI abilities.

2.1. Prior Literature Reviews

According to van de Wetering et al. [13], adaptive transformation capabilities are defined as a company's ability to adopt new strategic directions while simultaneously identifying and exploiting new market and technological opportunities and developing organizational capabilities [3, 42, 45]. This insight can also be considered as a dynamic capability [47] that develops organizational resources and capabilities to achieve intended outcomes [46] and drives the organization's future business activities and business value potential. However, little is currently known about the ambidextrous use of AI, how companies can use AI routinely and creatively, how AI supports dynamic capabilities, and especially how AI collaborates to create value [48]. Today, more and more companies are using AI to improve and adapt their organizational operations, but there is a lack of theoretical justification and empirically validated data to help guide the strategic orientation of organizations [42, 45]. As a result, the main objective of this paper is to draw attention to key aspects of how AI capabilities contribute to a different perspective on the business value achieved through the alignment of business and IT strategies. According to van de Wetering et al. [13], adaptive transformation capabilities are defined as a company's ability to adopt new strategic directions while simultaneously identifying and exploiting new market and technological opportunities and developing organizational capabilities [3, 42, 45]. This insight can also be considered as a dynamic capability [47] that develops organizational resources and capabilities to achieve intended outcomes [46] and drives the organization's future business activities and business value potential. However, little is currently known about the ambidextrous use of AI, how companies can use AI on a day-to-day and creative basis, how AI supports dynamic capabilities, and especially how AI collaborates to create value [48]. Today, more and more companies are using AI to improve and adapt their organizational operations, but there is a lack of theoretical justification and empirically validated data to help guide the strategic orientation of organizations [42, 45]. As a result, the main objective of this paper is to draw attention to key aspects of how AI capabilities contribute to a different perspective on the business value achieved through the alignment of business and IT strategies. According to van de Wetering et al. [13], adaptive transformation capabilities are defined as a company's ability to adopt new strategic directions while simultaneously identifying and exploiting new market and technological opportunities and developing organizational capabilities [3, 42, 45]. This insight can also be considered as a dynamic capability [47] that develops organizational resources and capabilities to achieve intended outcomes [46] and drives the organization's future business activities and business value potential. However, little is currently known about the ambidextrous use of AI, how companies can use AI routinely and creatively, how AI supports dynamic capabilities, and especially how AI collaborates to create value [48]. Today, more and more companies are using AI to improve and adapt their organizational operations, but there is a lack of theoretical justification and empirically validated data to help guide the strategic orientation of organizations [42, 45]. As a result, the main objective of this paper is to draw attention to key aspects of how AI capabilities contribute to a different perspective on the business value achieved through the alignment of business and IT strategies.

This study is about the integration of AI with business and IT strategies in the context of digital transformation. The study found that organizations are generally undergoing digital transformation driven by technological advances and regulatory changes, and that integrating AI capabilities with business and IT strategies is essential to drive business value and achieve digital transformation. The study also highlighted the importance of a dual strategic focus on innovative and tactical AI adoption, the need for responsible AI governance, and the need to leverage AI to support adaptive transformation. The findings contribute to our understanding of how organizations can optimize their use of AI to drive strategic agility and benefit realization.

The paper is structured as follows: The methodology used for the literature review is described in Section 2, and the results from the evaluation of the literature are discussed in Section 3. The final section concludes by summarizing the findings, stating their implications, outlining limitations, and inviting the scientific community to further explore the issues and connections identified in this conceptual model.

Our study was conducted following the guidelines of Webster and Watson's systematic literature [49]. (1) A recent literature search was conducted to identify databases and keywords. (2) This was followed by an exhaustive backward search to examine cited literature and a forward search to look for references of selected publications. (3) At the end of the process, potential research opportunities were identified and all papers were classified by concept according to their individual content.

Given that the number of studies on AI has increased dramatically over the years, it is not surprising that in the field of management, systematic literature reviews [1, 7, 26, 27, 50, 51, 52, 53, 54] and bibliographic analysis [55, 56, 57] are widely used as review methods. Of note, these studies cover a wide range of topics, indicating that AI in the organizational context is applied in different domains for different purposes. To the best of our knowledge, no recent literature review has specifically addressed the integration of business/IT strategies and AI capabilities to drive business value improvement. Rather, these reviews explore this phenomenon by investigating a wide range of factors that may affect this relationship. All these reviews are presented below (Table 1).

2.2. Article Selection

Initially, Kitsios and Kamariotou [1] presented a research question on the use of AI and ML in organizational planning and decision-making processes. They identified future research needs and developed a theoretical model that discusses four sources of value creation, including AI and machine learning in organizations, alignment of AI and information technology (IT) tools with organizational strategies, AI, knowledge management and decision-making, and AI, service innovation and value. Çebeci's study [50] completed a contextual, journal, and concept-based analysis with AI techniques, application areas, and future domain preferences, and presented a detailed analysis of the current status and direction of AI research in MIS literature. Given that the number of studies on AI has increased dramatically over the years, it is not surprising that in the field of management, systematic literature reviews [1, 7, 26, 27, 50, 51, 52, 53, 54] and bibliographic analysis [55, 56, 57] are widely used as review methods. It is noteworthy that these studies cover a wide range of topics, indicating that AI in organizational contexts is applied in different areas for different purposes. To the best of our knowledge, no recent literature review has specifically addressed the integration of business/IT strategies and AI capabilities to drive business value improvement. Rather, these reviews explore this phenomenon by investigating a wide range of factors that may affect this relationship. All these reviews are presented below (Table 1).

Initially, Kitsios and Kamariotou [1] presented a research agenda on the use of AI and ML in organizational planning and decision-making processes. They identified future research needs and created a theoretical model that discusses four sources of value creation, including AI and machine learning in organizations, alignment of AI and information technology (IT) tools with organizational strategies, AI, knowledge management and decision-making, and AI, service innovation and value. Çebeci's study [50] completed a contextual, journal, and concept-based analysis with AI techniques, application areas, and future domain preferences, and presented a detailed analysis of the current status and direction of AI research in the MIS literature. Given that the number of studies on AI has increased dramatically over the years, it is not surprising that in the field of management, systematic literature reviews [1, 7, 26, 27, 50, 51, 52, 53, 54] and bibliographic analysis [55, 56, 57] are widely used as review methods. Of note, these studies cover a wide range of topics, indicating that AI in the organizational context is applied in different domains for different purposes. To the best of our knowledge, no recent literature review has specifically addressed the integration of business/IT strategies and AI capabilities to drive business value improvement. Rather, these reviews explore this phenomenon by investigating a wide range of factors that may affect this relationship. All these reviews are presented below (Table 1).

Enholm et al. [51] provide a systematic review of the literature that seeks to clarify how organizations can use AI technologies in their operations and to identify mechanisms of value creation. They synthesize the existing literature and identify (1) the main drivers and inhibitors of AI adoption and use, (2) a typology of AI use in organizational environments, and (3) primary and secondary outcomes of AI. They also present gaps in the literature and develop a research agenda that suggests areas for further study in more detail. The framework developed by Keding [27] provides a structure for the research field by grouping related papers into two research areas: situation-oriented research explores the antecedents of AI use in strategic management, while outcome-oriented research examines the impact of AI on strategic management at both the individual and organizational levels. To set the research agenda, Zuiderwijk et al. [52] conducted a thorough evaluation of the literature on the effects of AI use in public governance. They used qualitative analysis to categorize the potential benefits of AI in government into nine categories: (1) effectiveness and efficiency benefits, (2) risk identification and monitoring benefits, (3) economic benefits, (4) data and information processing benefits, (5) service benefits, (6) society-wide benefits, (7) decision-making benefits, (8) engagement and interaction benefits, and (9) sustainability benefits. Eight categories were used to identify challenges to the adoption of AI in government: (1) data challenges, (2) organization and management challenges, (3) skills challenges, (4) interpretation challenges, (5) ethics and legitimacy challenges, (6) political, legal, and policy challenges, (7) social and societal challenges, and (8) economic challenges. Literature Review Borges et al. [26] conducted a literature review to examine the relationship between AI and corporate strategy. Enholm et al. [51] provide a systematic review of the literature that seeks to clarify how organizations can use AI technologies in their operations and to identify mechanisms of value creation. They synthesize the existing literature and identify (1) the main drivers and inhibitors of AI adoption and use, (2) a typology of AI use in organizational environments, and (3) primary and secondary outcomes of AI. They also present gaps in the literature and develop a research agenda that suggests areas for further study in more detail. The framework developed by Keding [27] provides a structure for the research field by grouping related papers into two research areas: situation-oriented research explores the antecedents of AI use in strategic management, while outcome-oriented research examines the impact of AI on strategic management at both the individual and organizational levels. To set the research agenda, Zuiderwijk et al. [52] conducted a thorough evaluation of the literature on the effects of AI use in public governance. They used qualitative analysis to categorize the potential benefits of AI in government into nine categories: (1) effectiveness and efficiency benefits, (2) risk identification and monitoring benefits, (3) economic benefits, (4) data and information processing benefits, (5) service benefits, (6) society-wide benefits, (7) decision-making benefits, (8) engagement and interaction benefits, and (9) sustainability benefits. Eight categories were used to identify challenges to the adoption of AI in government: (1) data challenges, (2) organization and management challenges, (3) skills challenges, (4) interpretation challenges, (5) ethics and legitimacy challenges, (6) political, legal, and policy challenges, (7) social and societal challenges, and (8) economic challenges. Literature Review Borges et al. [26] conducted a literature review to examine the relationship between AI and corporate strategy. Enholm et al. [51] provide a systematic review of the literature that seeks to clarify how organizations can use AI technologies in their operations and to identify mechanisms of value creation. They synthesize the existing literature and identify (1) the main drivers and inhibitors of AI adoption and use, (2) a typology of AI use in organizational environments, and (3) primary and secondary outcomes of AI. They also present gaps in the literature and develop a research agenda that suggests areas for further study in more detail. The framework developed by Keding [27] provides a structure for the research field by grouping related papers into two research areas: situation-oriented research explores the antecedents of AI use in strategic management, while outcome-oriented research examines the impact of AI on strategic management at both the individual and organizational levels. To set the research agenda, Zuiderwijk et al. [52] conducted a thorough evaluation of the literature on the effects of AI use in public governance. They used qualitative analysis to categorize the potential benefits of AI in government into nine categories: (1) effectiveness and efficiency benefits, (2) risk identification and monitoring benefits, (3) economic benefits, (4) data and information processing benefits, (5) service benefits, (6) society-wide benefits, (7) decision-making benefits, (8) engagement and interaction benefits, and (9) sustainability benefits. Eight categories were used to identify challenges to the adoption of AI in government: (1) data challenges, (2) organization and management challenges, (3) skills challenges, (4) interpretation challenges, (5) ethics and legitimacy challenges, (6) political, legal, and policy challenges, (7) social and societal challenges, and (8) economic challenges. Literature Review Borges et al. [26] conducted a literature review to examine the relationship between AI and corporate strategy.

From this perspective, in this article, we perform a thorough literature review that focuses on the integration of AI and business strategies, combine existing approaches and models to emphasize the expected advantages, difficulties, and opportunities. By starting discussions on the direction of research, the purpose was to fill this gap. Trunk et al. [7] conducted literature research to examine how artificial intelligence and corporate decisio n-making are dynamic. In order to provide a summary of existing research viewpoints on the relevance of AI and corporate decisio n-making in a dynamic environment, the authors searched for pee r-reviewed publications and performed content analysis. The insights were first explained how humans can use AI in a dynamic situation, and then provided in a theoretical framework to overview the issues, conditions, and meanings to be considered. Mr. GROVER et al Investigated the possibilities. From this point of view, this article gives a thorough review of the AI and a business strategy to combine existing approaches and models to emphasize the expected advantages, difficulties, and opportunities. The aim was to fill this gap by starting discussions on the direction of new research. Trunk et al. [7] conducted literature research to examine how artificial intelligence and corporate decisio n-making are dynamic. In order to provide a summary of existing research viewpoints on the relevance of AI and corporate decisio n-making in a dynamic environment, the authors searched for pee r-reviewed publications and performed content analysis. The insights were first explained how humans can use AI in a dynamic situation, and then provided in a theoretical framework to overview the issues, conditions, and meanings to be considered. Mr. GROVER et al Investigated the possibilities. From this perspective, in this article, we perform a thorough literature review that focuses on the integration of AI and business strategies, combine existing approaches and models to emphasize the expected advantages, difficulties, and opportunities. By starting discussions on the direction of research, the purpose was to fill this gap. Trunk et al. [7] conducted literature research to examine how artificial intelligence and corporate decisio n-making are dynamic. In order to provide a summary of existing research viewpoints on the relevance of AI and corporate decisio n-making in a dynamic environment, the authors searched for pee r-reviewed publications and performed content analysis. The insights were first explained how humans can use AI in a dynamic situation, and then provided in a theoretical framework to overview the issues, conditions, and meanings to be considered. Mr. GROVER et al Investigated the possibilities.

To investigate the AI controversy in organizational and business administration, Smacchia and ZA [55] searched for a computational search in their research. Through impact analysis and content analysis, they revealed what kind of publications and research fields had developed over time. DI VAIO et al. [56] investigates a group of literature on the application of AI in the development of sustainable business models, quantitatively seeing research groups in this field, and the explanation functional growth of AI and machine learning. Discussed the relevance of sustainable development. Finally, according to the cluster analysis of DHAMIJA and BAG [57], six clusters have emerged as an important issue for AI's current and future researchers: (1) and (2) engineering (2) engineering. / Research and automation, (3) Business performance and machine learning, (4) Sustainable supply chain and sustainable development, (5) Technology introduction and green supply chain management, (6) Internet.

2.3. Classification Framework

Search for artificial intelligence (AI), AI ability, business strategy, IT strategy, business value, digital transmission phrases in titles, keywords, and abstracts to search for dissertations from Science, Science Direct, Web of Science databas did. Each paper is published in a recipient or a meeting record. There was no limit on the publishing year.

3. Results

3.1. Artificial Intelligence Capabilities in Organizations

3.1.1. AI Ambidexterity

As a result of searching with the abov e-mentioned keywords, a total of 1738 papers were collected. As a result of the restrictions on language, source, and categories, a total of 942 papers. As a result of examining the contents of the remaining papers, we decided to exclude 547 editions from the title, 164 from the abstract, and 109 from the actual content. As a result, six duplicate articles were excluded, and the total number of papers was 116. In addition to these, 12 articles have been added as a result of the pr e-search and 11 posts of the rear search. As a result, 139 papers were subject to reviews (Fig. 1). SMACCHIA and ZA [55] searched for a literature search by computers to investigate the AI controversy in organizational and business administration. Through impact analysis and content analysis, they revealed what kind of publications and research fields had developed over time. DI VAIO et al. [56] investigates a group of literature on the application of AI in the development of sustainable business models, quantitatively overlook the research groups in this field, and the explanation functional growth of AI and machine learning. Discussed the relevance of sustainable development. Finally, according to the cluster analysis of DHAMIJA and BAG [57], six clusters have emerged as an important issue for AI's current and future researchers: (1) and (2) engineering (2) engineering. / Research and automation, (3) Business performance and machine learning, (4) Sustainable supply chain and sustainable development, (5) Technology introduction and green supply chain management, (6) Internet.

As a result of searching with the abov e-mentioned keywords, a total of 1738 papers were collected. As a result of the restrictions on language, source, and categories, a total of 942 papers. As a result of examining the contents of the remaining papers, we decided to exclude 547 editions from the title, 164 from the abstract, and 109 from the actual content. As a result, six duplicate articles were excluded, and the total number of papers was 116. In addition to these, 12 articles have been added as a result of the pr e-search and 11 posts of the rear search. As a result, 139 papers were subject to reviews (Fig. 1). To investigate the AI controversy in organizational and business administration, Smacchia and ZA [55] searched for a computational search in their research. Through impact analysis and content analysis, they revealed what kind of publications and research fields had developed over time. DI VAIO et al. [56] investigates a group of literature on the application of AI in the development of sustainable business models, quantitatively seeing research groups in this field, and the explanation functional growth of AI and machine learning. Discussed the relevance of sustainable development. Finally, according to the cluster analysis of DHAMIJA and BAG [57], six clusters have emerged as an important issue for AI's current and future researchers: (1) and (2) engineering (2) engineering. / Research and automation, (3) Business performance and machine learning, (4) Sustainable supply chain and sustainable development, (5) Technology introduction and green supply chain management, (6) Internet.

The search is completed when the article is repeated with a combination of different keywords. A necessary number of papers were obtained to match the previous research. The main discoveries and contributions of each paper were compiled in Excel data extraction spreadsheets along with the explanatory elements, and were used to evaluate the final sample.

3.1.2. AI Capability Conceptualization

The novelty of this study is that it focuses on integrating AI with business strategies and IT strategies as an important means of realizing digital transformation alignment that improves the results of various organizational value. In this study, a systematic review of the literature was conducted using specific methods that have been widely used in the past to explore information system strategies and digital transformation issues. In the context of the responsible AI governance and the use of the tw o-way in the development of AI ability, specific issues, solutions, lever, and tide were examined.

This survey is developed in a scientific focus to present a more detailed image of what AI has, enabling digital transformations, and ultimately competitive. It demonstrates the important role of AI ability in creating the advantage of. In addition, the tissue is doubled to innovative AI and daily AI to clarify the eneven and drivers to pursue the improvement of business value by AI and have a synergistic impact on the strategic agility of the organization. It emphasizes the need to focus on strategic focus.

Furthermore, this study contributes to the understanding of how to optimize AI resources in order to promote strategic agility and benefit the strategic level of the organization, and to compose and develop dynamic capability. It contributes to the current knowledge system. Furthermore, research suggests that establishing digital skills that are difficult to compete can lead to difficult and changing business environments. In addition, AI is often monolithic, refuting the idea that it cannot adapt to changes in the environment, and emphasizes the importance of utilizing AI to support it, rather than hindering adaptive changes. There is. Finally, this study has paid attention to the importance of excellent AI governance and the direct impact of the organization's achievements. If the article is repeated with a combination of different keywords, the search is completed. A necessary number of papers were obtained to match the previous research. The main discoveries and contributions of each paper were compiled in Excel data extraction spreadsheets along with the explanatory elements, and were used to evaluate the final sample.

Furthermore, this study contributes to the understanding of how to optimize AI resources in order to promote strategic agility and benefit the strategic level of the organization, and to compose and develop dynamic capability. It contributes to the current knowledge system. Furthermore, research suggests that establishing digital skills that are difficult to compete can lead to difficult and changing business environments. In addition, AI is often monolithic, refuting the idea that it cannot adapt to changes in the environment, and emphasizes the importance of utilizing AI to support it, rather than hindering adaptive changes. There is. Finally, this study has paid attention to the importance of excellent AI governance and the direct impact of the organization's achievements. The search is completed when the article is repeated with a combination of different keywords. A necessary number of papers were obtained to match the previous research. The main discoveries and contributions of each paper were compiled in Excel data extraction spreadsheets along with the explanatory elements, and were used to evaluate the final sample.

3.1.3. AI Resources Orchestration

Applying the classification framework, 169 papers were surveyed. All articles were organized into two general concepts: 1. Capabilities of artificial intelligence, 2. Integration of AI and business/IT strategies for business value enhancement. Providing additional knowledge in the field and deepening the understanding of the strategic operation of AI, these concepts will help current and future scholars and researchers to develop the relevant body of research. These papers are presented in Table A1 (Appendix A).

Thinking, understanding, interpretation, learning, judging, reasoning, etc. are some of the many abilities that constitute intelligence. It is thanks to intelligence that humans are able to perform tasks such as learning from experience, generating responses to the various unknown challenges they face, and reacting as quickly as possible to new scenarios [50]. These actions constitute the core of the AI methodology. Artificial intelligence refers to a broad methodology that seeks to reproduce the intelligence of humans and other living organisms in a computational environment by generating solutions with equivalent patterns to solve problems. These methods, which have succeeded in solving NP (non-deterministic polynomial) Hard problems, i. e. problems that are extremely difficult to solve using traditional methods, are of greatest benefit in work and personal life.

Artificial intelligence is defined as "the ability of a system that uses the knowledge learned to achieve certain goals and tasks through flexible adaptation" [58] [58] [58] [58] [58]. Huh. Algorithms have already been able to digitally implement a function that effectively imitate human learning, deep judgment, and decisio n-making in a wide range of applied areas by improving data generation and computing capabilities. [9, 59]. Examples of such business applications include product design, collection of limited external knowledge, recruitment and selection of human resources, and optimization of internal operations [60, 61]. However, the classification of AI technology is not generally accepted. Most of the AI technologies are grouped under the headlines (FL), artificial neural networks (ANN), expert systems (ES), and meta hulestics (MH) supported by AI. Various types and applications are also presented [27, 50].

As a result of such progress, business scholars are increasingly interested in the possibility of AI supporting and changing companies. [62] However, the use of AI algorithms is currently subject to extensive research in various functional areas, including human resource management, marketing, information systems, finance [63], economics, operations management, and manufacturing. On the other hand, it is not very focused on understanding how the appearance of AI is changing the activities of the strategic, that is, how people involved in the strategic process are changing [64, 65, 66) [64, 65, 66. ]. Againstical intelligence is "the ability of a system that uses the knowledge learned to achieve a specific goal and task through flexible adaptation, which is interpreted correctly, learn from that data. [58] Is defined. Algorithms have already been able to digitally implement a function that effectively imitate human learning, deep judgment, and decisio n-making in a wide range of applied areas by improving data generation and computing capabilities. [9, 59]. Examples of such business applications include product design, collection of limited external knowledge, recruitment and selection of human resources, and optimization of internal operations [60, 61]. However, the classification of AI technology is not generally accepted. Most of the AI technologies are grouped under the headlines (FL), artificial neural networks (ANN), expert systems (ES), and meta hulestics (MH) supported by AI. Various types and applications are also presented [27, 50].

As a result of such progress, business scholars are increasingly interested in the possibility of AI supporting and changing companies. [62] However, the use of AI algorithms is currently subject to extensive research in various functional areas, including human resource management, marketing, information systems, finance [63], economics, operations management, and manufacturing. On the other hand, it is not very focused on understanding how the appearance of AI is changing the activities of the strategic, that is, how people involved in the strategic process are changing [64, 65, 66) [64, 65, 66. ]. Artificial intelligence is defined as "the ability of a system that uses the knowledge learned to achieve certain goals and tasks through flexible adaptation" [58] [58] [58] [58] [58]. Huh. Algorithms have already been able to digitally implement a function that effectively imitate human learning, deep judgment, and decisio n-making in a wide range of applied areas by improving data generation and computing capabilities. [9, 59]. Examples of such business applications include product design, collection of limited external knowledge, recruitment and selection of human resources, and optimization of internal operations [60, 61]. However, the classification of AI technology is not generally accepted. Most of the AI technologies are grouped under the headlines (FL), artificial neural networks (ANN), expert systems (ES), and meta hulestics (MH) supported by AI. Various types and applications are also presented [27, 50].

According to ambidexterity theory, an organization's strategic flexibility comes from placing equal strategic emphasis on the routine and innovative use of AI [17, 19, 67]. The expression "routine use of AI" describes how an organization uses AI on a daily basis. As a result, it is considered an exploitation and standardization posture that aims to enhance, expand, and promote incremental innovation in AI in the various products and services offered by the company [19, 20]. The routine use of AI often involves the standardization of work practices and operations that utilize resources already available to the company (e. g. tools, frameworks, algorithms, AI approaches) [68]. On the other hand, the creative, emergent, and inventive use of AI in employees' work processes is referred to as innovative application [69, 70]. This perspective on the application of AI therefore emphasizes finding innovative and creative ways to integrate AI into work processes [69] to provide entirely new or significantly improved customer services. Moreover, innovative applications of AI serve as a catalyst for exploring and identifying new areas where AI tools, applications, and uses can be applied. According to ambidexterity theory, an organization's strategic flexibility comes from placing equal strategic emphasis on the routine and innovative use of AI [17, 19, 67]. The expression "routine use of AI" describes how an organization uses AI on a daily basis. As a result, this is considered an exploitation and standardization posture that aims to enhance, expand, and promote incremental innovation in AI in the various products and services offered by the company [19, 20]. The routine use of AI often involves the standardization of work practices and operations that utilize resources already available to the company (e. g. tools, frameworks, algorithms, AI approaches) [68]. On the other hand, the creative, emergent, and inventive use of AI in employees' work processes is called innovative application [69, 70]. Thus, this perspective on the application of AI emphasizes finding innovative and creative ways to integrate AI into work processes [69], providing entirely new or significantly improved customer services. Moreover, innovative applications of AI serve as a catalyst for exploring and identifying new areas where AI tools, applications, and uses can be applied. According to ambidexterity theory, an organization's strategic flexibility comes from placing equal strategic emphasis on the routine and innovative use of AI [17, 19, 67]. The expression "routine use of AI" describes how an organization uses AI on a daily basis. As a result, this is seen as an attitude of exploitation and standardization that aims to enhance, expand, and promote incremental innovation in AI in the various products and services that the company offers [19, 20]. The routine use of AI often involves the standardization of work practices and operations that utilize resources already available to the company (e. g. tools, frameworks, algorithms, AI approaches, etc.) [68]. On the other hand, the creative, emergent, and inventive use of AI in employees' work processes is called innovative application [69, 70]. Thus, this perspective on the application of AI emphasizes finding innovative and creative ways to integrate AI into work processes [69], providing entirely new or significantly improved customer services. Moreover, innovative applications of AI serve as a catalyst for exploring and identifying new areas where AI tools, applications, and uses can be applied.

Due to the simultaneous adaptation of tw o-way AI or AI in these two modes, companies detect the environment by investigating rea l-time and large amounts of data, identifies and collects customer needs and trends, and finds patterns. [9, 61, 73] can be extracted for valuable data for the decision process. According to 13], the use of AI in business is to use AI to deal with business problems, find innovative solutions, succeed in business integration, and accelerate changes within the company. [23, 74] forms the dynamic ability of the organization by being used. Furthermore, the tw o-way is designed from a time separation perspective, suggesting that the organization should switch between the time zone of promoting routines and standardization, and the time zone that prioritizes creativity and exploration. [75]. Finally, the structurally divided vague is associated with the double organizational structure that focuses on search and use (along with related sisons, talent, and culture). [21]

Gaining value from both contradictory operations of AI and innovative utilization is not an easy process because it requires routines, abilities, and routines of different organizations. Rather, the literature suggests that big data and AI should be used as an important organizational resource to enhance the dynamic abilities of the organization and to fully demonstrate their strategic potential. [13, 76]. In addition, it is important to involve stakeholders in order to secure the full commitment of stakeholders, and all employees commit to new improvement projects, strategic direction and consistency of the entire organization. It is necessary to plan [77]. In order to achieve important and lon g-term economic benefits and powerful adaptive abilities, the organization needs to simultaneously consistent with "innovation" and "routine" [31, 63, 78]. Due to the simultaneous adaptation of AI in tw o-way AI or these two modes, companies detect the environment by investigating rea l-time and large amounts of data, identifies and collects customer needs and trends. [9, 61, 73] can be discovered and valuable data for the decisio n-making process. According to 13], the use of AI in business is to use AI to deal with business problems, find innovative solutions, succeed in business integration, and accelerate changes within the company. [23, 74] forms the dynamic ability of the organization by being used. Furthermore, the tw o-way is designed from a time separation perspective, suggesting that the organization should switch between the time zone of promoting routines and standardization, and the time zone that prioritizes creativity and exploration. [75]. Finally, the structurally divided vague is associated with the double organizational structure that focuses on search and use (along with related sisons, talent, and culture). [21]

Gaining value from both contradictory operations of AI and innovative utilization is not an easy process because it requires routines, abilities, and routines of different organizations. Rather, the literature suggests that big data and AI should be used as an important organizational resource to enhance the dynamic abilities of the organization and to fully demonstrate their strategic potential. [13, 76]. In addition, it is important to involve stakeholders in order to secure the full commitment of stakeholders, and all employees commit to new improvement projects, strategic direction and consistency of the entire organization. It is necessary to plan [77]. In order to achieve important and lon g-term economic benefits and powerful adaptive abilities, the organization needs to simultaneously consistent with "innovation" and "routine" [31, 63, 78]. Due to the simultaneous adaptation of tw o-way AI or AI in these two modes, companies detect the environment by investigating rea l-time and large amounts of data, identifies and collects customer needs and trends, and finds patterns. [9, 61, 73] can be extracted for valuable data for the decision process. According to 13], the use of AI in business is to use AI to deal with business problems, find innovative solutions, succeed in business integration, and accelerate changes within the company. [23, 74] forms the dynamic ability of the organization by being used. Furthermore, the tw o-way is designed from a time separation perspective, suggesting that the organization should switch between the time zone of promoting routines and standardization, and the time zone that prioritizes creativity and exploration. [75]. Finally, the structurally divided vague is associated with the double organizational structure that focuses on search and use (along with related sisons, talent, and culture). [21]

Regarding the impact of AI on business owners and business, recently, particularly interesting research [66, 79, 80] has been conducted. There are several early research on business models [81], A I-related organizations' decisions [9, 60, 61], and how companies promote AI's trust [82]. The literature on practica l-oriented management often describes the processes and actions on the supervision that the supervisor can take to introduce AI in the organization [6, 10, 42]. The meaning of AI is mostly agreed that in addition to the improvement of the process, it also fundamentally includes new corporate management and expansion methods. However, there is a shortage of research on how AI can be used to acquire new skills and transition to business models that utilize AI.

In fact, there is a survey that the largest barrier of AI introduction is lack of AI capacity. [6] Most scholars agree to the importance of AI ability, but each literature does not provide a clear definition of AI ability. For example, blocks and vongenheim's management guidelines [6] have several abilities related to the goal of integrating AI into business. However, these scholars argued that in addition to data science and technical talent, AI integration requires strategic abilities, and has a fairly wide stance. As a result, the depth of insights of these publications is limited, especially considering that these skills were not the only or major target. Researchers and practitioners have not yet clarified the skills needed for companies to make full use of AI and the basics process [33, 83, 84]. The repetition of digital capability like AI is "to use the knowledge of organized knowledge to continue optimizing the value obtained from digital technology, rather than technical issues. Is an issue. "[59] AI has recently been conducting a particularly interesting research [66, 79, 80] on the impact of business owners and businesses. There are several early research on business models [81], A I-related organizations' decisions [9, 60, 61], and how companies promote AI's trust [82]. The literature on practica l-oriented management often describes the processes and actions on the supervision that the supervisor can take to introduce AI in the organization [6, 10, 42]. The meaning of AI is mostly agreed that in addition to the improvement of the process, it also fundamentally includes new corporate management and expansion methods. However, there is a shortage of research on how AI can be used to acquire new skills and transition to business models that utilize AI.

In fact, there is a survey that the largest barrier of AI introduction is lack of AI capacity. [6] Most scholars agree to the importance of AI ability, but each literature does not provide a clear definition of AI ability. For example, blocks and vongenheim's management guidelines [6] have several abilities related to the goal of integrating AI into business. However, these scholars argued that in addition to data science and technical talent, AI integration requires strategic abilities, and has a fairly wide stance. As a result, the depth of insights of these publications is limited, especially considering that these skills were not the only or major target. Researchers and practitioners have not yet clarified the skills needed for companies to make full use of AI and the basics process [33, 83, 84]. The repetition of digital capability like AI is "to use the knowledge of organized knowledge to continue optimizing the value obtained from digital technology, rather than technical issues. Is an issue. "[59] Regarding the impact of AI on business owners and business, recently, particularly interesting research [66, 79, 80] has been conducted. There are several early research on business models [81], A I-related organizations' decisions [9, 60, 61], and how companies promote AI's trust [82]. The literature on practica l-oriented management often describes the processes and actions on the supervision that the supervisor can take to introduce AI in the organization [6, 10, 42]. The meaning of AI is mostly agreed that in addition to the improvement of the process, it also fundamentally includes new corporate management and expansion methods. However, there is a shortage of research on how AI can be used to acquire new skills and transition to business models that utilize AI.

3.1.4. AI Governance

Since AI is a new technology, a larger research research is being conducted to determine the value of AI and its abilities to the business. The main factors that affect the adoption of machine learning and the support of top management have been revealed by REIS and others in [85]. Wamba-taguimdje et al. [16] investigated the relevance between AI and the company based on the mini-case study at the service provider. [16] investigated the relevance of AI resource and business value. Alsheibani et al. [86] examined issues over the creation of AI by thoroughly verifying the actual AI use case and pr e-research groups. They have identified the six organizational barriers related to AI, including AI business cases, relative benefits of AI, support for top management, effective use of data, AI human resources, and AI compatibility. In order to identify the mechanism that creates the business value of AI, Enholm et al. [51] summarizes existing literature and reviewed. They identified many AI use, realization, barriers, and the effects of AI, especially competitiveness. As shown in previous studies, the introduction of AI technology alone is not enough to improve the performance of an organization in the evolving digital society, and it is necessary to introduce a complementary AI technology. AI is a new technology, so a larger research is being conducted to determine the value of AI and its abilities to the business. The main factors that affect the adoption of machine learning and the support of top management have been revealed by REIS and others in [85]. Wamba-taguimdje et al. [16] investigated the relevance between AI and the company based on the mini-case study at the service provider. [16] investigated the relevance of AI resource and business value. Alsheibani et al. [86] examined issues over the creation of AI by thoroughly verifying the actual AI use case and pr e-research groups. They have identified the six organizational barriers related to AI, including AI business cases, relative benefits of AI, support for top management, effective use of data, AI human resources, and AI compatibility. In order to identify the mechanism that creates the business value of AI, Enholm et al. [51] summarizes existing literature and reviewed. They identified many AI use, realization, barriers, and the effects of AI, especially competitiveness. As shown in previous studies, the introduction of AI technology alone is not enough to improve the performance of an organization in the evolving digital society, and it is necessary to introduce a complementary AI technology. Since AI is a new technology, a larger research research is being conducted to determine the value of AI and its abilities to the business. The main factors that affect the adoption of machine learning and the support of top management have been revealed by REIS and others in [85]. Wamba-taguimdje et al. [16] investigated the relevance between AI and the company based on the mini-case study at the service provider. [16] investigated the relevance of AI resource and business value. Alsheibani et al. [86] examined issues over the creation of AI by thoroughly verifying the actual AI use case and pr e-research groups. They have identified the six organizational barriers related to AI, including AI business cases, relative benefits of AI, support for top management, effective use of data, AI human resources, and AI compatibility. In order to identify the mechanism that creates the business value of AI, Enholm et al. [51] summarizes existing literature and reviewed. They identified many cases of AI use, realization, barriers, and also the influence of AI, especially competitiveness. As shown in previous studies, the introduction of AI technology alone is not enough to improve the performance of an organization in the evolving digital society, and it is necessary to introduce a complementary AI technology.

Governance Mechanisms

In addition, the concept of AI Caucanity is that in advance research, from the focus of AI to simple technical resources, all associated organizational resources that are essential to fully realize the strategic potential of AI. I tried to spread it as if it was included [42, 45, 50]. According to research on AI's abilities, it is necessary to have some major resources in order for companies to adopt AI performance and to enjoy their benefits [14, 16, 74]. 。 The organization can integrate certain AI resources such as AI algorithms and training data, thanks to the AI capacity that enables value creation. Sjödin et al. [87] is an analysis of case studies, data pipeline, algorithm development, and democratization of AI to examine how manufacturing companies create AI abilities. I found an ability set. Through a thorough research research, Mikalef and GUPTA [14] concluded that companies can improve their organization's innovation and performance by developing AI capabilities linked to human resources, formal resources, and intangible resources. To explore additional or other important aspects, we created a list of AI resources that led to AI capabilities. Through explorable interviews, The concept of AI Capability is essential to fully realize the strategic potential of AI, from the focus on simple technical resources, in advance research. [42, 45, 50] has tried to spread the related organizational resources [42, 45, 50]. According to research on AI's abilities, it is necessary to have some major resources in order for companies to adopt AI performance and to enjoy their benefits [14, 16, 74]. 。 The organization can integrate certain AI resources such as AI algorithms and training data, thanks to the AI capacity that enables value creation. Sjödin et al. [87] is an analysis of case studies, data pipeline, algorithm development, and democratization of AI to examine how manufacturing companies create AI abilities. I found an ability set. Through a thorough research research, Mikalef and GUPTA [14] concluded that companies can improve their organization's innovation and performance by developing AI capabilities linked to human resources, formal resources, and intangible resources. To explore additional or other important aspects, we created a list of AI resources that led to AI capabilities. Furthermore, through explorable interviews, the concept of AI Capability is all indispensable to fully realize the strategic possibilities of AI from the focus on simple technical resources in advance research. [42, 45, 50] has been trying to spread it to include organized resources. According to research on AI's abilities, it is necessary to have some major resources in order for companies to adopt AI performance and to enjoy their benefits [14, 16, 74]. 。 The organization can integrate certain AI resources such as AI algorithms and training data, thanks to the AI capacity that enables value creation. Sjödin et al. [87] is an analysis of case studies, data pipeline, algorithm development, and democratization of AI to examine how manufacturing companies create AI abilities. I found an ability set. Through a thorough research research, Mikalef and GUPTA [14] concluded that companies can improve their organization's innovation and performance by developing AI capabilities linked to human resources, formal resources, and intangible resources. To explore additional or other important aspects, we created a list of AI resources that led to AI capabilities. Through explorable interviews

Structural Mechanisms

Despite the existing literature that discusses the structure of AI capabilities and the correlation between AI and business value, there is little knowledge on how AI resources and AI capabilities enable business value for organizations [70]. As a result, exploratory research studies adopting an interpretive approach are needed to clarify the role of AI resources (the combination of AI elements and AI capabilities) in the development of business value and to delve deeper into the topic of AI capabilities. Despite the great potential of AI technology, Brynjolfsson et al. [6] highlight that we are dealing with a modern productivity paradox. According to the authors, one of the main reasons why AI has not yet delivered the expected results is due to delays in adoption and reengineering. Therefore, organizations need to invest in additional resources to be able to leverage their AI investments. To achieve performance gains from AI, it is essential to understand which complementary resources need to be deployed and how to deploy them. In other words, it is time for organizations to consider how to build AI capabilities. As mentioned above, the study by Mikalef and Gupta [14] seeks to investigate the resources required to develop AI capabilities based on the resource-based theory (RBT) of organizations. RBT is a theoretical lens particularly suited to dynamic and turbulent environments where resource complementarity is encouraged and organizations create unique capabilities around their respective resources. According to Melville et al. [89], RBT allows researchers to generate empirically tested hypotheses, the evaluation of which can advance knowledge about the importance of various IT resources and how they affect organizational performance [43, 90]. According to Wade and Hulland [91], RBT provides a robust framework for evaluating the strategic value of information system resources. The fact that RBT is a widely recognized theory in other business disciplines, such as operations management and marketing and supply chain management [92, 93, 94], demonstrates its value in describing organizational-level phenomena. As a result, Mikalef and Gupta [14] developed the concept of AI capabilities by drawing on current research on AI in organizational settings and previous research on IT capabilities.

Many types of resources necessary to develop performanc e-driven organizational abilities are described in several research [95]. One of the most popular classifications is the GRANT [14] presented, and in formal resources (physical resources, financial resources, etc.), human resources (employee expertise and skills), and inadvertent resources (customers). It distinguishes knowledge and skills) (synergies, adjustments, strategic orientation, etc.). In the IS literature, this resource segment is roughly divided into material resources, human resources, and intangible resources. In order to make effective use of investment and create economic value, companies need to create a dedicated resource set, as in any new technology, including AI.

Procedural mechanisms

The research we have quoted so far, many other academic papers, and business reports show the diversity of resources that companies need to develop to realize returns to AI investment. However, there is a lack of reasonable research on how companies develop AI abilities. [14] This is for both research and practice because it points out the main fields that companies should focus on when implementing AI projects and giving the basics of potential business value and various production mechanisms. An important gap. RBT's theoretical foundation [93, 94], empirical research that applies RBT to the IS area [91, 97], recent research that emphasizes issues related to recruitment and value creation [5, 33, 98] FIG. 3 shows the classification of each category and the main resource type. Many types of resources needed to develop the organizational ability to perform performance are described in several research [95]. One of the most popular classifications is the GRANT [14] presented, and in formal resources (physical resources, financial resources, etc.), human resources (employee expertise and skills), and inadvertent resources (customers). It distinguishes knowledge and skills) (synergies, adjustments, strategic orientation, etc.). In the IS literature, this resource segment is roughly divided into material resources, human resources, and intangible resources. In order to make effective use of investment and create economic value, companies need to create a dedicated resource set, as in any new technology, including AI.

The research we have quoted so far, many other academic papers, and business reports show the diversity of resources that companies need to develop to realize returns to AI investment. However, there is a lack of reasonable research on how companies develop AI abilities. [14] This is for both research and practice because it points out the main fields that companies should focus on when implementing AI projects and giving the basics of potential business value and various production mechanisms. An important gap. RBT's theoretical foundation [93, 94], empirical research that applies RBT to the IS area [91, 97], recent research that emphasizes issues related to recruitment and value creation [5, 33, 98] FIG. 3 shows the classification of each category and the main resource type. Many types of resources necessary to develop performanc e-driven organizational abilities are described in several research [95]. One of the most popular classifications is the GRANT [14] presented, and in formal resources (physical resources, financial resources, etc.), human resources (employee expertise and skills), and inadvertent resources (customers). It distinguishes knowledge and skills) (synergies, adjustments, strategic orientation, etc.). In the IS literature, this resource segment is roughly divided into material resources, human resources, and intangible resources. In order to make effective use of investment and create economic value, companies need to create a dedicated resource set, as in any new technology, including AI.

To more, data, technology, and main resources are examples of tangible resources and are defined as those that can be traded or purchased in the public market. For example, there are several categories, such as financial assets, debt and stocks, such as debt and shares. Than resources are unlikely to provide a clear competitive advantage because all companies in the market are accessible. However, although a formal resource is required, it is not enough to build abilities in itself. Regarding the second group, the human capital of the organization is often evaluated by evaluating the staff knowledge, skills, experience, leadership, vision, communication, collaboration, problem solving. In a pr e-study on digital capability [93, 97], technical skills and business skills are the pillars of human resources that are important. This study argues that the technical and business skills specializing in AI are two important factors of corporate AI human resources. Compared to the third category, in unstable and unstable market, intangible resources, which are difficult to replicate, are emphasized. Intangible resources are more uncertain and more detailed data, technology, and main resources are examples of tangible resources and are defined as trading or purchasing in the public market. For example, there are several categories, such as financial assets, debt and stocks, such as debt and shares. Than resources are unlikely to provide a clear competitive advantage because all companies in the market are accessible. However, although a formal resource is required, it is not enough to build abilities in itself. Regarding the second group, the human capital of the organization is often evaluated by evaluating the staff knowledge, skills, experience, leadership, vision, communication, collaboration, problem solving. In a pr e-study on digital capability [93, 97], technical skills and business skills are the pillars of human resources that are important. This study argues that the technical and business skills specializing in AI are two important factors of corporate AI human resources. Compared to the third category, in unstable and unstable market, intangible resources, which are difficult to replicate, are emphasized. Intangible resources are more uncertain, more detailed, and is defined as a test that can be traded or purchased in the public market, an example of a tangible resource. For example, there are several categories, such as financial assets, debt and stocks, such as debt and shares. Than resources are unlikely to provide a clear competitive advantage because all companies in the market are accessible. However, although a formal resource is required, it is not enough to build abilities in itself. Regarding the second group, the human capital of the organization is often evaluated by evaluating the staff knowledge, skills, experience, leadership, vision, communication, collaboration, problem solving. In a pr e-study on digital capability [93, 97], technical skills and business skills are the pillars of human resources that are important. This study argues that the technical and business skills specializing in AI are two important factors of corporate AI human resources. Compared to the third category, in unstable and unstable market, intangible resources, which are difficult to replicate, are emphasized. Intangible resources are more uncertain

The identification of the main resources for building RBT and ability is an important perspective for practice [101]. And you can identify the potential gap that can be solved by intensive efforts.

Resource orchestration is an integrated technique for organizing corporate resources, pooling the current resources to create new abilities, and utilizing them to enhance both business and customer value. 102]. Structure, integration, and leverage are three important components, and as shown in FIG. 3, each component contains three subprocesses. [103] The environment causes uncertainty, leading to the unstable of the organization, which can impair competitive advantage to rivals, so that it affects resource cooperation that must be taken seriously.

Relational Mechanisms

How companies configure resource portfolios will affect the tangible and intangible resources held by the company, and determine the potential value that can be created at any time. Data, technical infrastructure, people's skills, expertise, and other intangible resources, such as learning culture and independence, are examples of such resources as described above. This process is extremely important because companies provide resources needed to develop new abilities. In other words, in the context of AI, the types of A I-based abilities (NLP tools, intellectual assistants, predictions, etc.) that companies that companies can develop are greatly dependent on widely available data, infrastructure, abilities, and other related resources. [14].

AI acquisitions in the context meant to hire staff with deep expertise in AI systems, or to purchase necessary technical infrastructure, such as clusters with extremely high calculation capabilities. [103] This activity is indispensable for companies that need to develop and strengthen skills, especially if they cannot be outsourced. Although it is expensive, the decision of the acquisition can accelerate the budget management of companies in highly uncertain environments [104]. Managers must use new resources wisely by developing new measures based on the market perspective. For example, investing in an expensive data set to complement existing data sources may not be possible to obtain as expected results, so it may be found that it is a hig h-risk behavior. be. 特特特特とととと [と [[[[[[[[[[[[[[[[[[[[[[[[ And you can identify the potential gap that can be solved by intensive efforts.

AI acquisitions in the context meant to hire staff with deep expertise in AI systems, or to purchase necessary technical infrastructure, such as clusters with extremely high calculation capabilities. [103] This activity is indispensable for companies that need to develop and strengthen skills, especially if they cannot be outsourced. Although it is expensive, the decision of the acquisition can accelerate the budget management of companies in highly uncertain environments [104]. Managers must use new resources wisely by developing new measures based on the market perspective. For example, investing in an expensive data set to complement existing data sources may not be possible to obtain as expected results, so it may be found that it is a hig h-risk behavior. be. The identification of the main resources for building RBT and ability is an important perspective for practice [101]. And you can identify the potential gap that can be solved by intensive efforts.

3.2. The Integration of AI and Business/IT towards Digital Transformation Alignment for Enhanced Business Value Outcomes

3.2.1. The Road to Strategic Flexibility

Anticipating Drivers of Change and Future Options

Formulate and Design Strategies

The creation of internal resources is accumulation. Accumulation is important when a company lacks the necessary resources and cannot adapt to changes in the market environment [105]. Otherwise, it will be very difficult to fill the time gap and make a profit when the environment becomes uncertain, such as when new opportunities arise or valuable employees leave the company [102]. For example, knowledge of AI system development and AI project management must be accumulated in a sufficient number of employees to increase tacit knowledge. As a result, the work experience, development information, and general expertise that are lacking in those who leave the company will not be included in the company's knowledge, which may create a gap in the production of knowledge necessary for future growth [43]. The ability to collect all relevant data internally should be prioritized in such efforts compared to obtaining it from market factors.

Assemble and Develop Capabilities

The release of resources that a company manages is called a "divestment." Resources are scarce in business, so it is essential to free up resources for other tasks. In order to develop AI capabilities, it is essential to sell out obsolete systems and infrastructure, such as code written in less-preferred machine learning languages such as Python and R, and computer clusters with insufficient processing power [10]. Moreover, maintaining large amounts of low-value data, such as data typically used for forecasting and model building, simply complicates things and uses storage space for technical staff [13, 76]. However, it is essential to carefully calculate all allocated resources, since it is difficult to reallocate or accumulate these resources under unpredictable circumstances. This applies to staff with specific knowledge and skills, as well as data and technology resources.

3.2.2. The Role of AI in Strategic Components

AI in Strategic Analysis

Combining resources to create capabilities is the process of resource engagement. Having resources alone does not create an advantage. This refers to knowing the application areas that an AI initiative can focus on in the context of its utilization. Three sub-processes make up the engagement process: stabilization, enrichment, and exploitation.

What is provided by stabilization is a slight improvement in the current ability. However, it helps to create value and enable companies to maintain competitive advantage over the long term. In the case of AI development, this is to teach software developers about machine learning, and to teach analysts how to use interactive interfaces with AI. [15] However, in a highly unpredictable environment, stabilization may not be a good option, as drastic and urgent adjustments may be required.

Enrichment is to expand existing abilities. This can be achieved by expanding resources, developing new skills, and forming a new partnership with companies that can access the necessary resources. Companies that use the AI application require data accessible by other companies to develop AI models, or individuals who are familiar with A I-specific problems. [7] The Alliance can provide important skills to achieve competitive advantage without great risk, which is beneficial to both sides. However, other companies may use the same tactics, so competitive advantage may be instantaneous and only faint.

Innovative is a process that incorporates brand new materials from the market and work on exploring learning. This may include a branch process that combines heterogeneous and unrelated information. [4] In terms of AI applications, it may include the use of data sources that are completely different from those owned or managed by companies, and hire employees with characteristic and completely different skill sets. It may include [10]. The ability to provide value proposals that surpass rivals in ambiguous environments contributes to competitive advantage. Innovation is an important process to create such value proposals.

Leverage means mobilizing, coordinating, and developing processes to increase customer value and benefit tissues. [107, 108]. A typical AI ability to create an organizational idea begins with the ability to define the digital business strategy and complete the process. Subsced to stabilization is a slight improvement in the current ability. However, it helps to create value and enable companies to maintain competitive advantage over the long term. In the case of AI development, this is to teach software developers about machine learning, and to teach analysts how to use interactive interfaces with AI. [15] However, in a highly unpredictable environment, stabilization may not be a good option, as drastic and urgent adjustments may be required.

Leverage means mobilizing, coordinating, and developing processes to increase customer value and benefit tissues. [107, 108]. A typical AI ability to create an organizational idea begins with the ability to define the digital business strategy and complete the process. What is provided by stabilization is a slight improvement in the current ability. However, it helps to create value and enable companies to maintain competitive advantage over the long term. In the case of AI development, this is to teach software developers about machine learning, and to teach analysts how to use interactive interfaces with AI. [15] However, in a highly unpredictable environment, stabilization may not be a good option, as drastic and urgent adjustments may be required.

Innovative is a process that incorporates brand new materials from the market and work on exploring learning. This may include a branch process that combines heterogeneous and unrelated information. [4] In terms of AI applications, it may include the use of data sources that are completely different from those owned by or managed by companies, and employees with a distinctive and completely different skill set. It may include [10]. The ability to provide value proposals that surpass rivals in ambiguous environments contributes to competitive advantage. Innovation is an important process to create such value proposals.

AI in Strategy Formulation and Implementation

The purpose is to identify all the abilities necessary for companies to gain competitive advantage. This is not a simple process if the business environment is very unclear. Companies that are interested in AI technology must decide which internal gap and which external opportunities to use [74, 109]. In other words, it is essential to carefully evaluate the opportunity for the target environment before starting the development of AI applications. For this reason, advanced managers need to acquire technical expertise in strategically predict development and can understand how AI technology can be applied creatively in order to deal with imminent issues [. 3, 45]. However, in order to introduce AI, it is necessary to continuously change the business process, and it is difficult to maintain it for a long time.

The integration of assets mobilized to create the ability configuration is a adjustment process. Hig h-level managers are indispensable to adjust the individual skills and knowledge of the team and enable quick, efficient and seamless integration. In other words, in the context of the introduction of AI, the developed AI ability must be well adjusted and integrated with other organizational abilities to effectively execute the leverage strategy and create value. For example, the construction of an AI pipeline that enables effective processing between data and model requires a lot of effort. In other words, in order to integrate AI capabilities that enable business value with other abilities, an open communication line and collaboration that spans different organizational units are required [14]. Managers play an important role in using their personal connection network to perform arrangements that combine different abilities to create added value. The purpose is to identify all the abilities necessary for companies to gain competitive advantage. This is not a simple process if the business environment is very unclear. Companies that are interested in AI technology must decide which internal gap and which external opportunities to use [74, 109]. In other words, it is essential to carefully evaluate the opportunity for the target environment before starting the development of AI applications. For this reason, advanced managers need to acquire technical expertise in strategically predict development and can understand how AI technology can be applied creatively in order to deal with imminent issues [. 3, 45]. However, in order to introduce AI, it is necessary to continuously change the business process, and it is difficult to maintain it for a long time.

The integration of assets mobilized to create the ability configuration is a adjustment process. Hig h-level managers are indispensable to adjust the individual skills and knowledge of the team and enable quick, efficient and seamless integration. In other words, in the context of the introduction of AI, the developed AI ability must be well adjusted and integrated with other organizational abilities to effectively execute the leverage strategy and create value. For example, the construction of an AI pipeline that enables effective processing between data and model requires a lot of effort. In other words, in order to integrate AI capabilities that enable business value with other abilities, an open communication line and collaboration that spans different organizational units are required [14]. Managers play an important role in using their personal connection network to perform arrangements that combine different abilities to create added value. The purpose is to identify all the abilities necessary for companies to gain competitive advantage. This is not a simple process if the business environment is very unclear. Companies that are interested in AI technology must decide which internal gap and which external opportunities to use [74, 109]. In other words, it is essential to carefully evaluate the opportunity for the target environment before starting the development of AI applications. For this reason, advanced managers need to acquire technical expertise in strategically predict development and can understand how AI technology can be applied creatively in order to deal with imminent issues [. 3, 45]. However, in order to introduce AI, it is necessary to continuously change the business process, and it is difficult to maintain it for a long time.

AI and Corporate Strategy

The development process needs to actively support the leverage strategy. For growth, it is necessary to integrate machine learning models into the current production system so that business decisions can be made based on data such as resource advantage, market opportunities, and business strategies. , 111]. To support the selected leverage strategy, you need to actually use the capacity configuration. Selecting A I-oriented strategies and developing solutions in accordance with it can result in different consequences depending on the environmental context developed in the context of AI deployment [84, 112]. For this reason, there are a wide variety of ways to use AI in organizations, and AI CaPability managed by companies can be used in cooperation with other capability to pursue a digital business strategy. & amp; lt; Pan & amp; gt; Development process needs to actively support the use strategy. In the development, it is necessary to integrate the machine learning model into the current production system so that business decisions can be made based on data such as resource advantage, market opportunity, business strategy, etc. [107, [107, [107. 111]. In order to support the selected leverage strategy, it is necessary to actually use the capacity configuration. The strategy choice is based on the use of leverage strategies. The Development process needs to actively support the leverage strategy. For growth, it is necessary to integrate machine learning models into the current production system so that business decisions can be made based on data such as resource advantage, market opportunities, and business strategies. , 111]. To support the selected leverage strategy, you need to actually use the capacity configuration. Selecting A I-oriented strategies and developing solutions in accordance with it can result in different consequences depending on the environmental context developed in the context of AI deployment [84, 112]. For this reason, there are a wide variety of ways to use AI in organizations, and AI CaPability managed by companies can be used in cooperation with other capability to pursue a digital business strategy. & amp; lt; Pan & amp; gt; Development process needs to actively support the use strategy. In the development, it is necessary to integrate the machine learning model into the current production system so that business decisions can be made based on data such as resource advantage, market opportunity, business strategy, etc. [107, [107, [107. 111]. In order to support the selected leverage strategy, it is necessary to actually use the capacity configuration. The strategy choice is based on the use of leverage strategies. The development process needs to actively support the leverage strategy. For growth, it is necessary to integrate machine learning models into the current production system so that business decisions can be made based on data such as resource advantage, market opportunities, and business strategies. , 111]. To support the selected leverage strategy, you need to actually use the capacity configuration. Selecting A I-oriented strategies and developing solutions in accordance with it can result in different consequences depending on the environmental context developed in the context of AI deployment [84, 112]. For this reason, there are a wide variety of ways to use AI in organizations, and AI CaPability managed by companies can be used in cooperation with other capability to pursue a digital business strategy. & amp; lt; Pan & amp; gt; Development process needs to actively support the use strategy. In the development, it is necessary to integrate the machine learning model into the current production system so that business decisions can be made based on data such as resource advantage, market opportunity, business strategy, etc. [107, [107, [107. 111]. In order to support the selected leverage strategy, it is necessary to actually use the capacity configuration. The strategy choice is based on the use of leverage strategies.

The AI technology required to support the project is expected to evolve extremely rapidly, but it is also essential to focus on other organizational resources that should be promoted in addition to technology. The development of AI ability in a particular business requires these complementary organizational resources. As a result, the organization's ability to AI can be defined as the ability to select, adjust, and utilize A I-specific resources. Ransbotham et al. [5] indicates an example of the complementary organizational resources required to acquire business value through AI investment. While DaVenport and Ronanki [33] point out that more than on e-third of managers of the investigated companies do not understand AI technology and their mechanisms, the writers mentioned above have 1 major barriers to value creation. It is observing that it is absent with leadership that promotes AI. As mentioned above, the importance of such complementary resources is emphasized in many practical research research. For example, FOUNTAINE et al. [113] emphasizes the value of across collaboration in sector and creating a team with a variety of views and expertise. The organization can consider the AI project to consider the proposal of the entire organization. AI technology required to support the project is expected to evolve extremely rapidly, but it is also essential to focus on other organizational resources that should be promoted in addition to technology. The development of AI ability in a particular business requires these complementary organizational resources. As a result, the organization's ability to AI can be defined as the ability to select, adjust, and utilize A I-specific resources. Ransbotham et al. [5] indicates an example of the complementary organizational resources required to acquire business value through AI investment. While DaVenport and Ronanki [33] point out that more than on e-third of managers of the investigated companies do not understand AI technology and their mechanisms, the writers mentioned above have 1 major barriers to value creation. It is observing that it is absent with leadership that promotes AI. As mentioned above, the importance of such complementary resources is emphasized in many practical research research. For example, FOUNTAINE et al. [113] emphasizes the value of across collaboration in sector and creating a team with a variety of views and expertise. The organization can consider the AI project to consider the proposal of the entire organization. The AI technology required to support the project is expected to evolve extremely rapidly, but it is also essential to focus on other organizational resources that should be promoted in addition to technology. The development of AI ability in a particular business requires these complementary organizational resources. As a result, the organization's ability to AI can be defined as the ability to select, adjust, and utilize A I-specific resources. Ransbotham et al. [5] indicates an example of the complementary organizational resources required to acquire business value through AI investment. While DaVenport and Ronanki [33] point out that more than on e-third of managers of the investigated companies do not understand AI technology and their mechanisms, the writers mentioned above have 1 major barriers to value creation. It is observing that it is absent with leadership that promotes AI. As mentioned above, the importance of such complementary resources is emphasized in many practical research research. For example, Fountaine et al. [113] emphasizes the value of across sector collaboration and creating a team with a variety of views and expertise. The organization can consider the AI project to consider the proposal of the entire organization.

Despite the lack of a precise definition, there is a growing consensus on responsible governance of AI.[115] It can be described as a function that details various ethical rules that apply to AI. It can also be described as a process that covers all stages of the life cycle of an AI project, following the principles of responsible use.[35] In particular, it is crucial to consider how responsible AI governance impacts a company's ability to adopt changes in business products, processes, and services, as well as benchmarking against improved competitive performance.[44] Microsoft, for example, created explanatory tools to understand machine learning models that support decision-making.[112, 116] Thus, evidence is accumulating to support the assertion that responsible governance of AI impacts an organization's internal knowledge management skills.[30] Also, how an organization is perceived by external entities when using AI.[35] AI is impacting more aspects of society, such as marketing, healthcare, and human rights. It would be ill-advised to allow the development of AI applications without oversight.[39] It is therefore important to promote AI that is law-abiding, ethical, affirms ethical principles and values, and is socially and technically trustworthy. Different perspectives will lead to different views of government AI initiatives. The European Commission (EC) and Singaporean authorities view AI governance from a trustworthy stance where solutions are humane, while Microsoft researchers view AI governance from a technical perspective.[115] Figure 4 shows the EC's seven principles for ethical AI.

AI in Strategic Innovation, Entrepreneurship, and Renewal Models

A variety of mechanisms are available for enterprises to govern AI.[52] These include formal frameworks that connect business, data, model, and machine learning functions; formal processes and procedures for decision-making and monitoring; and practices that foster stakeholder engagement and collaboration.[34] We follow the IT governance literature[115] to categorize governance approaches into three categories: structural, procedural, and relational.

[117] The report framework, governing aircraft R, and accountability are defined by structural governance procedures. These are composed of roles and tasks, as well as distribution of decisio n-making authority. There are many research on data governance [32], but there are not many research on AI governance [106, 113, 118].

[119] is involved in many different fields in the development of AI systems. In order to manage the complex interaction between model output, training data, regulations, and business needs, it is necessary to build an AI Governance Council proposed for AI [106] in healthcare. There may be. Executive sponsors are also essential for this process. Depending on the recruiting level of the company, the authority of the executive sponsor and the decisio n-making level may be different. For example, at least in the early stages, A I-only budget without business requirements will be useful for adoption. Studies on more accurate roles related to data models are currently underway.

AI in Strategy Control

The purpose of the procedural governance mechanism is that AI systems and ML models operate efficiently and accurately, all laws, regulations, and internal, which are used for explanations, fairness, accountability, safety, and security. It is to guarantee that it works in accordance with rules and policies. [119] Data characteristics, models, syste m-related, and syste m-related guarantees are another purpose of procedural methods. Both the (1) strategy, (2) policy, (3) standard, (4) procedure and process agreement, (6) performance evaluation, (7) compliance monitoring, (7), (7) (7) 8) Includes data, model, and system version control. Report framework, rule, and accountability are defined by structural governance procedures. These are composed of roles and tasks, as well as distribution of decisio n-making authority. There are many research on data governance [32], but there are not many research on AI governance [106, 113, 118].

3.2.3. AI Business Value Drivers and Enhanced Outcomes

The purpose of the procedural governance mechanism is that AI systems and ML models operate efficiently and accurately, all laws, regulations, and internal, which are used for explanations, fairness, accountability, safety, and security. It is to guarantee that it works in accordance with rules and policies. [119] Data characteristics, models, syste m-related, and syste m-related guarantees are another purpose of procedural methods. Both the (1) strategy, (2) policy, (3) standard, (4) procedure and process agreement, (6) performance evaluation, (7) compliance monitoring, (7), (7) (7) 8) Includes data, model, and system version control. Report frameworks, governance agencies, and accountability are defined by structural governance procedures. These are composed of roles and tasks, as well as distribution of decisio n-making authority. There are many research on data governance [32], but there are not many research on AI governance [106, 113, 118].

Based on strategic organizational goals, a strategy provides high-level guidelines for action. Keding's work [27] focuses on where AI and strategic management interact, with particular emphasis on two aspects: (a) antecedents such as data-driven workflows (data value and data quality), managerial willingness, and organizational determinants (AI strategy and implementation), and (b) the implications of AI on strategic management at individual and organizational levels (e. g., human collaboration, AI, AI in business models) [119]. This project focuses on the use of AI for business information-related decision-making in organizations. Several components related to AI strategy are currently being addressed, including conceptual guidance from academics and practitioners [118, 120, 121].

High-level standards and rules are provided by AI policies. Key objectives, accountabilities, roles, and tasks are communicated from organizations through AI policies. Policymakers are actively discussing AI policies. For example, the European Union (EU) has presented policy options on how to promote the use of AI while minimizing risks, some of which are already included in the 2021 European Commission proposal [115]. Best practices can be a source of policies at the enterprise level [122]. Measurement of public (AI) policies is a hot topic that is also the subject of ongoing discussion and research. For example, Mishra et al. [31] address the measurement of the social and economic impact of AI and the risks and threats posed by AI systems. While there has been great progress in the development of data standards, there has been less progress in the development of AI standards [119]. Examples include Welfare Indicators for Ethical AI (IEEE P7010), Accuracy Benchmarks for Facial Recognition Systems (IEEE P7013), Design for Impairment in AI Systems (IEEE P7009), and Ethically Driven AI Methodologies (IEEE P7008). Based on strategic organizational objectives, the strategy provides high-level guidelines for action. Keding’s work [27] focuses on where AI and strategic management interact, with particular emphasis on two aspects: (a) antecedents such as data-driven workflows (data value and data quality), managerial willingness, and organizational determinants (AI strategy and implementation), and (b) the implications of AI for strategic management at the individual and organizational levels (e. g., human collaboration, AI, AI in business models) [119]. This project focuses on the use of AI for business information-related decision-making in organizations. Several components related to AI strategy are currently being addressed, including conceptual guidance from academics and practitioners [118, 120, 121].

4. Discussion

High-level standards and rules are provided by AI policies. Key objectives, accountabilities, roles, and tasks are communicated from organizations through AI policies. Policymakers are actively discussing AI policies. For example, the European Union (EU) has presented policy options on how to promote the use of AI while minimizing risks, some of which are already included in the 2021 European Commission proposal [115]. Best practices can be a source of policies at the enterprise level [122]. Measurement of public (AI) policies is a hot topic that is also the subject of ongoing discussion and research. For example, Mishra et al. [31] address the measurement of the social and economic impact of AI and the risks and threats posed by AI systems. While there has been great progress in the development of data standards, there has been less progress in the development of AI standards [119]. Examples include Welfare Indicators for Ethical AI (IEEE P7010), Accuracy Benchmarks for Facial Recognition Systems (IEEE P7013), Design for Impairment in AI Systems (IEEE P7009), and Ethically Driven AI Methodologies (IEEE P7008). Based on strategic organizational goals, the strategy provides high-level guidelines for action. Keding’s work [27] focuses on where AI and strategic management interact, with particular emphasis on two aspects: (a) antecedents such as data-driven workflows (data value and data quality), managerial willingness, and organizational determinants (AI strategy and implementation), and (b) the implications of AI for strategic management at the individual and organizational levels (e. g., human collaboration, AI, AI in business models) [119]. This project focuses on the use of AI for business information-related decision-making in organizations. Several components related to AI strategy are currently being addressed, including conceptual guidance from academics and practitioners [118, 120, 121].

According to ISO Standards 9000, the process is defined as a series of activities that convert input into output, interconnection, or interaction, while procedures are in advance for implementing activities or processes. [119] is defined as a technique. The process and procedure in the governance context is considered as standardized, documented, and repetitive. The AI process can be regarded as an important element of effective AI governance development. These are associated with the processes related to the ML and AI systems and the data governance process. The industry standard cros s-industrial process for data mining is often used when building ML models. A similar governance tool for AI, which promotes interoperability, may include models and systems architectural principles, documentation, and coding to improve configuration and preservation [32 , 39, 119. Recording each function together with its legitimacy is considered to be the best practices.

In addition, more proposals have been made with software engineering as a guide. [119] Generalization from Case Study Analysis describes the application of ML to deal with transaction errors, while others are based on research conducted by a single organization. 67] However, the ideal method still has room for debate. As mentioned in 119], the study of the model governance emphasizes the process of verification, management, and evaluation of models (eg, review policies, updates, security). [119] [124] The ML warranty, that is, the implementation of the ML development process that generates evidence of the model safety and the test technique of the ML system. As a whole, research on AI models and technology is generally lacking. According to the ISO standard 9000, the process is defined as a series of activities that convert input into output, interacting, or interact, but to carry out the activity or process. [119] is defined as a pr e-defined technology. The process and procedure in the governance context is considered as standardized, documented, and repetitive. The AI process can be regarded as an important element of effective AI governance development. These are associated with the processes related to the ML and AI systems and the data governance process. The industry standard cros s-industrial process for data mining is often used when building ML models. A similar governance tool for AI, which promotes interoperability, may include models and systems architectural principles, documentation, and coding to improve configuration and preservation [32 , 39, 119. Recording each function together with its legitimacy is considered to be the best practices.

In addition, more proposals have been made with software engineering as a guide. [119] Generalization from Case Study Analysis describes the application of ML to deal with transaction errors, while others are based on research conducted by a single organization. 67] However, the ideal method still has room for debate. As mentioned in 119], the study of the model governance emphasizes the process of verification, management, and evaluation of models (eg, review policies, updates, security). [119] [124] The ML warranty, that is, the implementation of the ML development process that generates evidence of the model safety and the test technique of the ML system. As a whole, research on AI models and technology is generally lacking. According to ISO Standards 9000, the process is defined as a series of activities that convert input into output, interconnection, or interaction, while procedures are in advance for implementing activities or processes. [119] is defined as a technique. The process and procedure in the governance context is considered as standardized, documented, and repetitive. The AI process can be regarded as an important element of effective AI governance development. These are associated with the processes related to the ML and AI systems and the data governance process. The industry standard cros s-industrial process for data mining is often used when building ML models. A similar governance tool for AI, which promotes interoperability, may include models and systems architectural principles, documentation, and coding to improve configuration and preservation [32 , 39, 119. Recording each function together with its legitimacy is considered to be the best practices.

Contractual arrangements between internal and external parties may involve data, models, and artificial intelligence systems. Models contain information about training data that may be improperly obtained and used in various ways, such as by competitors to reduce data tagging costs [119]. By precisely defining the operational parameters of AI, terms also play an important role in reducing liability risks [106, 125]. In unexpected situations or situations that go against human reasoning, artificial intelligence may malfunction. Therefore, clear legal notice is required for operational concerns. In addition, traditional risk management strategies have also been proposed for AI [126]. Compliance monitoring ensures that an organization's rules, standards, procedures, and contractual commitments are implemented and enforced. The European General Data Protection Regulation (GDPR), which affects data and models and provides a right to explanation of model decisions, is one of the most notable regulations [9, 119, 125, 127]. Issues regarding AI are identified, managed, and resolved as part of issue management. This includes procedures for formalizing and resolving data problems.

Collaboration among stakeholders is facilitated through a relational governance framework. This includes (1) communication, (2) education, and (3) decision-making coordination.[128] A collaborative development platform should be used to facilitate communication within the multidisciplinary AI team. According to Michalefand Gupta[14], technical and business knowledge are key competencies for AI. Therefore, employee training is undoubtedly important. Training is often associated with learning how to use AI, but it can also mean preparing staff for tasks that AI can automate or augment to mitigate negative effects.[129] Using communication to communicate the company's goal of deploying AI as a tool for augmentation rather than replacement will help reduce employee stress.[129] Compared to data governance, governance of ML models and AI systems is generally less understood.[117, 123] Contractual arrangements between internal and external parties may involve data, models, and artificial intelligence systems. Models contain information about training data, which may be improperly obtained and used in various ways, such as by competitors to reduce data tagging costs [119]. By precisely defining the operational parameters of AI, terms also play an important role in reducing liability risks [106, 125]. In unexpected situations or situations that go against human reasoning, artificial intelligence may malfunction. Therefore, clear legal notice is required for operational concerns. In addition, traditional risk management strategies have also been proposed for AI [126]. Compliance monitoring ensures that an organization's rules, standards, procedures, and contractual commitments are implemented and enforced. The European General Data Protection Regulation (GDPR), which has implications on data and models and provides a right to explanation of model decisions, is one of the most notable regulations [9, 119, 125, 127]. Issues with AI are identified, managed, and resolved as part of issue management. This includes procedures for formalizing and resolving data problems.

Collaboration among stakeholders is facilitated through a relational governance framework. This includes (1) communication, (2) education, and (3) decision-making coordination.[128] A collaborative development platform should be used to facilitate communication within the multidisciplinary AI team. According to Michalefand Gupta[14], technical and business knowledge are key competencies for AI. Therefore, employee training is undoubtedly important. Training is often associated with learning how to use AI, but it can also mean preparing staff tasks that AI can automate or augment to mitigate negative effects.[129] Using communication to communicate the company's goal of deploying AI as a tool for augmentation rather than replacement will help reduce employee stress.[129] Compared to data governance, governance of ML models and AI systems is generally less understood.[117, 123] Contractual arrangements between internal and external parties may involve data, models, and artificial intelligence systems. Models contain information about training data, which may be improperly obtained and used in various ways, including by competitors to reduce data tagging costs [119]. By precisely defining the operational parameters of AI, regulations also play an important role in reducing liability risks [106, 125]. In unexpected situations or situations that go against human reasoning, artificial intelligence may malfunction. Therefore, operational concerns require clear legal notice. In addition, traditional risk management strategies have also been proposed for AI [126]. Compliance monitoring ensures that an organization's rules, standards, procedures, and contractual commitments are executed and implemented. The European General Data Protection Regulation (GDPR), which has implications on data and models and provides a right to explanation of model decisions, is one of the most notable regulations [9, 119, 125, 127]. Issues with AI are identified, managed, and resolved as part of issue management. This includes procedures for formalizing and resolving data issues, and cooperation between stakeholders is facilitated through a relationship governance framework. This includes (1) communication, (2) education, and (3) decision-making coordination.[128] Collaborative development platforms should be used to facilitate communication within multidisciplinary AI teams. According to Michalefand Gupta[14], technical and business knowledge are key competencies for AI. Therefore, employee training is undoubtedly important. Training is often associated with learning how to use AI, but it can also mean preparing staff for tasks that AI can automate or augment to mitigate negative effects.[129] Using communication to communicate the company's goal of deploying AI as a tool for augmentation rather than replacement will help reduce employee stress.[129] Compared to data governance, governance of ML models and AI systems is generally less understood.[117, 123]

In the already existing literature, scholars who are studying governance and data governance argue that they provide frameworks and processes to strengthen and reduce risks caused by AI. [119]. However, AI governance, which directly affects AI and handles both IT governance and data governance, has a gap [62, 130]. As a result, research on how to implement AI governance will give great profits as well as knowledge of how to improve the performance of the organization while ignoring the shortcomings of using AI [43]; [119] scholars who are studying governance and data governance are providing frameworks and processes to strengthen and reduce risks caused by AI. [119] However, AI governance, which directly affects AI and handles both IT governance and data governance, has a gap [62, 130]. As a result, research on how to introduce AI governance will have great profits, as well as knowledge of how to improve the performance of the organization while ignoring the shortcomings of using AI. [43] There were already frameworks and processes for enhancing functions caused by AI and reducing risks. However, there is a gap in AI governance, and scholars who are studying governance and data governance in already exist, strengthen and reduce the functions caused by AI. He argues that it provides frameworks and processes for. However, AI governance, which directly affects AI and handles both IT governance and data governance, has a gap [62, 130]. As a result, research on how to implement AI governance will give great profits as well as knowledge of how to improve the performance of the organization while ignoring the shortcomings of using AI [43]; [119] scholars who are studying governance and data governance are providing frameworks and processes to strengthen and reduce risks caused by AI. [119] However, AI governance, which directly affects AI and handles both IT governance and data governance, has a gap [62, 130]. As a result, research on how to introduce AI governance will have great profits, as well as knowledge of how to improve the performance of the organization while ignoring the shortcomings of using AI. [43] There were already frameworks and processes for enhancing functions caused by AI and reducing risks. However, there is a gap in AI governance, and scholars who are studying governance and data governance in the already existing literature will strengthen and reduce risks caused by AI. He argues that it provides work and processes. However, AI governance, which directly affects AI and handles both IT governance and data governance, has a gap [62, 130]. As a result, research on how to implement AI governance will give great profits as well as knowledge of how to improve the performance of the organization while ignoring the shortcomings of using AI [43]; [119] scholars who are studying governance and data governance are providing frameworks and processes to strengthen and reduce risks caused by AI. [119] However, AI governance, which directly affects AI and handles both IT governance and data governance, has a gap [62, 130]. As a result, research on how to introduce AI governance will have great profits, as well as knowledge of how to improve the performance of the organization while ignoring the shortcomings of using AI. [43] There were already frameworks and processes for enhancing functions caused by AI and reducing risks. However, there is a gap in AI governance,

AI applicatio n-responsible development not only has ethical and moral advantages, but may also have a medium- to lon g-term competitive advantage. For example, companies can attract skilled engineers and ensure excellent human resources by showing commitment to ethical standards, especially in an era where it is difficult to find special developers. According to EIU's survey, no n-ethical business practices suppress their potential candidates and impair the trust in this field, which is the consequences of public skepticism and hostility of major hig h-tech companies. [115] is one of the s o-called "tech rush". In addition, with AI's ethics policies and procedures, it is possible to document how companies are dealing with A I-related issues [131] to identify future business problems and business opportunities. Useful for [112]. [14, 132] responsible AI has begun to affect business performance to increase customer maintenance, expenditure, and absorption of new services. By complying with ethical and responsible criteria, professional designed AI applications can protect and increase their customer base. Companies can maintain their customers and enhance their trust by securing security and creating transparent comprehensive products and services for all types of customers. For example, in order to ensure traceability and transparency, the adoption of blockchain technology for artificial intelligence services can solve customer reliability issues. From the viewpoint of compliance, it is as important in building a responsible AI governance. Authorities have monitored AI applications and have begun to create rules that incorporate standards and ethical considerations, such as audit procedures and algorithm analysis. As a result, various privacy and privacy frameworks have incorporated desig n-based privacy into their structures. AI applicatio n-responsible development not only has ethical and moral advantages, but also has a medium- to lon g-term competitive advantage. For example, companies can attract skilled engineers and ensure excellent human resources by showing commitment to ethical standards, especially in an era where it is difficult to find special developers. According to EIU's survey, no n-ethical business practices suppress their potential candidates and impair the trust in this field, which is the consequences of public skepticism and hostility of major hig h-tech companies. [115] is one of the s o-called "tech rush". In addition, with AI's ethics policies and procedures, it is possible to document how companies are dealing with A I-related issues [131] to identify future business problems and business opportunities. Useful for [112]. [14, 132] responsible AI has begun to affect business performance to increase customer maintenance, expenditure, and absorption of new services. By complying with ethical and responsible criteria, professional designed AI applications can protect and increase their customer base. Companies can maintain their customers and enhance their trust by securing security and creating transparent comprehensive products and services for all types of customers. For example, in order to ensure traceability and transparency, the adoption of blockchain technology for artificial intelligence services can solve customer reliability issues. From the viewpoint of compliance, it is as important in building a responsible AI governance. Authorities have monitored AI applications and have begun to create rules that incorporate standards and ethical considerations, such as audit procedures and algorithm analysis. As a result, various privacy and privacy frameworks have incorporated desig n-based privacy into their structures. AI applicatio n-responsible development not only has ethical and moral advantages, but may also have a medium- to lon g-term competitive advantage. For example, companies can attract skilled engineers and ensure excellent human resources by showing commitment to ethical standards, especially in an era where it is difficult to find special developers. According to EIU's survey, no n-ethical business practices suppress their potential candidates and impair the trust in this field, which is the consequences of public skepticism and hostility of major hig h-tech companies. [115] is one of the s o-called "tech rush". In addition, with AI's ethics policies and procedures, it is possible to document how companies are dealing with A I-related issues [131] to identify future business problems and business opportunities. Useful for [112]. [14, 132] responsible AI has begun to affect business performance to increase customer maintenance, expenditure, and absorption of new services. By complying with ethical and responsible criteria, professional designed AI applications can protect and increase their customer base. Companies can maintain their customers and enhance their trust by securing security and creating transparent comprehensive products and services for all types of customers. For example, in order to ensure traceability and transparency, the adoption of blockchain technology for artificial intelligence services can solve customer reliability issues. From the viewpoint of compliance, it is as important in building a responsible AI governance. Authorities have monitored AI applications and have begun to create rules that incorporate standards and ethical considerations, such as audit procedures and algorithm analysis. As a result, various privacy and privacy frameworks have incorporated desig n-based privacy into their structures.

There are seven elements to the idea of responsible AI governance. Accountability, environmental sustainability, social well-being, transparency, fairness, equality, robustness and security, data governance, and human-centered AI are some of these dimensions[112]. Developing a responsible AI governance system requires a lot of effort, and one of its main goals is to reduce the likelihood that small changes in input weights will significantly alter the output of a machine learning model. Continuous evaluation is necessary to ensure that companies are working to create objective and trustworthy AI[119]. Therefore, having maturity models and standards of compliance is essential for companies developing and deploying AI systems. The role of knowledge in improving organizational performance is crucial, as is the organization's ability to successfully adapt information for future use and respond to changes in the environment[90].

The performance of AI applications should be evaluated by organizations involved in responsible AI governance, both before and after deployment[35]. The ability to clearly and concisely describe aspects of data and AI governance and explain their interactions is also important[12]. For example, it is important to document each step of the data processing process, from data collection to data use.[133] Documentation also reduces reliance on expertise, since documented processes are less likely to become obsolete. Responsible AI governance also emphasizes inclusive design and the development of human services and autonomy. These are key elements to improve the utilization of human capital within the enterprise and maximize knowledge exchange and relationships. Finally, proper governance of AI must emphasize the security and trustworthiness of the subjects and systems that communicate with AI agents through the design, implementation, and monitoring of AI applications.[71, 134] Establishing such privacy and security regulations promotes mutual access and knowledge sharing without the risk of confidential information being leaked or accessed by unauthorized employees.[135] The proactive aspect of strategic flexibility seems less pervasive in the existing body of knowledge, but scholars define and operationalize it primarily as a rather reactive capability to meet environmental demands.[21, 136, 137]

This study has a wide range of and comprehensive understanding of what strategic flexibility means in accordance with the strategic flexibility paradigm presented in [138]. As a result, the use of existing documents to identify the four major components of strategic flexibility, and these are united to create the concept of dynamic capability [139]. These components are suitable for resource development strategies, competitive actions, professional activity, and rear category and capability [37, 136, 140, 141]. As a result, strategic agility is a multifaceted concept. These four elements are (1) prediction of the promotion of changes and future options [43, 136, 140, 142, 143], (2) Design and design [137, 140, 144, 145], and (3) Assembling and developing abilities [95, 136, 1414, 146, 146].

The first factor focuses on the abilities of many business contracts and scenarios that can be caused in the future, based on the promotion of changes. This theoretical aspect of strategic flexibility describes the path between prediction (denial of uncertainty) and paralysis (excessive vague). [21, 138, 148]. For example, a scenario supports the organization to think more flexibly about the future so that it can respond promptly depending on the changing situation. [20, 136] promotes innovation and creativity by letting the organization think about the future more deeply than usual and the decisio n-determined users consider new options. Thus, the first factor helps understand the promotion factors, interactions, and changing dynamics in the business environment.

In order to identify important and potentially necessary unexpected situations, the ability to evaluate the ideal strategy of the organization is the second aspect of strategic agility, the focus of strategy formulation and planning. As a result, by consistently doing this, it is made to make decisions on how to create value and build a clear accountability for the results. [9, 145]. The decisio n-maker can collect and analyze future options and scenarios using surgical approaches. [144, 149] can be focused on dealing with serious uncertainty in order to support decisio n-making and enhance the commitment of action.

5. Conclusions

The third element, "Assembly and Development of Ability", includes the acquisition and choice of the necessary ability to execute the basic plan. In order for companies to adapt to the demands of changing customers and market, the development of adaptable abilities must be accelerated [2, 138, 148]. In order to effectively develop the skills of the tissue and do the structure, process, decision right, performance indicators, and adaptation mechanisms, the organization needs to design relevant series of improvements [9, 147 ]. In order to make the change successful, the decisio n-determined and leaders need to change their mindset and behavior. Ultimately, this will allow companies to respond to the rapid and complex changes in the environment. [95] Furthermore, instead of restricting the adaptive ability of the organization, the transformation program that supports it is likely to lead to lon g-term success.

In search of the role of AI in the strategic process, the possibility that AI may improve and change strategic analysis is the starting point. In order to support environmental scanning and data collection and arrangement for decisio n-making, managers have long rely on information systems [128], but AI algorithms have greatly improved the size, range, and speed of the organization's analysis. There is a possibility. This is because AI algorithm interacts with the environment and can generate data. According to previous research [66], AI specifically collects or continuously collects data from (a) internal and external sources, analyzes and interprets this data through pattern identification. c) It is possible to support executives who support decisio n-making through predictive analysis. As a starting point for mapping how AI can support strategic analysis, the application to the analysis of external (that is, macro and micro environment) is explained. Next, we will examine the role of AI in competitive analysis, and then examine the role of AI in internal analysis of human resources, financial resources, and auxiliary resources. The third element, "Assembly and Development of Ability", includes the acquisition and choice of the necessary ability to execute the basic plan. In order for companies to adapt to the demands of changing customers and market, the development of adaptable abilities must be accelerated [2, 138, 148]. In order to effectively develop the skills of the tissue and do the structure, process, decision right, performance indicators, and adaptation mechanisms, the organization needs to design relevant series of improvements [9, 147 ]. In order to make the change successful, the decisio n-determined and leaders need to change their mindset and behavior. Ultimately, this will allow companies to respond to the rapid and complex changes in the environment. [95] Furthermore, instead of restricting the adaptive ability of the organization, the transformation program that supports it is likely to lead to lon g-term success.

In search of the role of AI in the strategic process, the possibility that AI may improve and change strategic analysis is the starting point. In order to support environmental scanning and data collection and arrangement for decisio n-making, managers have long rely on information systems [128], but AI algorithms have greatly improved the size, range, and speed of the organization's analysis. There is a possibility. This is because AI algorithm interacts with the environment and can generate data. According to previous research [66], AI specifically collects or continuously collects data from (a) internal and external sources, analyzes and interprets this data through pattern identification. c) It is possible to support executives who support decisio n-making through predictive analysis. As a starting point for mapping how AI can support strategic analysis, the application to the analysis of external (that is, macro and micro environment) is explained. Next, we will examine the role of AI in competitive analysis, and then examine the role of AI in internal analysis of human resources, financial resources, and auxiliary resources. The third element, "Assembly and Development of Ability", includes the acquisition and choice of the necessary ability to execute the basic plan. In order for companies to adapt to the demands of changing customers and market, the development of adaptable abilities must be accelerated [2, 138, 148]. In order to effectively develop the skills of the tissue and do the structure, process, decision right, performance indicators, and adaptation mechanisms, the organization needs to design relevant series of improvements [9, 147 ]. In order to make the change successful, the decisio n-determined and leaders need to change their mindset and behavior. Ultimately, this will allow companies to respond to the rapid and complex changes in the environment. [95] Furthermore, instead of restricting the adaptive ability of the organization, the transformation program that supports it is likely to lead to lon g-term success.

Author Contributions

In terms of external analysis, the strategic evaluation of political, economic, social, technical, environmental and legal variables is affected by the AI application. Using various A I-based text analysis technology created by political scientists, you can guess the "political environment" in all fields. These systems survey various data sources, such as news organizations, parliamentary processes, and online political dialogue. In particular, no n-structured analysis of social media data using ML algorithms has been successful. According to research on Chan and Zhong [151], all election results, policy changes, political bias, and disputes can be predicted using such data. Using AI can identify and predict political trends and events early, and to better understand how government and government policies will affect business. You can get competitive advantage. In addition, ML can be used to quantify economic patterns such as economic growth, start of recession, poverty, and bankruptcy with accurate and consistent [63, 66]. The adoption of ML tools in the financial service field is external analysis, and the strategic evaluation of political, economic, social, technical, environmental, and legal variables is influenced by the AI application. [3] Using various A I-based text analysis technology created by political scientists, you can guess the "political environment" in all fields. These systems survey various data sources, such as news organizations, parliamentary processes, and online political dialogue. In particular, no n-structured analysis of social media data using ML algorithms has been successful. According to research on Chan and Zhong [151], all election results, policy changes, political bias, and conflicts can be predicted using such data. Using AI can identify and predict political trends and events early, and to better understand how government and government policies will affect business. You can get competitive advantage. In addition, ML can be used to quantify economic patterns such as economic growth, start of recession, poverty, and bankruptcy with accurate and consistent [63, 66]. In terms of external analysis, the adoption of ML tools in the financial service field is that the strategic evaluation of political, economic, social, technical, environmental, and legal variables is affected by the AI application. 3] Using various A I-based text analysis technology created by political scientists, you can guess the "political environment" in all fields. These systems survey various data sources, such as news organizations, parliamentary processes, and online political dialogue. In particular, no n-structured analysis of social media data using ML algorithms has been successful. According to research on Chan and Zhong [151], all election results, policy changes, political bias, and disputes can be predicted using such data. Using AI can identify and predict political trends and events early, and to better understand how government and government policies will affect business. You can get competitive advantage. In addition, ML can be used to quantify economic patterns such as economic growth, start of recession, poverty, and bankruptcy with accurate and consistent [63, 66]. The adoption of ML tools in the financial service field

Funding

Changes in demographics and changes in social values are only two examples of social elements that the general must take into account in analysis. Some of these changes (like ethnic culture) are gentle, while others are prone to sudden reversal and sudden changes (for example, fashion), which are serious for the organization. [106] Possibility of opportunities and risks. In order to map the contours in cultural areas, classify cultural materials, and track cultural evolution over time, scholars propose methods such as automatic text analysis [154]. In addition, the new algorithm enables systematic cultural measurements in social associations and organizations and modeling their development [27, 86]. Such developments have new possibilities in strategic research measurement of difficult concepts such as culture. From another point of view, whether it is a manufacturer or a user, researchers often say that technical elements are important in most strategic analysis. Artificial intelligence helps companies monitor technical development and predict serious technical changes. For example, by searching for a huge amount of patents and dissertation data, artificial intelligence is increasingly used. Changes in demographics and changes in social values are only two examples of social elements that the general must take into account in analysis. Some of these changes (like ethnic culture) are gentle, while others are prone to sudden reversal and sudden changes (for example, fashion), which are serious for the organization. [106] Possibility of opportunities and risks. In order to map the contours in cultural areas, classify cultural materials, and track cultural evolution over time, scholars propose methods such as automatic text analysis [154]. In addition, the new algorithm enables systematic cultural measurements in social associations and organizations and modeling their development [27, 86]. Such developments have new possibilities in strategic research measurement of difficult concepts such as culture. From another point of view, whether it is a manufacturer or a user, researchers often say that technical elements are important in most strategic analysis. Artificial intelligence helps companies monitor technical development and predict serious technical changes. For example, by searching for a huge amount of patents and dissertation data, artificial intelligence is increasingly used. Changes in demographics and changes in social values are only two examples of social elements that the general must take into account in analysis. Some of these changes (like ethnic culture) are gentle, while others are prone to sudden reversal and sudden changes (for example, fashion), which are serious for the organization. [106] Possibility of opportunities and risks. In order to map the contours in cultural areas, classify cultural materials, and track cultural evolution over time, scholars propose methods such as automatic text analysis [154]. In addition, the new algorithm enables systematic cultural measurements in social associations and organizations and modeling their development [27, 86]. Such developments have new possibilities in strategic research measurement of difficult concepts such as culture. From another point of view, whether it is a manufacturer or a user, researchers often say that technical elements are important in most strategic analysis. Artificial intelligence helps companies monitor technical development and predict serious technical changes. For example, by searching for a huge amount of patents and dissertation data, artificial intelligence is increasingly used.

Data Availability Statement

With the growing awareness of climate change and the emphasis on corporate liability for sustainability (CSR), consideration of ecosystems and environments has gradually increased in strategic analysis. Research on the use of AI in this type of research is still in the early stages, but there are also reassured cases, such as research to find whether hydrogen electric vehicles can be the most common means of consumers [86]. 。 Such applications are indispensable for investors for profit and for policy devices to choose where and how to build transportation infrastructure. Organizations usually invest a large amount of legal advisors. Legal advisors are often manually compiled and evaluated the huge amounts of applicable laws, which may change their business conditions depending on the country and legal system. [39] Artificial intelligence applications can support the company's internal collection and processing of such legal data by companies and reduce overall legal costs. For example, AI algorithms, which sort thousands of pages of legal documents, can provide useful summaries related to corporate strategies. AI is also being rapidly used for monitoring and automating financial compliance [157].

Conflicts of Interest

Appendix A

Competitive analysis is an important field that AI is used for strategic analysis. Automated analysis is useful for data clustering, data tendency identification, and even competitors' strategic movements. Strategists often need to identify relevant competitors to promote strategic analysis, but this is still one of the most difficult tasks. A new method to identify the competition that will appear and predict business performance is the web content survey to determine the “correlation” and “convergence” of the organization's services and products. Similarly, AI algorithms can be used to identify strategic groups consisting of companies with similar strategies and estimate the crossover between companies and groups within these groups. With the growing awareness of climate change and the emphasis on corporate liability for sustainability (CSR), consideration of ecosystems and environments has gradually increased in strategic analysis. Research on the use of AI in this type of research is still in the early stages, but there are also reassured cases, such as research to find whether hydrogen electric vehicles can be the most common means of consumers [86]. 。 Such applications are indispensable for investors for profit and for policy devices to choose where and how to build transportation infrastructure. Organizations usually invest a large amount of legal advisors. Legal advisors are often manually compiled and evaluated the huge amounts of applicable laws, which may change their business conditions depending on the country and legal system. [39] Artificial intelligence applications can support the company's internal collection and processing of such legal data by companies and reduce overall legal costs. For example, AI algorithms, which sort thousands of pages of legal documents, can provide useful summaries related to corporate strategies. AI is also being rapidly used for monitoring and automating financial compliance [157]. Competitive analysis is an important field that AI is used for strategic analysis. Automated analysis is useful for data clustering, data tendency identification, and even competitors' strategic movements. Strategists often need to identify relevant competitors to promote strategic analysis, but this is still one of the most difficult tasks. A new method to identify the competition that will appear and predict business performance is the web content survey to determine the “correlation” and “convergence” of the organization's services and products. Similarly, AI algorithms can be used to identify strategic groups consisting of companies with similar strategies and estimate the crossover between companies and groups within these groups. With the growing awareness of climate change and the emphasis on corporate liability for sustainability (CSR), consideration of ecosystems and environments has gradually increased in strategic analysis. Research on the use of AI in this type of research is still in the early stages, but there are also reassured cases, such as research to find whether hydrogen electric vehicles can be the most common means of consumers [86]. 。 Such applications are indispensable for investors for profit and for policy devices to choose where and how to build transportation infrastructure. Organizations usually invest a large amount of legal advisors. Legal advisors are often manually compiled and evaluated the huge amounts of applicable laws, which may change their business conditions depending on the country and legal system. [39] Artificial intelligence applications can support the company's internal collection and processing of such legal data by companies and reduce overall legal costs. For example, AI algorithms, which sort thousands of pages of legal documents, can provide useful summaries related to corporate strategies. AI is also being rapidly used for monitoring and automating financial compliance [157].

All these tools provide more dynamic methods for performing competition analysis. In fact, academic experts have warned that it is necessary to r e-examine and utilize the prerequisites of competition in order to evaluate the continuous validity of the organization's strategy. [86] Automated competitive analysis has a great advantage in conventional manual and individual approaches: If you connect to machines that generate market and competitive data, such as web scanners and web sales, algorithm continues to be competitive analysis. It is possible for strategic to dynamically evaluate strategic execution of the organization's current strategies.
In addition, from the viewpoint of internal analysis, AI can support strategic analysis of internal elements, such as human resources, budgets, supply chains and customer relationships, [132]. The AI application supports human resources and other data pattern recognition, especially for managers track employee performance, predict their career paths, and clarify the patterns of rewards and gaps. Can be done [10]. Strategists can more accurately measure how much potential strategy can be executed by carefully evaluating skills, knowledge, competency, and demographics.				In addition, the organization has a huge amount of detailed and accurate accurate accounting and financial data. When performing analysis, the strategist often uses the data of the balance sheet. The AI algorithm can analyze the collected standard or discrete data. By performing this, algorithms will improve the interpretation of specific financial resources related to the nature and timing of corporate strategic commitments, such as fluid premium and exchange rate fluctuations in corporate budgets. Can be done [45, 158]. AI can also apply to economic research that is not so important for strategic formulation, such as tools for monitoring business transactions [157]. The application can quickly detect fraud by detecting abnormal amounts and frequency transactions. With the help of such tools, AI will strengthen compliance and risk management activities with better dat a-led information, use hig h-speed and automated data analysis, and reduce the management burden. [142] By turning the time of the accounting staff to more ingenious tasks, it can be useful for managing financial risks.
Various additional resources for strategic processes, such as demand forecasts, production plans, resource allocation, and logistics, are being analyzed with remarkable efficiency using AI algorithms. AI, for example, automates the process of mining data from online catalogs and other repositories to select a qualified supplier [9, 128], provides predictions on potential suppliers [159]. In addition, by estimating the evaluation and evaluation of online bidding and [160], the decisio n-making decision of the production plan can be strengthened.	In addition, the complementary resources that are particularly suitable for the analysis supported by AI are related to the relationship with customers. [161] Customers have more conversations on various social media platforms related to the consumption of products, services, digital information, and the sharing of expertise. Strategist can better understand the patterns and seasonal patterns of consumer needs related to services and products by tracking customer behavior through digital footprint. Such information can be used to predict customer preferences, eventually leading to important analysis for corporate products.	Many companies are using AI at the center of hig h-level strategies, given the rapid rise in genera l-purpose technology. In a keynote speech in I/0 2017, Sundar Pichai, Google's CEO, emphasizes the company's policy that gives priority to "AI First", increases the efficiency of organizations and improves customer experiences. He emphasized that other AI technology would be used from a greater perspective. In the next few years, Google and their business partners use AI to use AI to identify the people, places, and objects in the image, Rankbrain, which speeds up searches, and Google Astand, a personal virtual assistant. We invested a lot of products that seem to be "intelligent". All of these movements greatly raised the evaluation of the entire Google. < SPAN> Various additional resources, such as demand forecasts, production plans, resource allocation, and logistics, are being analyzed with remarkable efficiency using AI algorithms. AI, for example, automates the process of mining data from online catalogs and other repositories to select a qualified supplier [9, 128], provides predictions on potential suppliers [159]. In addition, by estimating the evaluation and evaluation of online bidding and [160], the decisio n-making decision of the production plan can be strengthened.	In addition, the complementary resources that are particularly suitable for the analysis supported by AI are related to the relationship with customers. [161] Customers have more conversations on various social media platforms related to the consumption of products, services, digital information, and the sharing of expertise. Strategist can better understand the patterns and seasonal patterns of consumer needs related to services and products by tracking customer behavior through digital footprint. Such information can be used to predict customer preferences, eventually leading to important analysis for corporate products.	Many companies are using AI at the center of hig h-level strategies, given the rapid rise in genera l-purpose technology. In a keynote speech in I/0 2017, Sundar Pichai, Google's CEO, emphasizes the company's policy that gives priority to "AI First", increases the efficiency of organizations and improves customer experiences. He emphasized that other AI technology would be used from a greater perspective. In the next few years, Google and their business partners use AI to use AI to identify the people, places, and objects in the image, Rankbrain, which speeds up searches, and Google Astand, a personal virtual assistant. We invested a lot of products that seem to be "intelligent". All of these movements greatly raised the evaluation of the entire Google. Various additional resources for strategic processes, such as demand forecasts, production plans, resource allocation, and logistics, are being analyzed with remarkable efficiency using AI algorithms. AI, for example, automates the process of mining data from online catalogs and other repositories to select a qualified supplier [9, 128], provides predictions on potential suppliers [159]. In addition, by estimating the evaluation and evaluation of online bidding and [160], the decisio n-making decision of the production plan can be strengthened.	In addition, the complementary resources that are particularly suitable for the analysis supported by AI are related to the relationship with customers. [161] Customers have more conversations on various social media platforms related to the consumption of products, services, digital information, and the sharing of expertise. Strategist can better understand the patterns and seasonal patterns of consumer needs related to services and products by tracking customer behavior through digital footprint. Such information can be used to predict customer preferences, eventually leading to important analysis for corporate products.	Many companies are using AI at the center of hig h-level strategies, given the rapid rise in genera l-purpose technology. In the keynote speech of I/0 2017, Google's CEO Sundar Pichai emphasizes the company's policy that gives priority to "AI First", increases the efficiency of organizations and improves customer experiences. He emphasized that other AI technology would be used from a greater perspective. In the next few years, Google and their business partners use AI to use AI to identify the people, places, and objects in the image, Rankbrain, which speeds up searches, and Google Astand, a personal virtual assistant. We invested a lot of products that seem to be "intelligent". All of these movements greatly raised the evaluation of the entire Google.	Activities that make up the stage of strategy formulation and execution process also change as a result of AI. [157] The organization sets business growth goals, financial goals, and relevant execution strategies at various levels of sensitivity to pursue profitable growth. The formulation and execution of a strategy is a lon g-term complex process, including a significant amount of data, whether such processes are frequently formatively performed or in rare cases. [9, 12, 34] rely on business analysis and decisio n-making at various levels of organizations. Therefore, the cognitive limits of strategists in data processing for the decision making and implementation, which are the basis of strategy formulation and implementation, are widely documented in literature. The debate on the development of the previous generation of the previous generation since the 1950s is surprisingly similar to the debate on the possibility of artificial intelligence that improves human strategy and execution.	On the other hand, scientists seem to agree that the latest applications of big data and artificial intelligence have surpassed the abilities of the previous system. [106, 163] is essential for effective strategic formulas to understand the company, its environment, and strategic choice risk return profiles [106, 163]. Similarly, strategists are also essential for successful strategic execution, as strategists monitors operations and evaluates the effectiveness of strategic activities. Therefore, the ability of the AI application that optimizes continuous data analysis, generates new information on strategic opportunities, and identifies the patterns to predict the results of options, is the development that mainly requires cognitive elements. [27, 30, 63] improves the quality of strategic decisio n-making related to the executable task. < SPAN> activities that make up the stage of strategy formulation and execution processes also change as a result of AI. [157] The organization sets business growth goals, financial goals, and relevant execution strategies at various levels of sensitivity to pursue profitable growth. The formulation and execution of a strategy is a lon g-term complex process, including a significant amount of data, whether such processes are frequently formatively performed or in rare cases. [9, 12, 34] rely on business analysis and decisio n-making at various levels of organizations. Therefore, the cognitive limits of strategists in data processing for the decision making and implementation, which are the basis of strategy formulation and implementation, are widely documented in literature. The debate on the development of the previous generation of decisio n-making support systems since the 1950s is surprisingly similar to discussions on the possibility of artificial intelligence that improves human strategy and execution.	On the other hand, scientists seem to agree that the latest applications of big data and artificial intelligence have surpassed the abilities of the previous system. [106, 163] is essential for effective strategic formulas to understand the company, its environment, and strategic choice risk return profiles [106, 163]. Similarly, strategists are also essential for successful strategic execution, as strategists monitors operations and evaluates the effectiveness of strategic activities. Therefore, the ability of the AI application that optimizes continuous data analysis, generates new information on strategic opportunities, and identifies the patterns to predict the results of options, is the development that mainly requires cognitive elements. [27, 30, 63] improves the quality of strategic decisio n-making related to the executable task. Activities that make up the stage of strategy formulation and execution process also change as a result of AI. [157] The organization sets business growth goals, financial goals, and relevant execution strategies at various levels of sensitivity to pursue profitable growth. The formulation and execution of a strategy is a lon g-term complex process, including a significant amount of data, whether such processes are frequently formatively performed or in rare cases. [9, 12, 34] depends on the management analysis and decisio n-making at various levels of the organization. Therefore, the cognitive limits of strategists in data processing for the decision making and implementation, which are the basis of strategy formulation and implementation, are widely documented in literature. The debate on the development of the previous generation of decisio n-making support systems since the 1950s is surprisingly similar to discussions on the possibility of artificial intelligence that improves human strategy and execution.
On the other hand, scientists seem to agree that the latest applications of big data and artificial intelligence have surpassed the abilities of the previous system. [106, 163] is essential for effective strategic formulas to understand the company, its environment, and strategic choice risk return profiles [106, 163]. Similarly, strategic is also essential for successful strategic execution, as strategist monitors operating and evaluates the effectiveness of strategic activities. Therefore, the ability of AI applications to optimize continuous data analysis, generate new information about strategic opportunities, and identify patterns to predict the results of options is to formulate mainly that requires cognitive elements. [27, 30, 63] improves the quality of strategic decisio n-making related to the executable task.	2021	x	x	x	x
On the other hand, there are some caveats regarding the limitations of AI systems in strategy formulation. For example, researchers have found that AI performs well in stable environment-specific settings, but underperforms in settings that involve creativity, innovation, and uncertainty [12, 62, 164] and may even increase the overall complexity of organizational decision-making [66]. As a result, AI is best viewed as an “enabler” that improves strategists’ decision-making capabilities regarding external threats and opportunities, internal strengths and weaknesses, and strategic challenges [60, 73].	2010	x	x	x	x
To provide an example of how AI can be used in a specific area of strategy formulation and execution, we consider its function in the study of a typical corporate-level strategy involving a company’s business portfolio and decisions regarding growth and diversification [165]. A company’s business portfolio can be assessed for fit, risk, and performance using AI, and potential synergies between companies can be analyzed and proposed [159]. AI can assist strategists in addressing portfolio risk. For example, when demand changes are highly correlated across a portfolio or when multiple businesses are exposed to similar exchange rate risks, they can be addressed based on the study of market and product data across business units. Such AI “sparring partner” activities are called “cognitive insights” by Davenport [29].	2021	x	x	x	x
AI Technology has also changed the way companies identify and evaluate potential M & Amp; A candidates, and how to treat the deal cycle and the integrated phase after M & amp; A. Conventional M & Amp; A (merger / acquisition) is forced to track a limited target candidate, although it requires various analytics, due to du deviation, market analysis, valuation, and pricing. By analyzing, companies can automatically and continuously display companies when many opportunities are presented. [5, 86]. Automatic summary and topic modeling of documents are two examples of natural language processing (NLP) technology, and can be used to create filters to prepare more attractive situations so that humans can consider them. The advantage of AI in this situation is that in order to identify rare skills, it is the ability to integrate various data sources, such as patented database, organization's financial records, M-A preceding data, social media, Linkedin data. Origin. Set, media, documentation, discussion forum, and dynamics customize the overall standards.	2021	x	x	x	x	x
Furthermore, in consideration of the speed of decisio n-making and efficiency at the integration stage after the merger and the importance of efficiency after the merger, AI automates the steps of the acquisition process and reduce important M & AMP; A activities. So, you can have a big impact on performance. A [109]. These tasks may be able to be executed more cheaply. For example, in recent years, in recent years, transaction legal affairs have greatly advanced, and legal experts have been able to rationalize the acquisition process in the acquisition work. Among the many uses of AI, automation is useful for companies to aim to collect and class related documents more efficiently while ensuring the information disclosure law. [168] < SPAN> AI technology has also changed the way companies identify and evaluate potential M & amp; A candidates, and how to handle the deal cycle and the integrated phase after M & amp; A. Conventional M & Amp; A (merger / acquisition) is forced to track a limited target candidate, although it requires various analytics, due to du deviation, market analysis, valuation, and pricing. By analyzing, companies can automatically and continuously display companies when many opportunities are presented. [5, 86]. Automatic summary and topic modeling of documents are two examples of natural language processing (NLP) technology, and can be used to create filters to prepare more attractive situations so that humans can consider them. The advantage of AI in this situation is that in order to identify rare skills, it is the ability to integrate various data sources, such as patented database, organization's financial records, M-A preceding data, social media, Linkedin data. Origin. Set, media, documentation, discussion forum, and dynamics customize the overall standards.	2019	x	x	x	x
Furthermore, in consideration of the speed of decisio n-making and efficiency at the integration stage after the merger and the importance of efficiency after the merger, AI automates the steps of the acquisition process and reduce important M & AMP; A activities. So, you can have a big impact on performance. A [109]. These tasks may be able to be executed more cheaply. For example, in recent years, in recent years, transaction legal affairs have greatly advanced, and legal experts have been able to rationalize the acquisition process in the acquisition work. Among the many uses of AI, automation is useful for companies to aim to collect and class related documents more efficiently while ensuring the information disclosure law. [168] AI Technology has also changed the way companies identify and evaluate potential M & Amp; A candidates, and how to treat the deal cycle and the integrated phase after M & amp; A. Conventional M & Amp; A (merger / acquisition) is forced to track a limited target candidate, although it requires various analytics, due to du deviation, market analysis, valuation, and pricing. By analyzing, companies can automatically and continuously display companies when many opportunities are presented. [5, 86]. Automatic summary and topic modeling of documents are two examples of natural language processing (NLP) technology, and can be used to create filters to prepare more attractive situations so that humans can consider them. The advantage of AI in this situation is that in order to identify rare skills, it is the ability to integrate various data sources, such as patented database, organization's financial records, M-A preceding data, social media, Linkedin data. Origin. Set, media, documentation, discussion forum, and dynamics customize the overall standards.	2017	x	x	x	x
Furthermore, in consideration of the speed of decisio n-making and efficiency at the integration stage after the merger and the importance of efficiency after the merger, AI automates the steps of the acquisition process and reduce important M & AMP; A activities. So, you can have a big impact on performance. A [109]. These tasks may be able to be executed more cheaply. For example, in recent years, in recent years, transaction legal affairs have greatly advanced, and legal experts have been able to rationalize the acquisition process in the acquisition work. Among the many uses of AI, automation is useful for companies to aim to collect and class related documents more efficiently while ensuring the information disclosure law. [168]	2020	x	x	x	x	x
With the introduction of AI, it will further enhance and accelerate the integration of operations by integrating the huge amount of no n-structured data obtained from a different type of business system, such as in customer databases and contract repositories, further strengthening and accelerating operations. Can be found. Similarly, AI can also be used for collaboration strategies, such as selecting and identifying partners in strategic partnerships. For example, indicators, such as the focus and strategic direction of potential partners, can be found by strategists using the NLP of the annual report.	2019	x	x	x	x	x	x
The adoption of AI technology by companies reveals how the opportunities for startups, strategic entrepreneurs spirit and barriers can occur. Genera l-purpose technology (GPT), AI, especially deep learning as a new creation method, has the potential to change the structure of the R & D process. [1, 20, 170] may change the properties of A and innovation processes. They claim that improving AI will bring a reinforced loop between the AI -level innovation and its applied area, as in the previous GPT development, like a microcessor.	2022	x	x	x	x	x	x
According to Chalmers, Mackenzie and Carter [171], there are three ways to improve the process of information search and ideas that AI will find and develop new business opportunities. First, with the ability of deep learning algorithms that identify the structure of hig h-dimensional data, entrepreneurs of companies and emerging companies can search for and test prospects for unable to observe before. Second, AI can promote the creation of new businesses by identifying and utilizing consumer demand. Third, simulation and tests using AI provide opportunities to test new ideas and businesses to fund providers and business owners. For example, a simulation using dynamic real world data may be able to imitate a situation where a new business may face in the future. If such tools are fully developed, it will not only help investing in investment, but will also help with innovation design and important product functions such as price setting and marketing [20, 34]. In this way, entrepreneurs can test their ideas, predict how the buyers react to the characteristics of a product, and adjust the design and marketing.	2022	x	x	x	x	x	x
Through the development of chat systems and robot sales consultants, AI has also been progressing by supporting and replacing human activities in the development of new business ideas. It is not yet clear that these solutions will be worth the long term. Robot consultant's doubts about the loss of tissue and the effects of the loss of relationships with customers, and concerns about customer acceptance in contact with A I-based systems will bring new research opportunities in the future [132 ]. Regarding the expansion of new businesses and the control of risks associated with business execution, it is important to have an important question from a strategic perspective on how A I-based technology affects strategic entrepreneurship.	2021	x	x	x	x	x	x
Strategic control refers to strategic actions that guarantee that strategic planning will proceed as planned and monitor the achievement of strategic goals. Despite the need, there are not many research on the use of AI for strategic control. However, we have the ability to automatically identify the appropriate performance standards, monitor and evaluate performance in light of the standards, and propose the path of corrective measures based on predictive analysis, so we will find some executable fields. You can. It is particularly interesting to use AI for project management and i n-house communication. AI will be an important tool that supports strategists who evaluate strategies and performances as more and more profit companies are managed on an increasingly connected digital platform. 90, 159].	2018	x	x	x	x
So far, it has been shown that applying AI technology to the form of an organization will increase the results of various business value, but it has not yet begun this new technology era. At present, there are few knowledge from the theoretical and technical perspective, but research on AI flow and business value in the organization should be noticeable. Pr e-research [91, 172, 173] is considering how IT resources have a higher level of organizational ability, but the development of intermediate abilities in the context of AI is very noteworthy. Not done [74, 174]. As a result, companies should adopt a tw o-way viewpoint that new use of routines and AI is reflected in the development of strategic agility of the organization while actually using AI.	2022	x	x	x	x	x	x
It may not be as simple as you can imagine, but it is difficult to bring out economic value from both profitable AI. First, considering the introduction and progress of AI, the daily and innovative use of AI may differ depending on the organization. According to the literature, AI should be used as an important organizational resource for building an organization so that the organization can maximize its potential. [42, 175]. Thus, important dynamic abilities and strategic agility evolved as a result of simultaneously focusing on these two basic characteristics of both interest AI [139]. This is often the same as the organization's strategic agility, often asserted that [72, 176], which is often affected by actually applying analytics and AI. In particular, AI's bilateral interests can predict the main changing drivers and business scenarios that can occur in the future, so that companies can predict market trends, make them better understand and respond appropriately. It brings the preliminary profit [37]. Dat a-led business plan, scenario, and frameworks to promote AI to promote commitment to actions are provided by both A I-friendly, AI. [13, 149, 177] < SPAN> It may not be as simple as you imagine, but it is difficult to bring out economic value from both interests. First, considering the introduction and progress of AI, the daily and innovative use of AI may differ depending on the organization. According to the literature, AI should be used as an important organizational resource for building an organization so that the organization can maximize its potential. [42, 175]. Thus, important dynamic abilities and strategic agility evolved as a result of simultaneously focusing on these two basic characteristics of both interest AI [139]. This is often the same as the organization's strategic agility, often asserted that [72, 176], which is often affected by actually applying analytics and AI. In particular, AI's bilateral interests can predict the main changing drivers and business scenarios that can occur in the future, so that companies can predict market trends, make them better understand and respond appropriately. It brings the preliminary profit [37]. [13, 149, 177], a framework for developing AI to promote dat a-led business plan, scenario, and commitment to behavior. [13, 149, 177] It may not be as simple as you can imagine, but it is difficult to bring out economic value from both profitable AI. First, considering the introduction and progress of AI, the daily and innovative use of AI may differ depending on the organization. According to the literature, AI should be used as an important organizational resource for building an organization so that the organization can maximize its potential. [42, 175]. Thus, important dynamic abilities and strategic agility evolved as a result of simultaneously focusing on these two basic characteristics of both interest AI [139]. This is often the same as the organization's strategic agility, often asserted that [72, 176], which is often affected by actually applying analytics and AI. In particular, AI's bilateral interests can predict the main changing drivers and business scenarios that can occur in the future, so that companies can predict market trends, make them better understand and respond appropriately. It brings the preliminary profit [37]. Dat a-led business plan, scenario, and frameworks to promote AI to promote commitment to actions are provided by both A I-friendly, AI. [13, 149, 177]	2021	x	x	x	x	x	x	x
In addition, AI vulnerability adjusts corporate resources, skills, unrelated abilities, and simplifies the ability to mobilize. [37, 63]. In addition, AI's weaknesses are to provide companies to reform their business processes with the help of intelligent technology and stat e-o f-th e-art algorithms. Finally, the organization constructs a business strategy by giving the decisio n-made [8, 72], the development or adjustment of business alternatives throughout the organization [5] by giving the authority to enter the potential new business area [5]. It can be executed on the target. The use of these seemingly incompatible and competing AI applications means that business can be more possible for business to detect and predict change, actively respond to changes, and consistently consistence business and IT strategies. do. Apparently accepted as a complementary view on AI's possibilities is the daily and leather new leather as an important propulsion for motivating the strategic agility of the tissue, beyond structural and time focus. It emphasizes the dynamic and mutual relevance of the use [21]. In fact, there is a good chance that the new application of AI, eventually the application for the application will appear before the standard use of AI [8, 75]. < SPAN> In addition, AI vulnerability adjusts corporate resources, skills, unrelated abilities, and simplifies the ability to mobilize. In addition, AI's weaknesses are to provide companies to reform their business processes with the help of intelligent technology and stat e-o f-th e-art algorithms. Finally, the organization constructs a business strategy by giving the decisio n-made [8, 72], the development or adjustment of business alternatives throughout the organization [5] by giving the authority to enter the potential new business area [5]. It can be executed on the target. The use of these seemingly incompatible and competing AI applications means that business can be more possible for business to detect and predict change, actively respond to changes, and consistently consistence business and IT strategies. do. Apparently accepted as a complementary view on AI's possibilities is the daily and leather new leather as an important propulsion for motivating the strategic agility of the tissue, beyond structural and time focus. It emphasizes the dynamic and mutual relevance of the use [21]. In fact, there is a good chance that the new application of AI, eventually the application for the application will appear before the standard use of AI [8, 75]. In addition, AI vulnerability adjusts corporate resources, skills, unrelated abilities, and simplifies the ability to mobilize. [37, 63]. In addition, AI's weaknesses are to provide companies to reform their business processes with the help of intelligent technology and stat e-o f-th e-art algorithms. Finally, the organization constructs a business strategy by giving the decisio n-made [8, 72], the development or adjustment of business alternatives throughout the organization [5] by giving the authority to enter the potential new business area [5]. It can be executed on the target. The use of these seemingly incompatible and competing AI applications means that business can be more possible for business to detect and predict change, actively respond to changes, and consistently consistence business and IT strategies. do. Apparently accepted as a complementary view on AI's possibilities is the daily and leather new leather as an important propulsion for motivating the strategic agility of the tissue, beyond structural and time focus. It emphasizes the dynamic and mutual relevance of the use [21]. In fact, there is a good chance that the new application of AI, eventually the application for the application will appear before the standard use of AI [8, 75].	2020	x	x	x	x
Many scholars argue that strategic agility motivates the renewal of an organization's business resources to develop a portfolio full of capabilities to execute various strategic contingencies and achieve operational capability effectiveness, business operations/alignment, improved quality, and innovation [70, 95, 138, 146, 147, 173, 178]. Furthermore, strategic agility enables companies to restructure and improve business practices and capabilities and accelerate the adoption of digital technologies [2]. Furthermore, strategic agility enables organizations to generate the best business strategies and skills required to execute their core strategies. Furthermore, agility can improve operational ambiguity and dual digitalization frameworks in operational capabilities and operational exploitation and exploitation capabilities [13, 138, 148, 179]. Therefore, companies need to be strategically agile because they need to reevaluate their current operating models and adapt as quickly as the environment, especially in the face of a pandemic like COVID-19 [146, 180, 181, 182]. Thus, the study suggests that aligning AI capabilities with business/IT strategies will enhance several business value outcomes for organizations [51, 109]. This is closely related to:	2020	x	x	x	x	x	x	x
Transformation projects that support rather than inhibit adaptive transformation skills are more likely to produce sustained results [13]. Unfortunately, as companies go through a transformation process, many barriers become entrenched within them, such as disgruntled employees, rigid decision makers, and routinized processes [34]. The extended leadership team is bound by adaptive transformation capabilities, which also help reshape the organization, its management, and its resilience [5, 86]. Adaptive transformation capabilities address such barriers to transformation and guide companies in orchestrating a balanced transformation alignment. Adaptive transformation capabilities as strategic capabilities enable companies to stabilize their transformation roadmaps and act as pillars to achieve high performance amid turbulence [34, 90]. This dynamic agility, supported by artificial intelligence capabilities, allows companies to quickly orchestrate and meaningfully adjust the launch of the next growth phase [21]. A high level of engagement allows companies to gain competitive advantage through long-term improvements and generate innovative outcomes even in challenging times [37, 159].	2021	x	x	x	x
To summarise, we present a conceptual model outlining all the above relationships. Figure 5, adapted from previous key literature, illustrates the flow of value-enhancing outcomes in the integration of AI into business/TT strategies. This framework was specifically designed to help managers assess the competitive value of complex AI investments and take a strategic position on expected linkages.	2021	x	x	x	x	x
The integration of AI with business/IT strategies was the focus of this study in order for companies to align with digital transformation and realise business value enhancement. AI research is fundamentally driven by digital transformation, challenging problems and the need for businesses to model and understand human behaviour. The importance of management information systems, which sit at the crossroads of information, business and industry, is even more significant as a result of the Fourth Industrial Revolution, especially now that digitalisation has become a necessity.	2022	x	x	x	x	x
We performed systematic reviews using specific methods that have been widely used in the past to investigate issues related to information system strategies and digital transformations. According to our survey, organizations usually experience digital transformation as a result of the development of environmental technology. Currently, it is urgently needed to connect the environment to the environment so that it can follow the new regulation framework. At the same time, integrating AI abilities with business / IT strategies is one of the important factors that consistently consistent digital transformations and improve the results of various corporate value. Specific issues, solutions, livers, and flows can be found in the context of the responsible AI governance and the use of A I-dominance in the development of AI ability. In any case, in order to provide a large competitive advantage to companies, there is a new value creation path that needs to be evaluated from various complicated perspectives by combining applied AI abilities with strategic agility. open.	2022	x	x	x	x	x	x	x
Research results show a more detailed figure of how AI affects living things. Previous research has shown that the application of AI helps the organization to acquire the adaptive organizational ability necessary to enhance the business effectiveness of the work. This study, which matches pr e-research, indicates that AI capacity integrates digital transformation consistency and ultimately play an important role in creating competitive advantage. As a result, this study revealed factors and propulsion for pursuing the improvement of the business value of AI. The survey also demonstrated that the synergistic effects exceed the unique and consistent use of AI. As a result, these two important aspects of A I-dominance have two important aspects of the tissue strategic agility, so the organization is innovative and customary AI development. Indicates the necessity of having. < SPAN> We conducted systematic literature reviews using specific methods that have been widely used in the past to investigate information related to information system strategies and digital transformations. According to our survey, organizations usually experience digital transformation as a result of the development of environmental technology. Currently, it is urgently needed to connect the environment to the environment so that it can follow the new regulation framework. At the same time, integrating AI abilities with business / IT strategies is one of the important factors that consistently consistent digital transformations and improve the results of various corporate value. Specific issues, solutions, livers, and flows can be found in the context of the responsible AI governance and the use of A I-dominance in the development of AI ability. In any case, in order to provide a large competitive advantage to companies, there is a new value creation path that needs to be evaluated from various complicated perspectives by combining applied AI abilities with strategic agility. open.	2022	x	x	x	x	x
Research results show a more detailed figure of how AI affects living things. Previous research has shown that the application of AI helps the organization to acquire the adaptive organizational ability necessary to enhance the business effectiveness of the work. This study, which matches pr e-research, indicates that AI capacity integrates digital transformation consistency and ultimately play an important role in creating competitive advantage. As a result, this study revealed factors and propulsion for pursuing the improvement of the business value of AI. The survey also demonstrated that the synergistic effects exceed the unique and consistent use of AI. As a result, these two important aspects of A I-dominance have two important aspects of the tissue strategic agility, so the organization is innovative and customary AI development. Indicates the necessity of having. We performed systematic reviews using specific methods that have been widely used in the past to investigate issues related to information system strategies and digital transformations. According to our survey, organizations usually experience digital transformation as a result of the development of environmental technology. Currently, it is urgently needed to connect the environment to the environment so that it can follow the new regulation framework. At the same time, integrating AI abilities with business / IT strategies is one of the important factors that consistently consistent digital transformations and improve the results of various corporate value. Specific issues, solutions, livers, and flows can be found in the context of the responsible AI governance and the use of A I-dominance in the development of AI ability. In any case, in order to provide a large competitive advantage to companies, there is a new value creation path that needs to be evaluated from various complicated perspectives by combining applied AI abilities with strategic agility. open.	2021	x	x
Research results show a more detailed figure of how AI affects living things. Previous research has shown that the application of AI helps the organization to acquire the adaptive organizational ability necessary to enhance the business effectiveness of the work. This study, which matches pr e-research, indicates that AI capacity integrates digital transformation consistency and ultimately play an important role in creating competitive advantage. As a result, this study revealed factors and propulsion for pursuing the improvement of the business value of AI. The survey also demonstrated that the synergistic effects exceed the unique and consistent use of AI. As a result, these two important aspects of A I-dominance have two important aspects of the tissue strategic agility, so the organization is innovative and customary AI development. Indicates the necessity of having.	2021	x	x	x
In addition, these findings contribute to our understanding of how organizations should optimize their AI resources to drive strategic agility and benefit their adoption at the strategic level, contributing to the current body of knowledge on the formation and deployment of dynamic capabilities. Moreover, our current study suggests that establishing hard-to-contest digital skills can help companies navigate challenging and volatile business environments. Thus, our study adds to the already existing body of knowledge and satisfies the demand for additional fundamental research on the role of AI in strategy. In doing so, we focus on how AI capabilities can help companies become more adaptable. Furthermore, our findings refute the notion that AI is often monolithic and unable to adapt to changes in the environment due to its long life cycle. However, the key is for companies to leverage AI to support, rather than hinder, adaptive transformation.	2021	x	x	x
In addition, the policies and goals that define and guide a company's strategy should take into account whether good AI governance has a direct impact on the organization's performance. Not understanding how unintended consequences of AI systems can affect a company's overall competitiveness is crucial to building and adopting a responsible AI regulatory framework that enhances economic value. Therefore, learning and studying the proper functioning of responsible AI governance frameworks may provide companies with an advantage over their competitors. Managers who want to incorporate ethical AI issues into their operations must first understand what is needed to do so before taking the necessary actions to build ethical AI systems. The key challenge is to reorganize organizational structures, pave the way for responsible AI research, and implement management reforms to introduce new organizational practices in the absence of an AI governance framework.	2021	x	x
It is another important thing to understand that both business interests are promoted by strategic agility, which also brings innovation and develop a functional business system and practice that utilizes digital technology. It is a theoretical contribution. This result is based on advance research and demonstrates the important roles of AI in achieving these business profits. These findings can be used as steps and visions for IS and management researchers who want to consider the usefulness of AI ability.	2021	x	x	x	x	x	x	x
This study also has many practical implications. First, policy imgers need to make coordination to invest in AI, and emphasize the creative and rather many methods that AI is already used. Strategically agileed organizations will be promoted by the synergistic effect of such a tw o-way behavior. To do so, it will be necessary to focus on processes and design continuous abilities to create abilities. This suggests that, for example, a compan y-friendly determination to look at AI through overall approaches, taking into account technology as an important factor in competitive strategy. Companies should actively invest in AI skills to further build and form a dynamic ability to respond to the decisions, optimize, and to make more dynamic abilities to respond to changes in market and demand. These actions guarantee that organizations will develop competitive advantage, achieve hig h-level organizational performance, and redefine the goals of many strategic growth and improvements. It is another understanding that	2021	x	x	x	x	x	x	x
Second, top management should use AI to increase the strategic agility of the organization. Companies can take measures to promote and mature this dynamic ability, and conduct structural improvement activities in business in all places in line with the basics of strategic agility. For example, an organization can create and test strategic alternatives and decisions based on many conditions that can occur in the future. By acting in such a proactive method and exercising many future, the ability of the organization to observe the turmoil and changes generated in the ecosystem through time, and succeed in it. Can be enhanced. This is also an advantage for this for learning and collaboration. The organization needs to formulate the optimal strategy for the selected business status, according to the second aspect of the strategy formulation and design, and determine what is needed. By separating responsibility and decisio n-making rights to ensure hig h-quality and direct decisio n-making, this process should lead to a focus on investment decisions. The decisio n-making person must achieve this by identifying and managing different thoughts and premise through a fruitful discussion and meeting with the two. < SPAN> Second, top management should use AI to enhance the strategic agility of the organization. Companies can take measures to promote and mature this dynamic ability, and conduct structural improvement activities in business in all places in line with the basics of strategic agility. For example, an organization can create and test strategic alternatives and decisions based on many conditions that can occur in the future. By acting in such a proactive method and exercising many future, the ability of the organization to observe the turmoil and changes generated in the ecosystem through time, and succeed in it. Can be enhanced. This is also an advantage for this for learning and collaboration. The organization needs to formulate the optimal strategy for the selected business status, according to the second aspect of the strategy formulation and design, and determine what is needed. By separating responsibility and decisio n-making rights to ensure hig h-quality and direct decisio n-making, this process should lead to a focus on investment decisions. The decisio n-making person must achieve this by identifying and managing different thoughts and premise through a fruitful discussion and meeting with the two. Second, top management should use AI to increase the strategic agility of the organization. Companies can take measures to promote and mature this dynamic ability, and conduct structural improvement activities in business in all places in line with the basics of strategic agility. For example, an organization can create and test strategic alternatives and decisions based on many conditions that can occur in the future. By acting in such a proactive method and exercising many future, the ability of the organization to observe the turmoil and changes generated in the ecosystem through time, and succeed in it. Can be enhanced. This is also an advantage for this for learning and collaboration. The organization needs to formulate the optimal strategy for the selected business status, according to the second aspect of the strategy formulation and design, and determine what is needed. By separating responsibility and decisio n-making rights to ensure hig h-quality and direct decisio n-making, this process should lead to a focus on investment decisions. The decisio n-making person must achieve this by identifying and managing different thoughts and premise through a fruitful discussion and meeting with the two.	2021	x	x	x
As a way to promote continuous learning and improve internal collaboration, regular reviews and performance measurement tools can also be used for monitoring progress. The managers in key positions can compare the results by using the survey results and identify the improvement. To do so, a large number of processes require the support of advanced managers and a clear strategy for integrating AI with responsibility throughout the organization. Since many companies are still in the early stages of introducing AI technology, it is important to take responsibility to take advantage of the creation of new abilities to improve performance. The AI system is complicated and costly, so it is necessary to incorporate further concerns in the overall design, but the merits can be evaluated immediately at the management and financial level.	2018	x	x	x	x
Last but not least, most companies have not yet specialized in AI development, given the complexity of the AI application. It is clear that management needs to spend more time and money on the development of AI ability. Since AI is created consistently from concept to implementation, new abilities are born from the AI team itself. In other words, related AI development teams have the opportunity and authority to change the results into useful and positive by conducting A I-based projects in accordance with the responsible AI framework. If the owner wants to maintain his competitiveness and promote future business value, he must make a plan and invest in his rivals. On the other hand, it takes time to overcome competition. To do so, a framework that clearly indicates major measures to achieve a typical AI practice, and a methodological strategy that limited all management efforts is required. < SPAN> As a way to promote continuous learning and improve internal collaboration, regular reviews and performance measurement tools can be used for monitoring of progress. The managers in key positions can compare the results by using the survey results and identify the improvement. To do so, a large number of processes require the support of advanced managers and a clear strategy for integrating AI with responsibility throughout the organization. Since many companies are still in the early stages of introducing AI technology, it is important to take responsibility to take advantage of the creation of new abilities to improve performance. The AI system is complicated and costly, so it is necessary to incorporate further concerns in the overall design, but the merits can be evaluated immediately at the management and financial level.	2020	x	x	x	x
Last but not least, most companies have not yet specialized in AI development, given the complexity of the AI application. It is clear that management needs to spend more time and money on the development of AI ability. Since AI is created consistently from concept to implementation, new abilities are born from the AI team itself. In other words, related AI development teams have the opportunity and authority to change the results into useful and positive by conducting A I-based projects in accordance with the responsible AI framework. If the owner wants to maintain his competitiveness and promote future business value, he must make a plan and invest in his rivals. On the other hand, it takes time to overcome competition. To do so, a framework that clearly indicates major measures to achieve a typical AI practice, and a methodological strategy that limited all management efforts is required. As a way to promote continuous learning and improve internal collaboration, regular reviews and performance measurement tools can also be used for monitoring progress. The managers in key positions can compare the results by using the survey results and identify the improvement. To do so, a large number of processes require the support of advanced managers and a clear strategy for integrating AI with responsibility throughout the organization. Since many companies are still in the early stages of introducing AI technology, it is important to take responsibility to take advantage of the creation of new abilities to improve performance. The AI system is complicated and costly, so it is necessary to incorporate further concerns in the overall design, but the merits can be evaluated immediately at the management and financial level.	2021	x	x	x	x	x	x	x
Last but not least, most companies have not yet specialized in AI development, given the complexity of the AI application. It is clear that management needs to spend more time and money on the development of AI ability. Since AI is created consistently from concept to implementation, new abilities are born from the AI team itself. In other words, related AI development teams have the opportunity and authority to change the results into useful and positive by conducting A I-based projects in accordance with the responsible AI framework. If the owner wants to maintain his competitiveness and promote future business value, he must make a plan and invest in his rivals. On the other hand, it takes time to overcome competition. To do so, a framework that clearly indicates major measures to achieve a typical AI practice, and a methodological strategy that limited all management efforts is required.	2021	x	x	x
Regarding the boundaries of the papers, although we searched for many variations of the relevant term, some publications related to AI, strategy, digital transformation, and business value may not have used this term or its variations in the title or abstract. Furthermore, restricting the search to business-related topics allowed us to exclude more technical publications. Furthermore, we excluded cutting-edge research written in other languages and only included papers written in English. It is noteworthy that industrial applications of AI, such as smart cities, health industry, and manufacturing, require more attention and original research comparing requirements, difficulties, and solutions across different industries.	2018	x	x	x	x	x
To summarize, the focus of our study is how digital transformation and AI can work together to produce the outcome of improved business value from a strategic perspective. Future research could carefully evaluate the alternatives of each model and evaluate the aforementioned conceptual model theoretically and experimentally. Now that we have considered the most important aspects of AI capabilities, it may be interesting for future research to group the connections and value flows generated as a result of different use cases and applications for better strategic application in different contexts. This digital age is changing rapidly, and organizations must if they do not want to be left behind by deep technology gaps.	2020	x	x	x
Incorporating AI solutions into business strategies can bring significant benefits to companies, including increased efficiency, improved decision-making, and new revenue streams. However, successful AI adoption requires a well-designed plan and a clear understanding of the technology and its capabilities.	2019	x	x	x
First of all, it is essential to conduct an in-depth analysis of business needs and goals to identify areas where AI can be most effectively utilized. Doing so will help identify the specific areas where AI can bring the most value and set clear, measurable goals for adoption. Once the areas are identified, it is important to have a clear understanding of the available AI technologies and their capabilities to align them with the identified business needs.	2019	x	x	x	x
In order to confirm that the AI solutions to be introduced are consistent with the overall business strategy, it is important to create a team that is a division of representatives of various departments such as IT, operation, and marketing. The team should be responsible for defining the range of the project, setting a budget, and monitoring the progress. In addition, the appropriate data management and governance infrastructure is essential for the success of the AI project. Without accurate and reliable compatible data, the AI system cannot be operated effectively. Therefore, it is important to invest in the data management and governance infrastructure to guarantee that the data used in the training and operation of the AI system is accurate and reliable, and that it is compliant with the relevant regulations.	2022	x	x	x	x	x	x	x
In addition, it is important to measure the performance of AI solutions and the impact on business, set a clear indicator according to the project goal, continuously monitor and evaluate the performance of the AI solution, and make adjustments as needed. It is. In order to foster the culture of experiments and innovation, the organization should encourage employees to test and propose new ideas and solutions based on AI. This helps to identify new opportunities and improve the overall effectiveness of AI solutions. In addition, we constantly understand the latest trends in AI and related technologies, adjust the strategy accordingly, plan potential ethical and legal considerations such as data privacy and prejudice, and comply with laws and regulations. It is important to maintain social trust. By doing so, the introduction will be implemented correctly and the solution will be adjusted according to corporate needs. By following these recommendations, companies can successfully integrate AI solutions into business strategies and take the way to enjoy benefits. < SPAN> In order to make sure that the AI solutions to be introduced are consistent with the overall business strategy, it is important to create a team that is a department that consists of representatives of various departments such as IT, operation, and marketing. It is. The team should be responsible for defining the range of the project, setting a budget, and monitoring the progress. In addition, the appropriate data management and governance infrastructure is essential for the success of the AI project. Without accurate and reliable compatible data, the AI system cannot be operated effectively. Therefore, it is important to invest in the data management and governance infrastructure to guarantee that the data used in the training and operation of the AI system is accurate and reliable, and that it is compliant with the relevant regulations.	2019	x	x	x
In addition, it is important to measure the performance of AI solutions and the impact on business, set a clear indicator according to the project goal, continuously monitor and evaluate the performance of the AI solution, and make adjustments as needed. It is. In order to foster the culture of experiments and innovation, the organization should encourage employees to test and propose new ideas and solutions based on AI. This helps to identify new opportunities and improve the overall effectiveness of AI solutions. In addition, we constantly understand the latest trends in AI and related technologies, adjust the strategy accordingly, plan potential ethical and legal considerations such as data privacy and prejudice, and comply with laws and regulations. It is important to maintain social trust. By doing so, the introduction will be implemented correctly and the solution will be adjusted according to corporate needs. By following these recommendations, companies can successfully integrate AI solutions into business strategies and take the way to enjoy benefits. In order to confirm that the AI solutions to be introduced are consistent with the overall business strategy, it is important to create a team that is a division of representatives of various departments such as IT, operation, and marketing. The team should be responsible for defining the range of the project, setting a budget, and monitoring the progress. In addition, the appropriate data management and governance infrastructure is essential for the success of the AI project. Without accurate and reliable compatible data, the AI system cannot be operated effectively. Therefore, it is important to invest in the data management and governance infrastructure to guarantee that the data used in the training and operation of the AI system is accurate and reliable, and that it is compliant with the relevant regulations.	2021	x	x	x	x	x
In addition, it is important to measure the performance of AI solutions and the impact on business, set a clear indicator according to the project goal, continuously monitor and evaluate the performance of the AI solution, and make adjustments as needed. It is. In order to foster the culture of experiments and innovation, the organization should encourage employees to test and propose new ideas and solutions based on AI. This helps to identify new opportunities and improve the overall effectiveness of AI solutions. In addition, we constantly understand the latest trends in AI and related technologies, adjust the strategy accordingly, plan potential ethical and legal considerations such as data privacy and prejudice, and comply with laws and regulations. It is important to maintain social trust. By doing so, the introduction will be implemented correctly and the solution will be adjusted according to corporate needs. By following these recommendations, companies can successfully integrate AI solutions into business strategies and take the way to enjoy the benefits.	2021	x	x	x	x
AI is attracting attention as a revolutionary technology that has the potential to change business. This survey focuses specific ways to navigate the digital environment and achieve goals by integrating AI into business strategies and IT strategies. In this survey, integrating AI abilities into business / IT strategies is an important means of realizing digital transformation alignment, and the synergistic effects of innovative AI development and customary AI development are tw o-way synergistic effects. He has discovered that it exceeds the profit alone.	2021	x	x	x
One of the important things from this study is that AI is not just a tool, but a power that can form the essence of the organization. When companies try to use AI power, they need to be aware of how to use this technology to create new opportunities and release new forms. To do so, not only deep understanding of technology itself, but also the willingness to challenge established concepts and develop unknown areas.	2021	x	x	x	x
In this sense, integrating AI with business strategies and IT strategies can be considered as a form of alchemy that converts data and technology resources into new value and competitiveness. This process requires a deeply understanding of the basic principles of AI, but also needs to take experiments and risks.	2022	x	x
After all, the survey emphasizes the importance of accepting AI possibilities to succeed in the digital age. When companies continue to run at the forefront of the times and try to adapt to rapidly changing situations, they need to challenge the status quo and explore unknown areas that AI opens up. By doing so, new values can be brought out and a competitive advantage that is difficult to duplicate is difficult.	2021	x	x	x	x	x
Conceptualization, n. Ρ .; Method theory, N.-A. P. P. K., Survey, N.-A. R.; Curation Data, F. K .; Creating-creation, N.-A. P. And F. K.; F. K .; Visualization, N.-A.	2021	x	x	x
This study has not received external funding.	2021	x	x	x	x	x	x
No data reports this study.	2022	x	x	x	x	x
The authors have declared that there is no conflict of interest.	2020	x	x	x	x	x	x	x
Table A1. Matrix table. source	2022	x	x	x	x	x
Table A1. Matrix table. Source: Independent edit.	2020	x	x	x	x	x	x	x
concept	2020	x	x	x	x
Biology artificial intelligence abilities	2019	x	x	x	x	x
Realization of digital transformation and improving business value by integrating AI and business/IT	2022	x	x	x	x
No.	2019	x	x	x	x	x	x
Author's name	2021	x	x	x	x
year	2018	x	x	x
AI both dominant properties	2019	x	x	x	x
Conceptual concept of AI ability	2014	x	x
AI resource orchestration	2021	x	x	x	x
AI governance	2020	x	x	x
Strategic agility	2020	x	x	x
Role of AI in strategic components	2022	x	x	x	x
AI business value driver and improvement of results	2019	x	x	x	x	x	x	x
KitSIOS and Kamariotou [1]	2021	x	x	x	x	x
Zhou and Li [2]	2020	x	x	x	x
Kar et al. [3]	2015	x	x	x	x	x	x
Van de Wetering et al. [4]	2021	x	x	x
Ransbothatm et al. [5]	2017	x	x	x	x
Brynjolfsson and Mcafee [6]	2020	x	x	x
Trunk et al. [7]	2017	x	x
Block and Vongheheim [8	2019	x	x	x	x
Al Surmi et al. [9]	2016	x	x	x
Chowdhury et al. [10]	2019	x	x	x
Makowski and Kajikawa [11]	2020	x	x	x	x	x	x	x
Jalahi [12]	2020	x	x	x	x
Van de Wettering [13]	2020	x	x	x	x	x
Mikaref and Gupa [14]	2021	x	x	x	x	x
Kanghot and Clear [15]	2022	x	x	x	x
Wamba Tagimjie and others [16]	2019	x	x	x	x
Hefner and others [17]	2018	x	x	x
Dwivedi and others [18]	2019	x	x	x	x
Truong and Papagiannidis [19]	2023	x	x	x	x
Füller and others [20]	2020	x	x	x	x
Van de Wettering [21	2017	x	x	x
Majhi et al. [22]	2019	x	x	x
Van de Vettering [23]	2022	x	x	x	x
Benbya et al. [24]	2022	x	x
Belente et al. [25	2022	x	x	x	x
Borgez [26]	2022	x	x	x	x
Kading [27]	2021	x	x	x
Tschang and almirall [28]	2019	x	x	x	x
Dabenport [29	2019	x	x	x
Chatage et al. [30]	2022	x	x	x	x	x
Michula and Pani [31]	2021	x	x	x	x
Fedler and Regner [32]	2020	x	x	x	x	x	x
Da Ben Port and Ronanki [33]	2022	x	x	x	x	x
Barnare [34]	2022	x	x	x	x	x
Amershi et al. [35]	2020	x	x	x
Frank and others [36]	2019	x	x	x
Krakowski et al. [37]	2019	x	x	x	x
Butcher and Berize [39]	2022	x	x	x	x
Yigit and Kanbach [42]	2015	x	x	x	x
Bags and others [43	2019	x	x	x	x
Collins and others [44]	2019	x	x	x	x
Yu καιmoon [45].	2021	x	x	x	x	x
Ο AWWAD et αϊ. [48].	2022	x	x	x	x
Çebeci [50].	2019	x	x	x
Enholm et αϊ. [51] zuiderwijk et αϊ.	2022	x	x	x	x	x	x	x
Zuiderwijk et αϊ. [52] Grover et αϊ.	2019	x	x	x	x	x	x
Grover et αϊ. [53] zuiderwijk et αϊ.	2018	x
Caner κα BHATTI [54].	2019	x	x	x	x	x	x
Smacchia κα za [55].	2020	x	x	x	x	x
DI VAIO et αϊ.	2012	x	x	x	x
Dhamija κα BAG [57].	2015	x
Haenlein κα Kaplan [58].	2014	x	x	x	x	x	x
Çark [59]	2017	x	x	x	x
ShRESTHA et αϊ. [60] ShRESTHA et αϊ.	2022	x	x	x	x	x
ShRESTHA et αϊ. [61] Von Krogh [62].	2017	x	x	x	x	x	x
von krogh [62].	2018	x	x	x
Agrawal et αϊ. [63] Whittington [63].	2022	x	x	x
Whittington [65].	2019	x	x	x	x	x
Krogh et αϊ. [66] liu et αϊ.	2018	x	x	x
Liu et αϊ. [67] CARTER et αϊ.	2017	x	x	x	x
CARTER ET αϊ. [69] Carter et αϊ.	2022	x	x	x	x
Sestino και mauro [71].	2022	x	x	x
Warner καιwäger [72].	2016	x	x
Huang και rust [73].	2022	x	x	x	x	x	x
Wamba-taguimdje et αϊ.	2017	x	x	x	x	x
Ο Papachroni et αϊ.	2021	x	x	x	x	x	x
VAN DE WETERING κα ι VERSENDAL [76].	2018	x	x	x	x	x	x
DIAZ-FERNANDEZ et αϊ.	2020	x	x	x	x	x	x
Ο PASCHEN et αϊ. [79] Phan et αϊ.	2018	x	x	x
Phan et αϊ. [80] Gabrio και	2021	x	x	x	x	x
Gabrio κα lin [81].	2021	x	x	x	x	x	x	x
Hengstler [82].	2022	x	x
Barro κα ι davenport [83].	2020	x	x	x	x	x	x
Ο Desouza et αϊ. [84] Reis et αϊ.	2020	x	x	x	x	x
Reis et αϊ. [85] Alsheibani et αϊ.	2021	x	x	x	x	x	x
Alsheibani et αϊ. [86] sjödin et αϊ.	2022	x	x	x	x	x

Sjödin et αϊ. [87] Weber et αϊ.

References

Weber et αϊ. [88] aydiner et αϊ.
Aydiner et αϊ. [90] bamel et αϊ.
Bamel et αϊ. [95] mikalef et αϊ.
Mikalef et αϊ. [98] Uren et αϊ.
Uren et αϊ. [101] kurniawan et αϊ.
Kurniawan et αϊ. [102] Carnes et αϊ.
Carnes et αϊ. [103] mikalef et αϊ.
Mikalef et αϊ. [104] HO et αϊ.
HO et αϊ. [106] fosso Wamba [106].
Fosso Wamba [108].
Lee et αϊ. [109] VAN DE Wetering et αϊ.
VAN DE WETERING ET αϊ. [111] Papagiannidis et αϊ.
Papagiannidis et αϊ. [112] FOUNTAINE et αϊ.
FOUNTAINE ET αϊ.
Lichtenthaler [114].
Papagiannidis et αϊ. De Laat [115].
De Laat [116].
KRUHSE-LEHTONEN κα hofmann [118].
Schneider et αϊ. De Laat [119].
CHEN ET αϊ. [120] Smit et αϊ.
Smit et αϊ. [121] Alsheibani et αϊ.
Alsheibani et αϊ. [122] Abraham et αϊ.
Abraham et αϊ. [123] Abraham et αϊ.
Ashmore et αϊ. [124]
Sheller [125]
Clark [126]
Caplan κα Hen Line [129]
Minkkien and others. [131]
LEDRO et al. [132]
Luwakatare and others [133]
Wang et al. [134]
Dignum [135]
Brosovic [136]
Van de Wettering and others [138]
Haar House and Lending [140]
Combe et al. [144]
Radomska et al. [145]
Kortmann et al. [147]
Van de Vettering and Mikalev [148]
Mataramaki, M. J. and Joensu Salo [149].
Yi et al. [150]
Chang and John [151]
Avramov et al. [152]
Kou et al. [153]
Lee et al. [155]
Suominen et al. [156]
Khan et al. [157]
Mishra et al. [159]
Gomez Uribe [162]
Dryidakis [163]
Brin Jolfson and Mitchell [164]
Moroi and Marwara [165]
Benites and others [166]
COOMBS et al. [168]
Menon et al. [169]
TROCIN et al. [170]
CHALMERS and others. [171]
Lui and others. [174]
Rialti et al. [176]
VERGANTI et al. [177]
MIROSHNYCHENKO et al. [179]
Li et al. [180]
Base: Symbol's 'X' is used to show the specific concept described in each quoted document.
KitSIOS, F.? Artificial intelligence and business strategy for digital transformation: research topic. Sustainability 2021, 13, 2025. [Google Scholar] [CrossRef].
RelationResult. relationResult. relationResult. relationResult. relationResult. relationResult. relationResult. relationResult. relationRelationResu lt. RelationResult Lee C. B. [Google School] [CrossRef].
KAR, S; Promotion of artificial intelligence introduction and modelization of barriers: insight from a strategic management perspective. In this situation, how will the introduction of artificial intelligence affect corporate management under such circumstances? [Google School] [CrossRef].
Van de Wetering, R.;. Hendrickx, T; Brinkchemper, S; Dynamic Caucy, Innovation, and Structural impacts on the interests of the tissue: From the viewpoint of variations and FSQCA. Sustainability 2021, 13, 5414. [Google School] [CrossRef].
Ransbotham, S. KHODABANDEH, S .; Fehling, R .; LafOUNTAIN, B .... Kiron, D. AI. Technical report. Mit Sloan Management Review and Boston Consulting Group: Boston, Ma, USA, 2019. [Google Scholar].
Brynjolfsson, E ...; The Business of Artificial Intelligence. Harv. Bus. Rev. 2017, 7, 3-11.
Trunk, A ...; Birkel, h; Hartmann, E. About the current situation of human and artificial intelligence for strategic organizational decisions. BUS. Under these circumstances, the trunk states the current status of "combination of human and artificial intelligence in strategic organization decisio n-making." [Google School] [CrossRef].
BROCK, J. K.-U.; Von Wangenheim, F. Demystify AI: Which Digital Transformation Leaders CAN TEACH You ABOUT PRAGMATICIAL INTELLIGENCE. Alifornia Management. Rev. 2019, 61, 110-134. [Google School] [CrossRef].
Al Sourmi, A. Basiri, M; Koliousis, I. A I-based decisio n-making: A combination of strategies to improve operations performance. In such a case, use [Google School], CROSSREF, CROSSREF, or CROSSREF. [Google School] [CrossRef].
Chowdhury, s.; Dey, p.;. Joel-edgar, s ;; bhattacharya, s.; Rodriguez-espindola, o. ;; Abadi, A; Release the value. Buito. Resource. Manag. Rev. 2022, 33, 100899. [Google School] [CrossRef].
MAKOWSKI, R. T; kajikawa, y. Automatio n-led innovation management? Innovation-Automation-To the strategy cycle. Technol. Forecast. SoC. Chang. 2021, 168, 120723. [Google School] [CrossRef].
JARRAHI, M. H. Artificial intelligence and the future of work: Symbiosis of human and artificial intelligence in organizational decisions. Bus. Horiz. 2018, 61, 577-586. [Google Scholar] [CrossRef].
Van de Wetering, R; Initial interest, adaptive transformation, and impact on business performance in the turbulent era. Under such circumstances, Papagiannidis, S., Alamanos, E., Gupta, S., Dwivedi, Y. K., M., M., M., M., M., M., M., M., M., M., K., K., K., K., K., K., K., K., K., M. Pappas, I. O., Eds. Computer science. Springer International Publishing. ISBN 978-3-031-15341-9. [Google School].
Ability to artificial intelligence: Demonstration research on conceptualization, measurement calibration, organizational creativity and impact on corporate performance. [Google School] [CrossRef].
Artificial intelligence and machine learning as a business tool: Framework to diagnose the possibility of value destruction. Bus. Horiz. 2020, 63, 183-193. [Google School] [CrossRef].
Wamba-taguimdje, S.-L .; fosso wamba, s; Kara Camjug, J. R; Artificial Intelligence (AI) The business value of the transformation project utilizing AI. Bus. Process Management. J. 2020, 26, 1893-1924. [Google School] [Crossr].
Haefner, n. ;; parida, v .; O. artificial intelligence and innovation management: Review, Context and Research Agenda✰. 0392. [Google Scholar ] [CrossRef].
Dwivedi, Y. K; Hughes, L.; Ismagilova, E.; Aarts, G.; Coombs, C.; Crick, T.; Duan, Y.; Dwivedi, R; Edwards, J.; Eirug, A.; et al. Artificial intelligence (AI): a multidisciplinary perspective on emerging issues, opportunities, and challenges for research, practice, and policy. [Google Scholar] [CrossRef].
Truong, Y.; Papagiannidis, S. Artificial Intelligence as an Enabler for Innovation: a Review and Future Research Agenda. Technol. Forecast. Soc. Chang. 2022, 183, 121852. [Google Scholar] [CrossRef].
Füller, J.;. Hutter, K.; Wahl, J; Bilgram, V; Tekic, Z. How artificial intelligence will revolutionize innovation management-Perceptions and adoption preferences of innovators based on artificial intelligence. Technol. Forecast. Under these circumstances, how will artificial intelligence revolutionize innovation management? [Google Scholar] [CrossRef].
Wetering, R. V. D. The impact of artificial intelligence ambidexterity and strategic agility on operational ambidexterity. In Proceedings of the PACIS 2022 Proceedings, Taipei, Sydney, 5-9 July 2022. 153. Available online: https://aisel. aisnet. org/pacis2022/153 (accessed January 1, 2023).
Majhi, S. G; Mukherjee, A; Anand, A. Business Value of Cognitive Analytics Technology: A Dynamic Capabilities Perspective. VINE J. Inf. Know. Manag. syst. [Google Scholar] [CrossRef].
Achieving Digital-Driven Patient Agility in the Big Data Era. Computer Science. Springer International Publishing. ISBN 978-3-030-85446-1. [Google Scholar].
Benbya, H.;. Pahidi, S.; Special Issue Editorial: Artificial Intelligence in Organizations: Implications for Information Systems Research. (Abstract) Implications for Information Infrastructure Research. [Google Scholar] [CrossRef].
Berente, N.; Gu, B.; Recker, J.; Santhanam, R. Managing Artificial Intelligence. 2021, 45, 1433-1450. [Google Scholar]
Borges, A. F. S.; Laurindo, F. J. B.; Spinola, M. M.; Goncalves, R. F..; Strategic Use of Artificial Intelligence in the Digital Era: A Systematic Literature Review and Future Research Directions. [Google Scholar] [CrossRef].
Ked, C. Understanding the interaction of artificial intelligence and strategic management: Looking back on 40 years of research. (1) Understanding the interaction of artificial intelligence and strategic management: Looking back on 40 years of research. [Google School] [CrossRef].
TSChang, F. T.;. Almirall, E. Autumn as an automation of growth: Impact on employment. Acad. Prospect. 2021, 35, 642-659. [Google School] [CrossRef].
From analytics to artificial intelligence. J. Bus. Anal. 2018, 1, 73-80. [Google Scholar] [CrossRef].
ChatterJee, S.; Knowledge Management in Business IMPROVement: Nightly Management in Business Process Improvement: Introduction of AI-CRM-Km system Interpretation framework to succeed. Bus. Process Management. J. 2020, 26, 1261-1281. [Google School] [CrossRef].
Mishra, a. n; pani, a business Value Roadmap for Artificial. CrossRef].
Fadler, M .; Legner, C. Towards Governance of Big Data and Analytics: Redefining Structural Governance Mechanisms. Red definition of Sensemekanism. [Google School].
Debenport, T. H.; Ronanki, R. Artificial intelligence for the real world. Harv. Bus. Rev. 2018, 96, 108-116. [Google Scholar].
BARNEA, A. How Will AI Intelligation And Decision Making Change? J. Intell. Collar Button.
Amershi, S. Begel, A; Bird, C .; Deline, R .; Gall, H. Camar, e; nagappan, n. ;;; Nushi, Nushi, Nushi, B .; Zimmermann, T. Software Engineering for Machine Learning: a Case: a Case Study. Proceedings of the 2019 IEEE/ACM 41st International Conference On Software Engineering: Software Engineering in Practice (ICSE-SEIP) ADA, May 25-31, 2019. 291-300. [Google Scholar].
Frank, M. Autor, d. ;; bessen, j. e ;; brynjolfsson, e .; c.; Deming, d. j.; Feldman, m .; Lobo [Google School] [CrossRef] [CrossRef] [Google School] [CrossRef].
Strategy. Direc. J. 2022, Smj. 3387. [Google School] [CrossRef].
RAISCH, S; Birkinshaw, j.; Probst, g; Gh; bidgot of tissue: Balance between exploitation and search for continuous performance. Organ. Organized bidirectional: Balance between exploitation and exploration to achieve continuous performance. [Google School] [CrossRef].
Butcher, J; What is the current status of artificial intelligence governance in the world? RUSI J. 2019, 164, 88-96. [Google School] [CrossRef].
BHARADWAJ, A Resource-Based Perspective on INFORMATION TECHNOLOGY CAPABILITY AND FIRM PERFORMANCE. [Google School] [CrossRef] [CrossRef] [Google School] [CrossRef] [Google School] [CrossRef]
Kohli, R .; GROVER, V. Business value: Essays for expanding research guidelines in accordance with the times. J. Assoc. System. 2008, 9, 23-39. [Google School] [CrossRef].
Yigit, A.? The importance of artificial intelligence in strategic management: Review of literature. Int. j. strategy. 2021, 21, 5-40. [Google Scholar] [CrossRef].
CHANDA, s.; Gupta, s;. Kumar, a. ;; In this way, "integrated artificial intelligence framework for knowledge creation and B2B marketing rational decisions" improves corporate performance. Let me do it. Ind. Mark. Direc. 2021, 92, 178-189. [Google scalar] [Ref].
Collins, c .; dennehy, d .; Conboy, k .... Artificial intelligence in information system research: systematic library and research issues. 2021, 60, 102383. [Google School] [CrossRef].
YU, J; Digital strategic orientation has an effect on organizational performance through digital capability. Sustainability 2021, 13, 9766. [Google School] [CrossRef].
Teece, D. J .; Pisano, g .; SHUEN Strategy. Direc. J. 1997, 18, 509-533. [Google Scholar] [CrossRef].
ESHIMA, Y ...; Corporate growth, adaptable ability, entrepreneurial. Strategy. Direc. J. 2017, 38, 770-779. [Google School] [CrossRef].
AWWAD, A. S. ABABNEH, O. A.??? LEXIBILITY: The Case of The Jordanian It Sector. Sphere. J. Flex. System. Direc. 2022, 23, 31 5-330. [Google Scholar] [CrossRef].
Watson, R. T.; Webster, J. Analyzing the past and preparing for the future: Load map of version 2. 0 to write literature reviews. 0. J. Decis. System. 2020, 29, 129-147. [Google Scholar] [CrossRef].
Applying artificial intelligence in the management information system: A comprehensive systematic review from a business analysis perspective. Intell. THEORY APPL. 2021, 1, 25-56. [Google School].
Enholm, I. m; papagiannidis, e; mikalef, p; artificial intelligence and business value. Int. 2021, 24, 1709-1734. [Google School] [Crossr].
Zuiderwijk, a.;. Meaning of artificial intelligence in public rule: systematic literature review and research topic. (Note 1) This book was created with reference to "the way of governance utilizing artificial intelligence" (Nihon Keizai Shimbun Publisher). [Google School] [CrossRef].
Grover, P.;. Understanding the introduction of artificial intelligence in management management: Academic literature reviews and insights from social media discussions. Anna. OPPER. RES. 2022, 308, 177-213. [Google School] [CrossRef].
Caner, S; concept framework on the business strategy of artificial intelligence. (1) Conceptual framework on the business strategy of artificial intelligence.
A concept framework on the definition of a business strategy of artificial intelligence. [Google School].
Di Viio, A.; Paladino, R; Hassan, R; Artificial Intelligence and Business Model: Systematic Literature Review J. Bus. Using artificial intelligence under such circumstances What are the possibilities of the business model? [Google School] [CrossRef].
Dhamija, TQM 2020, 32, 869-896. [Google Scholar] [CrossRef] .
Haenlein, m.;. History of artificial intelligence: The past, present, and future of artificial intelligence. California Manag. Rev. 2019, 61, 5-14.
Digital conflict in strategic business management. In Conflict Management in Digital Business; özsungur, F., EMERALD PUBLISHING LIMITED: Bingley, UK, 2022; PP. Cholar].
SHRESTHA, y. r.;; California Manag. Rev. 2019, 61, 66-83. [Google School] [CrossRef].
SHRESTHA, y. r.; In this way, deep learning expands the potential of deep learning in organizational decisio n-making. [Google School] [CrossRef].
Von Krogh, G. Artificial Intelligence In Organizations: New possibility of phenomena-based theory. ι έ έδση].
Agrawal, α .; Gans, j .; Goldfarb, A. Economic Policy for Artificial Intelligence. Innov. 2019, 19, 139-159.
Jarzabkowski, ρ .; SEIDL, D. STRATEGIZING: The ChallenGes of a Practice Perspection. ] [CrossRef].
Whittington, Strategy and Strategy-A s-practice πλαώαώαήά άΩ άω άω άω άω άω Olar] [CrossRef].
Σ σ σατmaιγ; Oxford University Press: OXFORD, UK, 2021; σ. 625-646. ISBN 978-0-19-009088-3. [Google School].
Artificial intelligence impact on technological innovation: Artificial intelligence effects on technological innovation: Evidence from panel data in China. Τ π.
Benner, M. J; Exploration, exploitation, process management: Productivity dilemma reconsideration. Under such circumstances, it is necessary to overcome the "productivity dilemma" in order to overcome the "productivity dilemma". [Google School] [CrossRef].
Carter, M.; Peter, S. glover, V. Identity Informatics: An important determination factor for IT ability and explorable MIS Q. [Google School] [CrossRef].
How does IT interest affect the flexibility of the tissue? Inf. RES. 2015, 26, 398-417. [Google Scholar] [CrossRef].
Sestino, a.; 16-29. [Google School] [CrossRef].
Warner, K. S. R.; M. Building Dynamic Capabilities for Digital Transformation: a conGoing Processes of Strategic Renewal. 26-349. [Google School] [CrossRef].
Huang, M. H.; Rust, R. T. ARTIFICIAL INTELLICIAL in Marketing. J. Acad. J. Acad. Strategic framework of intelligence. [ Google School] [CrossRef].
Wamba-taguimdje, Wamba, S. F ...; Kamdjoug, J. R. K.; IC Orientation Processes. in Digital Business Transformation? Agrifoglio, R , Lamboglia, R., MANCINI, D., RICCIARDI, F., eds. Lecture Notes in INFORMATION SYSTEMS and Organization. Springer International Publishing. CHAMBERS, CHAMBERS OF COMMERCE, Switzerland, Chamber of Commerce, Switzerland Chamber of Commerce. 3-18. ISBN 978-3-030-47354-9.
Papachroni, a.;. Heracreus, L; PAROUTIS, S. Paradox Lens both tissue and the construction of an unprecedented research topic. J. Applio. Sci. 2015, 51, 71-93. [Google Scholar] [CrossRef].
Van De Wetering, R.; J. INFORMATIONAL AMBULATORY SKILLS, Digital Dynamic and KnowLedge Processes as asssesses of Patient. TY: An Empirical Study. JmirX Med. 2021, 2, E32336. [Google School] [CrossRef].
DIAZ-FERNANDEZ, PASAMAR-CARERA, R. Human Capital and Human Resource Management to AchieveVE AmbideextRous Learning: a Structure spective. Bus brq. res. Q. 2017, 20, 63-77. [Google School] [CrossRef].
Investigating innovation, extracting innovation and performance: Effect of organizational precedence factors and environmental mediation factors. Under such circumstances, it may affect corporate management. [Google School] [CrossRef].
Paschen, j .;;; filson, m .; J. J. Cooperative Intelligence: Horiz. 2020, 63, 403-414. [Google Scholar] [CrossRef].
Phan, R.; Wright, M.; Lee, S.-H. Robots, artificial intelligence and work. Acad. manag. perspective. 2017, 31, 253-255. [Google Scholar] [CrossRef].
Garbuio, M.; Lin, N. Artificial Intelligation as a Development ENGINE FOR HEALTHCARE STARTUPS: California Manag. 9-83. [Google Scholar] [CrossRef].
Hengstler, m.;. Applied Artificial and Trust-the case of Autonomous Vehict and Medical Assistance Devices. FORECAST. 2016, 105, 105-120. [Google School] [CrossRef].
Barro, s.; Davenport, T. HUMANS and Machines: Human and Machines: Partners in Innovation. Mit Sloan Manag. Rev. 2019, 60, 22-28.
Desouza, K. O.; Dawson, G. S.; D. Design, development, deployment of artificial intelligence systems: lessons learned for public sector and public sector. bus. Horizontal. 2020, 63, 205-213. [Google Scholar] [CrossRef].
REIS, C.; Ruivo, P.;. Such evaluation of value drivers by machine learning. [Google School] [CrossRef].
ALSHEIBANI, S. A.; CHEUNG, Y. Messom, D. C; Six steps to create value from artificial intelligence. Amcis Proc. Available Online: https: // Aisnet. Org/amcis2020/ADV_INFO_SYSTEMS_RESEARCH/ADV_INFO_INFO_SYSTEMS_RESEARCH/1 Anuary 2023).
Sjödin, d .; parida, palmié, m.; ND Feedback Loops. In this way, AI is the business model innovation Is it possible? [Google School] [CrossRef].
Weber, M. Engert, M.; Schaffer, N.; Weking, J; J; Krcmar, H. AI Coping-. System in consideration of difficulties and data dependence in AI. Previous. 2022, 1-21. [Google Scholar] [CrossRef].
Melville, n.; Gurubaksani, v; Initial effects of information technology and productivity: The role of concentration and industrial dynamism. Summary Support System. 2007, 43, 229-242. [Google Scholar] [CrossRef].
Aydiner, A. S.;. Tatgle, E; Bayraktar, E.; Delen, D. Business Analytics and Firm Performance J. Bus. RES. 2019, 96, 228-237 . [Google Scholar] [CrossRef].
Wade, m.;. Review: The Research of Promotion-Based Information Systems EARCH. 2004, 28, 107-142. [Google School] [CrossRef].
Market, supply chain management and sustainable competitive advantage: the validity of resourc e-based theory. J. Supply Chain Management. 2012, 48, 3-6. [Google Scholar] [CrossRef].
Resourc e-based competitive advantage theory: Review of resourc e-based views for 10 years. J. Manag. 2001, 27, 643-650. [Google School] [CrossRef].
Bromiley, R.; Rau, D. Operations Managements Management-Based Projection: Another View. J. Oper. Direc. 2016, 41, 95-106. .]
Bamel U. K; Organizational resources, KM processing abilities, strategic flexibility: viewpoint of dynamic resource capability. J. Knowl. Direc. 2018, 22, 1555-1572.
GUPTA, m .; George, J. F. Towards Developing a Big Data Analytics Capability. Direc. 2016, 53, 1049-1064.
Ravichandran, t.; C. Impact of Information Systems ResourceS and Capability ON FIRM PERFORMANCE: A Resource-Based Perspection. ANAG. System. 2005, 21, 237-276. [Google Scholar] [CrossRef].
Mikalef, p .; fjørtoft, s. o.; Business information system workshop. Abra Movic, W.; Cortuero, R., Eds. Business Information Processing Lecture Note. Springer International Publishing. Chamber's, Chamber's, Chambers, Switzerland, Chamber's, Switzerland, Chambers. 409-416. ISBN 978-3-030-36690-2. [Google School].
Morgan, N. A., Voliz, D. W., Schlegelmirhi, B. B. Relationship between business and performance: Rewards of reward and resource alternative. Ind. Mark. 2006, 35, 621-633. [Google Scholar] [CrossRef].
SEDDON, R. B. suggestions for strategies are research on corporate resourc e-based theory: considered. J. Strategy. System. 2014, 23, 257-269. [Google Scholar] [CrossRef].
Uren, v.? Technical ladies and organizations for AI adoption: Ellocare research. 2023, 68, 102588. [Google Scholar] [CrossRef].
Kurniawan, r.;. Manurung, A. H. Hamsal, m.; Bij 2020, 28, 517-555. [Google Scholar] [CrossRef].
Culturalism for Karns, C. M.; Kiriko, F. Hitt, M. A; Huh, D. W.; Larg e-scale project. 2017, 50, 472-486. [Google School] [CrossRef].
Mikaref, P. Burla, M. Reccos, G; Big Data Anniversary: Dynamic Caucan's intermediary role and the easing effect of the environment. Br. J. MANAG. 2019, 30, 272-298. [Google School] [CrossRef].
Garbuio, m.;. J. Manag. 2011, 37, 1444-1463. [Google School] [CrossRef].
HO, L. T .; Gan, C; Artificial intelligence and corporate performance: Machine intelligence protects companies from risks? RelationResult "Artificial intelligence and corporate performance: Machine intelligence protects companies from risks? [Google School] [CrossRef].
Sirmon, D. G.;. Hitt, M. A; Ireland, R. D. Dynamic Corporate Resources Management: Look inside the Black Box. 2007, 32, 273-292. [Google Scholar] [CrossRef] [Green Publication].
Impact of artificial intelligence assimilation on corporate performance: Interview with organizational agility and customer agility. Int. J. Inf. Manag. [CrossRef].
Lee, M. C. M.; Lui, A. K. H.; Lui. E ICIS 2022, Copenhagen, DenMark, Decementer 9-14, 2022. Available Online : https: // aisel. Aisnet. Org/Icis2022/ai_business/ai_business/3/(Accessed January 1, 2023).
Sirmon, D. G.;. Hitt, M. A; Ireland, R. D.; J. Manag. [Google School] [CrossRef].
Information Technology-Traditional Agility, Patient Service, and Market Performance: Distributed and FSQCA approach. Sustainability 2022, 14, 4371. [Google School] [CrossRef].
Practice of παπααγιαπα α .; enholm, ι .; Mikalef, p .; Krogstie, J. AI Governance: a Revelature case Study. A ETHICAL AND INCLUSIVE DIGITITIZED SOCIETY; DENNEHY, D. , GRIVA, A., Pouloudi, N., Dwivedi, Y. K .. D, 2021; όμο 12896, σ. 208-219. ISBN 978-3-030-85446- 1. [Google School]
FOUNTAINE, τ.; mcCarthy, βόμηση τγα νσμύύαιαιαιαιαια αωό AI. Scholar].
LichtentHaler, u. An intelligence-Based View of Firm Performance: Get profit from artificial intelligence. J. Innov. Manag. 2019, 7, 7-20. [Google Scholar] [CrossRef].
Papagiannidis, E; Mikaref, P; P; Krogogstie, J.; Conboy, K. Responsible AI Governance to competitive performance: Bragging role in knowledge management abilities. About the role of digital technology in the formation of post parallel world. Papajenidis, S., Alamanos, E., S., S., Dwider, Y. K., Menti Maki, M., Papas, I. O. Computer science lecture record. Springer International Publishing. Volume 13454, PP. ISBN 978-3-031-15341-9. [Google Scholar].
De Laat, R. B. de Lat, R. B. Responsible AI: In principle, implementation, regulation? Friend. Technology 2021, 34, 1135-1193.
BORGMAN, H. HEIER, H .; Bahli, B .; Pointing "I" in IT governance and crossing "T": A new challenge of information governance. In Proceedings of the 2016 Hawaii International Conference On System Science (HICSS) .]
How do you define and execute kruhse-lehtonen, u; hofmann, D. data and AI strategy? Harv. Sci Data. Rev. 2020. Available Online: https: // hdsr. Mitpress. Mit. Mit. Edu/Pub/4VLRF0X2/RELEASE/1 (Accessed January 1, 2023).
Schneider, J. Abraham, R. Artificial intelligence governance for business. System. 2022, 1-21. [Google Scholar] [CrossRef]

Elim Poon - Journalist, Creative Writer

Last modified: 27.08.2024

Big data can enhance AIC (Ghasemaghaei, ) and decision-making for more profitable business outcomes (Denicolai et al., ). The AI-related. () at McKinsey Analytics reports that 50% of enterprises have adopted AI in at least one of their business functions, and 75% of the. This paper aims to implement a systematic literature review analyzing convergence of the AI and corporate strategy and develop a theoretical model.

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