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Статті в журналах з теми "AI Strategy"

Автор: Grafiati

Опубліковано: 7 червня 2025

Оновлено: 2 серпня 2025

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1

笹嶋, 宗彦. "AI戦略（AI Strategy）". Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 35, № 2 (2023): 25. http://dx.doi.org/10.3156/jsoft.35.2_25_1.

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2

Vykhodets, R. S. "China’s AI Strategy." EURASIAN INTEGRATION: economics, law, politics 16, no. 2 (2022): 140–47. http://dx.doi.org/10.22394/2073-2929-2022-02-140-147.

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Анотація:

The present study is devoted to the consideration of the peculiarities of Chinese policy in the field of artificial intelligence (AI) in the context of global technological confrontation.Aim. To identify the key areas of China’s AI strategy, as well as to identify priority areas of state policy in this area.Tasks. To analyze the results of the development of the AI industry in China, to consider the main economic indicators, achievements in the creation of computing infrastructure, the development of its own research and development in the field of AI, to identify the main provisions of the ke

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3

Vykhodets, R. S. "The EAEU’s AI Strategy." EURASIAN INTEGRATION: economics, law, politics 16, no. 3 (2022): 106–17. http://dx.doi.org/10.22394/2073-2929-2022-03-106-117.

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This study is devoted to the consideration of the peculiarities of the policy of the EAEU states in the field of artificial intelligence (AI) in the context of global technological confrontation.Aim. To identify the key directions of the strategy of the EAEU member states in the field of AI, as well as to identify priority policy directions in this area at the union level.Tasks. To analyze the results of the development of the AI industry in Armenia, Belarus, Kazakhstan, Kyrgyzstan and Russia, to consider the main economic indicators, achievements in the creation of computing infrastructure, t

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4

Miyamoto, Michiko. "Measuring AI Governance, AI Adoption and AI Strategy of Japanese Companies." International Journal of Membrane Science and Technology 10, no. 1 (2023): 649–57. http://dx.doi.org/10.15379/ijmst.v10i1.2627.

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Purpose: This study aims to measure the level of AI governance and AI adoption among Japanese companies. Theoretical Framework: The research investigates the extent to which Japanese companies have implemented AI governance frameworks and the degree of AI adoption in their operations. The study also explores the relationship between AI governance, AI adoption, and AI strategy, providing insights into the factors that influence successful AI implementation. Design / Methodology / Approach: a survey questionnaire was administered to a representative sample of Japanese companies across various in

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5

MIYAKE, Youichiro. "Introduction to Strategy Game AI." Journal of Digital Games Research 15, no. 1 (2022): 31. http://dx.doi.org/10.9762/digraj.15.1_31.

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6

Tyler, Neil. "UK Organisations Lack AI Strategy." New Electronics 51, no. 20 (2018): 8. http://dx.doi.org/10.12968/s0047-9624(23)60714-x.

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7

Ma, Haoyuan. "Marketing Strategy of Open AI." Advances in Economics, Management and Political Sciences 73, no. 1 (2024): None. http://dx.doi.org/10.54254/2754-1169/73/20231500.

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In the current society, the field of artificial intelligence is developing rapidly. In the field of artificial intelligence, OpenAI can be said to be one of the best, which promotes technological progress and market dynamic changes. This article discusses OpenAI's marketing strategy in depth, with the aim of understanding its formulation, evolution, and impact on the artificial intelligence industry. Through analysis, this paper examines the company's initial marketing efforts, branding and messaging strategies, and the role of collaborations and partnerships in expanding its market presence.

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8

Kobelev, Sergei Veniaminovich. "Conceptual model of AI-transformation strategy." Финансы и управление, no. 4 (April 2024): 61–78. http://dx.doi.org/10.25136/2409-7802.2024.4.72461.

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The research subject is the problem of the lack of a systematic approach to developing and implementing AI transformation strategies for business. The author examines differences between digital and AI transformation processes, specific requirements for data, technologies, and personnel competencies in artificial intelligence implementation, as well as ethical aspects of AI use in business processes. Special attention is paid to systematizing existing approaches to AI transformation and identifying their limitations in modern conditions. The paper explores key success factors of AI implementat

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9

Saputra, Beny, Hartati Hartati, and Olivér Bene. "Hungary’s AI Strategy: Lessons for Indonesia’s AI Legal Framework Enhancement." Jambe Law Journal 7, no. 1 (2024): 25–58. http://dx.doi.org/10.22437/home.v7i1.325.

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This study analyses Hungary's approach to regulating artificial intelligence (AI) by analyzing their AI Strategy (2020-2030) and provides insights for improving Indonesia's legal framework. In Hungary, although there is no dedicated legislation for artificial intelligence (AI), the country places a high importance on adhering to current regulations to regulate AI technologies. This paper does a comparative analysis to evaluate the influence of Hungary's approach on the advancement of artificial intelligence (AI), the methods used to enforce regulations, the ethical principles followed, the saf

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10

Zhao, Yinuo. "Research on momentum strategy and contrarian strategy in AI stock prediction." Applied and Computational Engineering 29, no. 1 (2023): 125–32. http://dx.doi.org/10.54254/2755-2721/29/20231207.

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The emergence of ChatGPT has significantly enhanced the recognition and acceptance of artificial intelligence concept stocks within the Chinese stock market. Nevertheless, the short- and long-term fluctuations in the prices of AI companies remain uncertain. Therefore, the purpose of this research is to determine optimal strategy for evaluating the suitability of the contrarian strategy versus the momentum strategy in predicting the stock prices of AI concept stocks in the Chinese stock market. Based on a cross-comparison of the Chinese financial data sources iFinD and Wind Economic Database (E

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11

Nechesov, Andrey Vitalievich, Ivan Dorokhov, and Sergey Barykin. "ONE AI, ONE WORLD: A GLOBAL AI STRATEGY BY THE INTERNATIONAL AI COMMITTEE (IAIC)." System Informatics, no. 26 (July 16, 2025): 77–102. https://doi.org/10.31144/si.2307-6410.2025.n26.p77-102.

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12

Lee, On. "Practical AI from Strategy to Execution." Procedia Computer Science 135 (2018): 4. http://dx.doi.org/10.1016/j.procs.2018.08.143.

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13

Dirutigliano, J. "Italy ∙ The Italian Strategy on AI." Journal of AI Law and Regulation 1, no. 4 (2024): 458–64. https://doi.org/10.21552/aire/2024/4/10.

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14

Prasanna Kumar Natta. "AI-Driven Decision Intelligence: Optimizing Enterprise Strategy with AI-Augmented Insights." Journal of Computer Science and Technology Studies 7, no. 2 (2025): 146–52. https://doi.org/10.32996/jcsts.2025.7.2.13.

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Анотація:

Artificial intelligence-driven decision intelligence represents a transformative force in contemporary enterprise strategy formulation and operational execution. This article examines the critical shift from traditional decision processes characterized by manual interventions and static analytics to dynamic, AI-augmented frameworks that enable organizations to respond proactively to complex business environments. Despite generating unprecedented volumes of operational data, many enterprises struggle to translate this abundance into actionable intelligence, creating a substantial gap between da

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15

Han, Yoon Jong, and Young Jae Oh. "AI and Brand Activism - Generative AI based Video Content Design Strategy." CONTENTS PLUS 23, no. 2 (2025): 51–76. https://doi.org/10.14728/kcp.2025.23.02.051.

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16

Tsai, Yun-Cheng, Fu-Min Szu, Jun-Hao Chen, and Samuel Yen-Chi Chen. "Financial Vision-Based Reinforcement Learning Trading Strategy." Analytics 1, no. 1 (2022): 35–53. http://dx.doi.org/10.3390/analytics1010004.

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Анотація:

Recent advances in artificial intelligence (AI) for quantitative trading have led to its general superhuman performance among notable trading performance results. However, if we use AI without proper supervision, it can lead to wrong choices and huge losses. Therefore, we need to ask why AI makes decisions and how AI makes decisions so that people can trust AI. By understanding the decision process, people can make error corrections, so the need for explainability highlights the artificial intelligence challenges that intelligent technology can explain in trading. This research focuses on fina

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17

Caner, Salih, and Feyza Bhatti. "A Conceptual Framework on Defining Businesses Strategy for Artificial Intelligence." Contemporary Management Research 16, no. 3 (2020): 175–206. http://dx.doi.org/10.7903/cmr.19970.

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Анотація:

The technological developments on artificial intelligence (AI) are going to diffuse in all scales of firms in different industries. AI is increasingly used in diverse business functions, including marketing, customer service, cost reduction, and product improvement. Although there exists a large number of studies on AI, those focusing on the businesses are rather rare, and there is no holistic conceptual framework that brings the information on defining AI business strategy. The aim of this paper is to develop a conceptual framework on defining AI business strategy through a systematic literat

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18

Omkar, Vijay Hawaldar. "AI in Robotics." International Journal of Advance and Applied Research S6, no. 23 (2025): 252–55. https://doi.org/10.5281/zenodo.15195192.

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<em>This article provides a summary of the dynamic relationship between robotics and artificial intelligence as well as how it affects organizational and economic dynamics. We dive into the emerging study streams in the domains of management and economics that examine the complex effects of these state of-the-art technologies. We offer insights into the implications of automation, robotics, and artificial intelligence for company strategy and organizational design by drawing on the various approaches taken by researchers in this subject. We suggest that organizational and strategy scholars foc

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19

Li, Yihong. "Research on Artificial Intelligence in Game Strategy Optimization." International Journal of Computer Science and Information Technology 4, no. 1 (2024): 210–17. http://dx.doi.org/10.62051/ijcsit.v4n1.25.

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Анотація:

This paper provides a comprehensive review of the current state and future directions of artificial intelligence (AI) in game strategy optimization. It explores the key AI techniques driving advancements in this field, including machine learning, reinforcement learning, neural networks, and Monte Carlo tree search. Through detailed case studies of landmark AI systems such as Deep Blue, AlphaGo, Libratus, and AlphaStar, the paper illustrates the remarkable progress made in domains ranging from chess and go to poker and real-time strategy games. Despite these achievements, significant challenges

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20

Hodgins, Julia. "Would artificial intelligence make strategy ‘less human’?" Journal of Intelligence, Conflict, and Warfare 5, no. 1 (2022): 75–84. http://dx.doi.org/10.21810/jicw.v5i1.4212.

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Анотація:

This article discusses the influence of artificial intelligence (AI)—specifically Narrow AI—in the formulation of strategy arguing that there is not a straightforward answer to the question posited in the title. The Impact of Narrow AI in strategic decision-making will not fundamentally alter the nature of strategy due to the impossibility to program human faculties such as rationality and intentionality. Notwithstanding, the article concludes that ethical issues in the global environment will sustain the basis of strategy primarily as a human and political activity for the foreseeable future.

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21

Gan, Yifan. "Meet the AI expert." Touchpoint 15, no. 1 (2024): 78–80. http://dx.doi.org/10.30819/touchpoint.15-1.18.

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Анотація:

In this interview, Touchpoint Editor-in-Chief Jesse Grimes interviews Yifan Gan, a Shanghai-based consultant working at the intersection of service design and AI, and a Guest Editor of thisissue of Touchpoint. In addition to his consultancy work assisting Chinese firms with AI strategy and implementation, he is a lecturer for TongJi University and Shanghai Jiaotong University in service design and strategy.

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22

CHAUDHARY, GAURAV. "AI In Dynamic Pricing: Strategy and Consumer Perception." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50720.

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Abstract: Dynamic pricing has become a crucial difference for companies looking to increase client engagement and boost sales in the quickly changing e-commerce market. This study examines the use of artificial intelligence (AI) by AMAZON, one of the top e-commerce sites in India, to deploy dynamic pricing on a large scale .AI helps change how prices are set in digital commerce with immediate, flexible and customized pricing options. The study investigates how AI affects dynamic pricing with Amazon as an example. It looks at how machine learning and behavioural analytics are used to change pri

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23

Barata, Alexandre, Pedro Santos, and Rui Prada. "AI for Massive Multiplayer Online Strategy Games." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 7, no. 1 (2011): 110–15. http://dx.doi.org/10.1609/aiide.v7i1.12441.

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Massive Multiplayer Online Strategy games present several unique challenges to players and designers. There is the need to constantly adapt to changes in the game itself and the need to achieve a certain level of simulation and realism, which typically implies battles involving combat with several distinct armies, combat phases and diferent terrains; resource management which involves buying and selling goods and combining lots of diferent kinds of resources to fund the player's nation and cutthroat diplomacy which dictates the pace of the game. However, these constant changes and simulation m

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24

Buro, Michael, and David Churchill. "Real-Time Strategy Game Competitions." AI Magazine 33, no. 3 (2012): 106. http://dx.doi.org/10.1609/aimag.v33i3.2419.

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Анотація:

In recent years, real-time strategy (RTS) games have gained attention in the AI research community for their multitude of challenging and relevant real-time decision problems that have to be solved in order to win against human experts or to effectively collaborate with other players in team-games. In this article we motivate research in this area, give an overview of past RTS game AI competitions, and discuss future directions.

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25

Koubaa El Euch, Hasna, and Foued Ben Said. "Marketing Strategy and Artificial Intelligence." Journal of Telecommunications and the Digital Economy 12, no. 1 (2024): 538–74. http://dx.doi.org/10.18080/jtde.v12n1.887.

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Анотація:

The marketing literature highlights the growing integration of artificial intelligence (AI) into marketing strategies. Several publications show that this field is attracting increasing interest from researchers. The purpose of this article is to provide an overview of academic publications related to AI and marketing strategies, while also examining the lack of bibliometric analysis in this area. In this study, 1100 articles, published in the Scopus and Web of Science databases, were selected and, according to a consistent search procedure, were examined. A performance analysis, based on bibl

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26

Maurice Bergsma, Maurice Bergsma, and Pieter Spronck. "Adaptive Spatial Reasoning for Turn-Based Strategy Games." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 4, no. 1 (2021): 161–66. http://dx.doi.org/10.1609/aiide.v4i1.18690.

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Анотація:

The quality of AI opponents often leaves a lot to be desired, which poses many attractive challenges for AI researchers. In this respect, Turn-based Strategy (TBS) games are of particular interest. These games are focussed on high-level decision making, rather than low-level behavioural actions. For efficiently designing a TBS AI, in this paper we propose a game AI architecture named ADAPTA (Allocation and Decomposition Architecture for Performing Tactical AI). It is based on task decomposition using asset allocation, and promotes the use of machine learning techniques. In our research we conc

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27

MVN, Nagalakshmi, and Chandrika Reddy P. "AI-driven Business Model Innovation - Where Technology Meets Strategy." RVIM Journal of Management Research 16, no. 1 (2024): 5–17. http://dx.doi.org/10.70599/rvim/2024/306.

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Анотація:

Business Model Innovation (BMI) involves redefining how organizations create, deliver, and capture value. With the advent of Artificial Intelligence (AI), businesses are increasingly leveraging these technologies to transform their models. The current research investigates the impact of AI on BMI using primary data collected from various industries. It employs statistical analyses to understand the extent of AI adoption, its effects on business model components and various key business metrics. The study identifies key trends in AI adoption across functions and industries. Key findings reveal

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28

Büber, Harun, and Emrullah Seven. "Strategic Decision-Making in the AI Era: An Integrated Approach Classical, Adaptive, Resource-Based, and Processual Views." International Journal of Management and Administration 9, no. 17 (2025): 67–97. https://doi.org/10.29064/ijma.1637935.

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Анотація:

This study explores how artificial intelligence (AI) can enhance strategic decision-making by integrating with four established strategic schools: Classical, Adaptive, Resource-Based, and Processual. While AI improves data-driven insights, it lacks the strategic foresight, contextual awareness, and ethical judgment inherent in traditional frameworks. Using a structured literature review, this conceptual study evaluates the synergy between AI and strategic schools. Sources were selected from peer-reviewed databases, including Scopus and Web of Science, using keywords such as "AI-driven strategy

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29

Maria, Indri, and Riswadi Riswadi. "Artificial Intelligence Governance Strategy in the Indonesian Regulation System, Offensive or Defensive?" Sharia Oikonomia Law Journal 2, no. 4 (2024): 233–43. https://doi.org/10.70177/solj.v2i4.1643.

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The Indonesian government unveiled its National Strategy on Artificial Intelligence (AI) in 2020, known as “Strategi Nasional Kecerdasan Artifisial”. This comprehensive plan addresses various aspects of AI, including ethical considerations, policy frameworks, workforce development, data ecosystems, and infrastructure for AI advancement. It’s important to note, however, that National Strategy serves as a guiding policy document rather than a legally enforceable set of regulations. However, this does not mean that the Indonesian government is absent from regulating AI technology. In Indonesia, t

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30

Ni, Wen, and Lufeng Bai. "Artificial Intelligence Promotes Educational Equity Strategy Design." Advances in Machine Learning & Artificial Intelligence 06, no. 01 (2025): 01–06. https://doi.org/10.33140/amlai.06.01.01.

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This article delves into the global issue of educational equity, particularly the core problems of unequal resource allocation, differences in educational quality, and individual differences among students. The article proposes an innovative approach to using artificial intelligence technology to address the issue of educational equity. Through specific applications such as data-driven decision support, personalized learning, and intelligent tutoring systems, the potential of artificial intelligence (AI) technology in improving the efficiency of educational resource utilization and meeting ind

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31

Kumar, A., A. Kumar, S. Kumari, S. Kumar, N. Kumari, and A. K. Behura. "Artificial Intelligence: The Strategy of Financial Risk Management." Finance: Theory and Practice 28, no. 3 (2024): 174–82. http://dx.doi.org/10.26794/2587-5671-2024-28-3-174-182.

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This research examines the use of artificial intelligence (AI) as a financial risk management tool. The concept is motivated by the revolutionary effects that financial technology has on business operations. Traditional methods of financial risk management are no longer effective and require revision. The purpose of the study is to assess the role of artificial intelligence in the management of financial risks and offer recommendations for its further use in the financial sector of the economy. Methodological analysis of relevant scientific literature showed that AI, in particular machine lear

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32

Nalini, B., and KA Maria John Joesph. "An Investigation of How AI-Powered Modern Strategy Management Might Improve Decision-Making, Optimise Operations, and Promote Innovation Modern Strategy Management." ComFin Research 13, S1-i2 (2025): 99–105. https://doi.org/10.34293/commerce.v13is1-i2.8744.

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Анотація:

In the realm of modern strategy management, Artificial Intelligence (AI) is making significant strides by improving strategic decision-making, enhancing operational efficiency, and driving innovation. Companies can leverage AI-driven analytics to process extensive datasets, extract valuable insights, and anticipate market shifts, enabling more informed and proactive strategic planning. Furthermore, AI technologies streamline and optimize routine tasks, leading to more effective business operations, reduced expenses, and improved productivity. Furthermore, the use of AI-driven personalization e

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33

Bashir, Jibran. "Strategic Enterprise Artificial Intelligence (The Conceptual Hierarchical Framework)." International Journal of Business & Management Studies 05, no. 05 (2024): 131–38. http://dx.doi.org/10.56734/ijbms.v5n5a13.

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This paper discusses three statements: Enterprise AI strategy must be linked with the existing business strategy of the organization, Enterprise AI must be a virtual strategist of the organization, and Enterprise AI must be a virtual strategy manager for organizational performance. Based on the research on these statements, this paper will introduce the conceptual hierarchical framework titled Strategic Enterprise Artificial Intelligence (SEAI) consisting of four levels: Disconnected Enterprise AI, Linked Enterprise AI, Strategist Enterprise AI, and Integrative Enterprise AI. Disconnected Ente

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34

Sayed Ahammad Smarn, MD Sakibul Hasan, Sangida Jahan Ripa, Sk Md. Mahibur Rahman Cheragee, Md. Abdul Mannan Rana, and Monsurul Islam Chowdhury. "Integrating Artificial Intelligence into Business Strategy: Opportunities and Challenges." Journal of Computer Science and Technology Studies 7, no. 2 (2025): 427–35. https://doi.org/10.32996/jcsts.2025.7.2.44.

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Анотація:

Artificial Intelligence (AI) has become a transformative business force, providing unparalleled opportunities for innovation, competitive advantage and efficiency. Artificial Intelligence (AI) is a technology that is here to stay and has the revolutionary potential to alter the corporate strategy landscape, so much so that it will improve the organization's competitiveness in a time of crisis. This literature explores the role of AI, particularly innovations driven by machine learning, in improving operational effectiveness and optimizing strategic decision-making, which business process manag

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35

Jyoti Aggarwal. "Building an AI-Ready Data Strategy Using Lakehouse Technology." Journal of Computer Science and Technology Studies 7, no. 3 (2025): 663–76. https://doi.org/10.32996/jcsts.2025.7.3.76.

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This article explores how organizations can leverage Lakehouse technology to build robust AI-ready data strategies. Lakehouse architecture represents a paradigm shift in data management by unifying data lake flexibility with data warehouse reliability. The integration of this technology enables organizations to overcome traditional barriers to AI implementation through unified storage, efficient processing, and comprehensive governance. By examining key components including data ingestion, preparation, metadata management, governance, and scalable infrastructure, the article illustrates how La

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36

Chatterjee, Sheshadri. "AI strategy of India: policy framework, adoption challenges and actions for government." Transforming Government: People, Process and Policy 14, no. 5 (2020): 757–75. http://dx.doi.org/10.1108/tg-05-2019-0031.

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Анотація:

Purpose The purpose of this study is to provide recommendations for policy framework on artificial intelligence (AI) in India. Design/methodology/approach Studies have been conducted through focus group discussion and the other sources such as different company websites using AI, Indian Government strategy reports on AI, literature studies, different policies implemented on AI in different locations and other relevant documents. After those studies, a charter of recommendation has been provided. This will help the authority to frame the AI policy for India. Findings This study highlights that

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37

Aladebumoye, Tolulope. "Integrating AI-Driven Tax Technology into Business Strategy." International Journal of Research and Review 12, no. 1 (2025): 224–31. https://doi.org/10.52403/ijrr.20250128.

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The integration of Artificial Intelligence (AI) into tax technology presents transformative potential for enhancing business strategies by significantly improving efficiency, accuracy, and compliance. This paper delves into these advancements and critically evaluates their strategic implications across various sectors, providing a thorough analysis of how AI-driven innovations are reshaping tax practices. It highlights the dual role of AI in not only simplifying and automating complex tax operations but also in enabling businesses to navigate the increasingly complex maze of global tax regulat

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38

Mohsin, Abdulridha Nasser. "Artificial Intelligence in Manufacturing Strategy: A Theoretical Perspective." European Journal of Management, Economics and Business 2, no. 2 (2025): 101–10. https://doi.org/10.59324/ejmeb.2025.2(2).11.

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The research explores the extent to which Artificial Intelligence (AI) technologies can be used to implement strategies for the Iraqi industrial factory. It will address critical challenges that may hinder the implementation of AI technologies in the Iraqi factory, such as insufficient data integration, high initial costs, lack of trained personnel, and the impact of organisational culture on productivity and operational efficiency. The research aims to answer one key question: To what extent can the issues hindering the implementation of the Iraqi factory strategy be identified and addressed

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39

Abdulridha, Nasser Mohsin. "Artificial Intelligence in Manufacturing Strategy: A Theoretical Perspective." European Journal of Management, Economics and Business 2, no. 2 (2025): 101–10. https://doi.org/10.59324/ejmeb.2025.2(2).11.

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The research explores the extent to which Artificial Intelligence (AI) technologies can be used to implement strategies for the Iraqi industrial factory. It will address critical challenges that may hinder the implementation of AI technologies in the Iraqi factory, such as insufficient data integration, high initial costs, lack of trained personnel, and the impact of organisational culture on productivity and operational efficiency. The research aims to answer one key question: To what extent can the issues hindering the implementation of the Iraqi factory strategy be identified and addressed

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IJHS, Medical Association. "AI applications in healthcare and medicine: A brief Strategy and applications." International Journal of Advances in Health Sciences 11, no. 3 (2024): 24–32. https://doi.org/10.5281/zenodo.11353521.

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Advancements in AI technology are transforming healthcare and education. This article analyzes the influence of AI in different industries and discusses its advantages in various fields, such as health care administrative, medicine, robotics for surgery, drug discovery, patient care. AI-powered predictive analytics can improve the accuracy, efficiency, and cost-effectiveness of disease diagnosis and laboratory testing. In this article, we survey the current status of AI in healthcare, as well as discuss its future. The focused question explores the impact of applying AI in healthcare settings

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Farwah, Mahmoud Mohammed Abu. "Saudi Arabia’s Generative AI Strategy: Legal Challenges in Intellectual Property Protection." Journal of Lifestyle and SDGs Review 5, no. 2 (2025): e04873. https://doi.org/10.47172/2965-730x.sdgsreview.v5.n02.pe04873.

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Objectives: This study aims to evaluate Saudi Arabia’s legal framework concerning intellectual property (IP) protection in AI development. It seeks to identify legal barriers to AI training, particularly in copyright law, and propose legislative reforms to enhance innovation while maintaining IP rights. The research aligns with Vision 2030, positioning Saudi Arabia as a leader in AI-driven innovation. Methods: The study employs a comparative legal analysis, examining international models such as the United States, the European Union, and Japan. It assesses Saudi copyright laws related to AI tr

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Kitsios, Fotis, and Maria Kamariotou. "Artificial Intelligence and Business Strategy towards Digital Transformation: A Research Agenda." Sustainability 13, no. 4 (2021): 2025. http://dx.doi.org/10.3390/su13042025.

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In the past decade, current literature and businesses have drawn attention to Artificial Intelligence (AI) tools and in particular to the advances in machine learning techniques. Nevertheless, while the AI technology offers great potential to solve difficulties, challenges remain implicated in practical implementation and lack of expertise in the strategic usage of AI to create business value. This paper aims to implement a systematic literature review analyzing convergence of the AI and corporate strategy and develop a theoretical model incorporating issues based on the existing research in t

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Ibiyemi, Abayomi, Olayinka Ogungbemi, and Martins Adenipekun. "Multi-Criteria Data Evaluation Strategy for Development Scales: An Example Study." International Journal of Social Sciences & Humanities (IJSSH) 1, no. 1 (2016): 40–67. http://dx.doi.org/10.58885/ijssh.v1i1.40.ai.

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This study illustrates checks using data from the sustainability and Appraisers' scale designed to make causal analysis between perceived industrial sustainability benefits and Appraisers' support. It obtained questionnaire responses from 267 Real Estate Appraisers. The study objectives are to assess the pattern and extent of the missing data; the assumptions of the multivariate normality with the bootstrap resampling; the consistencies of the slopes of change, and identify the costs that may arise from poor data quality. The work also investigates convergent, discriminant, cross boundary vali

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Moroz, Olena, Stanislav Komysh, Neonila Krasnozhon, Viktoriia Datsenko, and Bohdan Hranatyrko. "The strategy of the historical policy of Ukraine in the context of the development of the Russian-Ukrainian war: an anthropological aspect." Revista Amazonia Investiga 11, no. 57 (2022): 92–99. http://dx.doi.org/10.34069/ai/2022.57.09.10.

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In a confrontation with an aggressor who uses various hybrid tools, the correct conduct of humanitarian policy is of great importance. Unfortunately, in the realities of the Russian-Ukrainian war, there is no special strategy of historical policy. Therefore, development and its further improvement is an important topic of both theoretical and practical research. The purpose of the article is to investigate the Strategy of the Historical Policy of Ukraine in the context of the development of the Russian-Ukrainian confrontation. The research is based on the use of general scientific and special

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Sabil, Sabil, B. M. A. S. Anaconda Bangkara, Tini Mogea, Ery Niswan, and Elkana Timotius. "Identification of HRM Improvement Strategy Using Artificial Intelligence in Modern Economic Development." International Journal of Professional Business Review 8, no. 6 (2023): e01835. http://dx.doi.org/10.26668/businessreview/2023.v8i6.1835.

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Purpose: This literature review study aims to identify HRM improvement strategies using artificial intelligence (AI) in modern economic development. Theoretical framework: The study will review existing literature and synthesize the findings to identify best practices and key strategies for implementing AI in HRM. The study will focus on the role of AI in HRM improvement and explore how AI can be used to enhance recruitment, training, performance management, and employee engagement. Design/methodology/approach: Literature review, the search approach will include keywords and Boolean operators

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Jatin Navani. "Can AI models Outperform the Traditional Buy-and-Hold Strategy?" Journal of Electrical Systems 20, no. 11s (2024): 1211–19. https://doi.org/10.52783/jes.7420.

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The efficient market theory suggests that the 'buy and hold' (B&H) strategy is optimal due to its simplicity and lower costs. Artificial intelligence (AI) models, including machine learning (ML) is now a common research tool in financial applications. However, advancements in AI and machine learning offer the potential to outperform B&H. This paper investigates AI models applied to the Nifty 50 index, demonstrating that an AI-based strategy can surpass the performance of the traditional B&H approach. We compare various machine learning classifiers, such as LightGBM, KNN, XGB, SVC,

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Purnawati, Elisa. "The Influence of Digital Technology on Marketing Strategy." Management Studies and Business Journal (PRODUCTIVITY) 1, no. 3 (2024): 432–46. http://dx.doi.org/10.62207/f4s8px08.

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The alignment of Artificial Intelligence (AI) Technology in marketing has revolutionized market segmentation and marketing strategies. This paper examines the factors that influence the relationship between the use of AI in market segmentation and marketing strategy, including data and resource availability, organizational readiness and corporate culture, and legal and ethical considerations. Through a comprehensive literature review and analysis, this study identifies key hypotheses and discusses their implications for future research and practice. Despite challenges such as privacy concerns

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Sari, Dian Ratna. "INOVASI PEMBELAJARAN BERBASIS AI DAN IoT: STRATEGI PEMBELAJARAN BIOLOGI PADA SOCIETY 5.0." BIOCHEPHY: Journal of Science Education 4, no. 2 (2024): 610–18. https://doi.org/10.52562/biochephy.v4i2.1329.

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Penelitian ini bertujuan untuk memperkaya informasi dan meningkatkan awareness dalam hal inovasi pembelajaran Biologi berbasis AI dan IoT di era society 5.0. Metode penelitian yang digunakan yakni systematic review, dengan melakukan studi literatur yang relevan dan termutakhir. Hasil dari penelitian ini menunjukkan bahwa terdapat beberapa manfaat dari pemanfaatan AI dan IoT, seperti: develop problem solving strategy, allow continous learning, encourgae social intelligence, promote creativity, dan enable Human-AI synergy. Selain itu, kontribusi positif dari inovasi pembelajaran berbasis AI dan

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Li, Hanjie. "AI-Powered Negotiations: Opportunities, Challenges, and the Future of Business Strategy." Transactions on Economics, Business and Management Research 13 (November 18, 2024): 148–54. https://doi.org/10.62051/dg1trh68.

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Business negotiations are integral to various aspects of enterprise operations, from recruitment and resource allocation to procurement and international business collaborations. Despite their critical importance, negotiations often remain cumbersome, complex, and time-consuming, with outcomes heavily influenced by the negotiator's skills and external factors, frequently failing to maximize benefits or achieve desired goals. The rapid advancement of artificial intelligence (AI) technologies, particularly large language models (LLMs) like ChatGPT, has seen AI increasingly integrated into daily

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Yan, Jialu. "AI Optimization Strategy for Streaming Financial Advertising Placement." Journal of Computer Science and Artificial Intelligence 2, no. 2 (2025): 40–43. https://doi.org/10.54097/s5fek465.

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In the financial field, streaming media advertising is gradually showing its indispensable position. However, traditional advertising methods have encountered difficulties such as insufficient accuracy, heavy cost burden, and low conversion efficiency. In this context, artificial intelligence (AI) technology, with its excellent data analysis capabilities, user profiling, personalized promotion, and creative content generation advantages, has brought new ideas for the precise placement of streaming media advertising in the financial field. This article deeply analyzes the specific application o

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