Innovative applications in businesses: An evaluation on generative artificial intelligence

İşgüzar, Seda; Fendoğlu, Eda; Şimşek, Ahmed İhsan Article Innovative applications in businesses: An evaluation on generative artificial intelligence Amfiteatru Economic Journal Provided in Cooperation with: The Bucharest University of Economic Studies Suggested Citation: İşgüzar, Seda; Fendoğlu, Eda; Şimşek, Ahmed İhsan (2024) : Innovative applications in businesses: An evaluation on generative artificial intelligence, Amfiteatru Economic Journal, ISSN 2247-9104, The Bucharest University of Economic Studies, Bucharest, Vol. 26, Iss. 66, pp. 511-530, https://doi.org/10.24818/EA/2024/66/511 This Version is available at: https://hdl.handle.net/10419/300607 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Innovative Application of AI in Business Impacting Socio-Economic Progress AE Vol. 26 • No.66 • May 2024 511 INNOVATIVE APPLICATIONS IN BUSINESSES: AN EVALUATION ON GENERATIVE ARTIFICIAL INTELLIGENCE Seda İşgüzar1, Eda Fendoğlu2 * and Ahmed İhsan Şimşek3 1)2)Malatya Turgut Özal University, Malatya, Turkey 3)Fırat University, Elazığ, Turkey Please cite this article as: İşgüzar, S., Fendoğlu, E. and Şimşek, A.I., 2024. Innovative Applications in Businesses: An Evaluation on Generative Artificial Intelligence. Amfiteatru Economic, 26(66), pp. 511-530. DOI: https://doi.org/10.24818/EA/2024/66/511 Article History Received: 30 December 2023 Revised: 18 February 2024 Accepted:26 March 2024 Abstract The utilisation of Chat Generative Pre-Trained Transformer (ChatGPT) and generative artificial intelligence (GenAI) technologies has started to demonstrate its impact across several domains. The swift shift and widespread implementation of efficient artificial intelligence (AI) present distinct prospects such as optimisation, advancement, enhanced efficiency, boosted sales and marketing, expansion, reduced costs, and heightened profitability. GenAI has the potential to create a competition crisis between technologically advanced enterprises and less developed ones. Additionally, it may give rise to legal, moral, and ethical issues such as copyright infringement and the production of fake and false information. Hence, it is crucial for organisations to ensure that the productivity of AI is maximized in order to maximise its benefits and minimise any potential harm. The aim of this study is to provide suggestions regarding the use and potential of GenAI technologies in the corporate sector and to emphasise the potential research areas of future GenAI. This study contributes to research and practice in business and management and also identifies future research avenues. This study examines the benefits and disadvantages of using GenAI tools in businesses and individual departments, and it highlights the potential risks and dangers. A bibliometric analysis of 198 studies in the discipline of Business & Management from the Scopus database was conducted using the R program’s bibliometrix package. The study focuses on descriptive data, annual scientific production, most productive journals, most productive authors and authors dominance factor, most cited publications, and most relevant keywords. The findings show that GenAI is likely to continue with a strong and rapidly rising trend in 2024 and beyond. * Corresponding author, Eda Fendoğlu – e-mail: [email protected] This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2024 The Author(s). AE Innovative Applications in Businesses: An Evaluation on Generative Artificial Intelligence 512 Amfiteatru Economic Keywords: ChatGPT, artificial intelligence (AI), generative artificial intelligence (GenAI), OpenAI, business, business management, technology adoption, bibliometric analysis. JEL Classification: M10, M19, O10, O32, O33 Introduction “Is it possible for machines to possess the ability to think?” Alan Turing’s inquiry in 1950 serves as the catalyst for the integration of artificial intelligence (AI) into our contemporary society, as it outperforms humans in certain activities. AI is a branch of science and engineering that enables the creation of intelligent machines that mimic human intelligence. The concept of AI was first introduced in a text presented by a group of scientists led by J. McCarthy at a seminar held at Dartmouth College in 1956. With their work and that of subsequent researchers, it emerged as a science (Say, 2018). Since then, despite experiencing periods of both advancement and stagnation, AI has become a technology that not only names an era, but also, in the words of Harari, empowers humanity with the ability to “reshape life” (İşgüzar, 2021). These advancements in technology, particularly in the field of AI, have created a variable and uncertain competitive environment. Businesses are aware of the need to adapt their processes rapidly to changing conditions. Therefore, they are redefining their competitive strategies while simultaneously integrating these technologies into their processes, aiming to make many processes data-driven and automated, from planning and execution to performance measurement. In the past decade, rapid advancements in AI technologies have significantly impacted businesses’ operations and strategic decision-making processes. Utilised in various fields ranging from financial forecasting to customer relationship management, and from market analysis to inventory management, AI technologies support businesses in their decision-making processes. However, recent developments have raised the question, “Will humanity delegate its creative skills, which it still possesses, to artificial intelligence as well?” This discussion pertains to the rise of GenAI tools, which can generate new content by learning from data. GenAI, based on deep learning techniques, utilises data-driven learning methods with humanlike language abilities to learn from example data and generate new data. Although existing AI technologies provide algorithmic predictions based on data understanding, GenAI goes further by creating unique content such as text, images, videos, and music. The emergence of GenAI has attracted attention from academia to the business world due to its “creativity” ability. The existence of GenAI tools is undoubtedly based on cumulative knowledge in the field of AI. However, the release of the Generative Pre-Trained Transformer (GPT) by OpenAI in June 2018 marked a significant milestone in the popularity of GenAI technologies. The introduction of ChatGPT, which could be used as a language model, in November 2022 further increased interest in this technology. Trained on a vast dataset including publicly available texts, books, articles, and other written sources, ChatGPT has been successful in producing results similar to human language abilities (Serdaliyev and Zhunissov, 2023; Lyu et al., 2023). Currently, various companies, including Google, have developed and continue to improve GenAI tools in different domains. These technologies are expected to bring about significant transformations in education, communication, healthcare, business, and various aspects of life. Innovative Application of AI in Business Impacting Socio-Economic Progress AE Vol. 26 • No.66 • May 2024 513 The incorporation of GenAI tools into business and management processes is believed to provide a competitive advantage. Therefore, businesses are focusing on how to benefit from these technologies. These tools have the ability to merge different datasets, analyse, summarise, and generate new content and perspectives, thus inspiring creative thinking. Hence, it is deemed essential for businesses to comprehensively understand how to utilise GenAI technologies and explore their impacts through research. Seeing the applications and effects of GenAI tools in businesses will take time. Even the extensive literature on AI and business context can provide inconsistent results on the real impacts of these technologies on businesses (Pereira et al., 2023). Therefore, research on GenAI and business is still emerging. For instance, a search on the Web of Science database with the keyword “generative artificial intelligence and business” yields a total of 64 studies. Most of these studies focus on determining whether ChatGPT can produce effective results by generating instant scenarios and demonstrating their benefits. However, these studies only encourage businesses to use ChatGPT for instant problem-solving. Hence, there is a need for studies that can provide insight to managers who wish to integrate GenAI as an innovative technology into their businesses. This study aims to provide a projection regarding the future of GenAI tools in businesses, based on a bibliometric analysis of data from the academic literature, which is just beginning to conceptualise. The findings are expected to provide holistic knowledge about prominent researchers, countries, institutions, focused topics, and applications in the field of GenAI and business management. This study is structured to address the following research questions: RQ1: In which research areas and by whom have studies been conducted in the field of GenAI and business? RQ2: What are the connections among the studies conducted in the field of GenAI and business? What types of studies will these connections guide? RQ3: What are the opportunities indicated in the existing literature for implementing GenAI in businesses? RQ4: What are the potential threats or challenges associated with the implementation of GenAI in the business sector? The next section provides a review of the literature on GenAI, followed by a section presenting information on bibliometric analysis, which constitutes the second part of the study. The methodology is outlined in the third section, and the findings are presented in the fourth section. Finally, the last section discusses managerial and practical implications, along with research limitations. 1. Literature review GenAI has begun to revolutionise, as it has emerged as a new and stimulating solution tool to improve the performance and efficiency of businesses under a number of systematic changes and problems in corporate management and organisation. Table no. 1 summarises the most recent (2023-2024) studies on ChatGPT and GenAI, especially in the business field. AE Innovative Applications in Businesses: An Evaluation on Generative Artificial Intelligence 514 Amfiteatru Economic Table no. 1. Literature review Reference Business areas Purpose Methodology Outcomes Amin et al. (2024) Twitter, Stock Market To predict stock market trends. Analysed 500,000 tweets containing ChatGPT-related hashtags. Natural Language Processing techniques, sentiment analysis More precise market trends forecasts by correlating daily Twitter sentiment and engagement metrics on ChatGPT with end-of-day stock price movements, along with synergies between macroeconomic indicators and company-specific stock data. Aguinis et al. (2024) Human resource management (HRM) To demonstrate that GenAI and ChatGPT can be useful HRM assistants for strategic and operational tasks. Analysis of texts obtained with ChatGPT prompts and document analysis When used by a well-trained HRM professional, GenAI and ChatGPT can be an invaluable tool for completing strategic and operational tasks and allocating resources. Al Naqbi et al. (2024) Enhancing Work Productivity To assess GenAI impact on business performance and efficiency. Bibliometric analysis GenAI improves productivity and efficiency; serves as a guide for future research. Gupta and Yang (2024) Entrepreneurs To create a comprehensive GenAI Technology Adoption Model for entrepreneurs and other actors. Task-Oriented AI Adoption (T-AIA) model New model stands out due to its flexibility, includes different actors in the innovation ecosystem (libraries), is broadly applicable, and can be supported by statistical analysis in the future. Khan and Umer (2024) Finance To examine applications of ChatGPT in finance, and the corresponding ethical challenges. Literature review ChatGPT causes a positive disruption in financial decisionmaking and brings many ethical problems. Rane et al. (2024) Intelligent manufacturing To explore existing knowledge on ChatGPT and Bard integration into smart manufacturing, to create a new paradigm. Literature review and bibliometric analysis The major change brought to the industry by ChatGPT will push it to greater efficiency, competitiveness, and applicability in the rapidly developing global market. Rane (2023a) Industry 4.0, Industry 5.0 and Society 5.0 To examine the role of ChatGPT and GenAI in Industry 4.0, Industry 5.0 and Society 5.0. Literature review Efficient use of AI, in accordance with ethical rules and due attention, is a guiding light towards a future where societies are interconnected and industries develop in harmony. Korzynski et al. (2023) Management To examine GenAIChatGPT potential to be a new context for selected theories and concepts. Opinion piece article GenAI can impact managerial work at strategic, functional, and administrative levels. Soni (2023a) GEN AI with human, technology and market factors in revenue growth To examine GenAI impact on revenue levels, market dynamics, technological developments, and human variables. Data from 331 SMEs Ridge, Elastic, and Net regression methods. Strategic utilisation of human resources and robust technological infrastructure can enhance the benefits of GenAI. Advantages of GenAI may diminish as market competition increases. AI offers an extra benefit in competitive marketplaces. Chuma and Oliveira (2023) Decisionmaking To examine the use of ChatGPT as a decisionmaking tool in a commercial context. Three sets of questions presented to ChatGPT. ChatGPT provides an overview of the issue in the decision-making process, but does not replace the decision-making expert. Chen et al. (2023) Business and finance To examine advancements of GenAINatural Language Processing, Sentiment Analysis Sentiment scores from ChatGPT can effectively predict organisations’ stock return performance and risk Innovative Application of AI in Business Impacting Socio-Economic Progress AE Vol. 26 • No.66 • May 2024 515 Reference Business areas Purpose Methodology Outcomes ChatGPT in the business and financial sectors. management capabilities. Highlights its transformative impact on costs and productivity. Kanbach et al. (2023) Healthcare, software engineering, healthcare services Controversial example of GenAI’s impact on businesses. Qualitative content analysis on 513 examples Scholarly discussion on AI. Academic studies, business reports, news, and podcasts offer insight on leveraging technologies to enhance existing business models and uncover novel ones. Soni (2023b) Digital marketing campaigns To determine parameters affecting the adoption of productive AI in digital marketing campaigns. Survey of 411 professionals. Machine learning techniques: Decision Tree, KNN, Random Forest, SVM, Sequential Selection, Probability models Use of GenAI in digital marketing is driven by efficiency, scalability, and the ability to develop personalised content. Substantial barriers to adoption: complexity, knowledge gaps, and constraints on creativity. Wamba et al. (2023) Supply chain To explore advantages of GenAI, identify risks and strategies for effective use of GenAI in organisations. Surveys of 315 persons (US and UK). Comparison between companies. GenAI brings advantages in terms of efficiency and production, but also obstacles and risks in areas such as ethical use, privacy, and human integration. Rane (2023c) Human resource management (HRM) To examine roles and challenges presented by ChatGPT and its counterparts in HRM. Co-occurrence analysis of keywords in literature Technologies offer transformative potential, but introduce complexities. Budwar et al. (2023) Human resource management (HRM) To emphasise risks of ChatGPT and more complex AI. Case study ChatGPT is valuable in assisting researchers to generate ideas, propose theory, choose research design, determine metrics, choose a data analysis approach, and provide general guidance on conducting ethical research & reporting results. Rane (2023d) Multidisciplinar y: Retail, finance, manufacturing, accounting, transportation, and construction To evaluate ChatGPT and other AI contribution in facilitating seamless adoption in various sectors. Literature review Cross-domain teams are instrumental in harnessing the transformative potential of AI, thereby driving industries towards a more efficient, sustainable, and technologically advanced future. Rane (2023b) Finance and accounting To examine the changing role of ChatGPT and GenAI. Literature review ChatGPT increases customer engagement in finance; it efficiently processes large amounts of data. Automation in accounting streamlines processes, reduces expenses and human errors. Beerbaum (2023) Business landscape and accounting To apply behavioural theory to robotic process automation (RPA) and to address ethical concerns and constraints. Classification to increase the transparency of XBRL application Emergence of productive AI will lead to moral problems. Transparency technologies offer answers to mitigate risks. AE Innovative Applications in Businesses: An Evaluation on Generative Artificial Intelligence 516 Amfiteatru Economic 2. Bibliometric analysis Bibliometrics is a quantitative approach used to analyse publications in the scientific literature and the connections among them (Groos and Pritchard, 1969; Herubel, 1999). The primary objective of this approach is to methodically assess scientific papers and uncover the connections among them (Donthu et al., 2021). Additionally, it possesses the capacity to indicate the direction in which a field of study will progress in the next years. Consequently, it is a commonly employed strategy for conducting systematic literature reviews (Aria and Cuccurullo, 2017). The system examines extensive scientific data, including keywords, citations, publication count, and using mathematical and statistical techniques to extract relational information. Conducting bibliometric analyses in this manner enables the investigation of specific subjects, comprehension of trends, and provision of an unbiased viewpoint to researchers (Wang and Su, 2020; Han et al., 2020). In his 1972 work, Crane elucidates the advantages of bibliometric analyses, which can be summarised as follows:  Exploring novel research methodologies;  Formulating fresh research proposals;  Determining the variables associated with a research subject;  Recognising the interrelationships among countries, academic publications, and researchers;  Ensuring the integration of concepts and practical implementations. However, bibliometric analyses do possess certain limitations. Analyses can solely be conducted on studies that have open access. Excluded are studies that are currently in the publication phase or are not accessible. This could impede the provision of a more precise depiction of the present circumstances. Furthermore, in the present day, alterations are transpiring at an exponential pace. Bibliometric data may occasionally provide an inadequate representation in light of swift changes. Although it has limits, bibliometric analysis is nonetheless a crucial tool for comprehending the connections between various scientific studies in the publishing industry, monitoring advancements in a certain topic or field, and influencing future research paths. 3. Methodology and data This study examines the influence of efficient AI technologies on businesses and was carried out using a five-stage methodology suggested by Ruiz-Real et al. (2018). The procedures involved in this process are as follows: (1) Identifying suitable keywords, (2) Choosing the database to be scanned, (3) Enhancing the search criteria based on the Initial Research findings, (4) Exporting the results, (5) Analysing the data and discussing the findings. In order to conduct the study, we thoroughly reviewed the pertinent literature and identified the keywords as “generative artificial intelligence & business” and “generative artificial intelligence & business management”, without using quotation marks. Bibliometric studies typically rely on the utilisation of multidisciplinary scientific databases such as Web of Science or Scopus. These databases encompass a wide range of scientific journals, conference proceedings, and other academic resources (Arenas et al., 2018). The initial step of this research involved searching the Web of Science database using the keyword Innovative Application of AI in Business Impacting Socio-Economic Progress AE Vol. 26 • No.66 • May 2024 517 “generative artificial intelligence & business” without quotation marks. As of December 15, 2023, a cumulative of 45 studies including the period from 2019 to 2023 have been retrieved. A total of 31 articles are associated with the year 2023. There are a total of 32 articles, 11 early access publications, 6 proceeding papers, 4 review articles, 3 editorial materials, and 2 book chapters in these works. Upon examination of the acknowledged papers, it was ascertained that only 14 of them were directly or indirectly relevant to the objective of our study. A search was conducted using the phrase “generative artificial intelligence & business management”, resulting in a total of 14 matches. It was noted that 14 studies from the most recent screening were also included in the 45 studies from the prior screening. Consequently, the second keyword was excluded. Due to insufficient data received from the initial search in the Web of Science database, the required information was gathered from the Scopus database. Some research is inaccessible in their entirety, but their inclusion in the analysis does not have a negative impact on the results. Hence, all papers pertaining to the objective of our study were incorporated into the analysis. The study employs a combination of bibliometric analysis, which examines the frequencies of data, and network analysis, which uncovers patterns and connections between data (Lewis and Alpi, 2017). Bibliometric studies, which utilise historical data, can unveil the interconnections among various entities, such as journals, scholars, and countries, with the goal of predicting future trends. Various tools, such as Gephi, VOSviewer, and R, are employed to perform these analyses. The choice of program is contingent upon the problem being addressed in the study, as well as the merits and limitations of such program. Certain programs may restrict the flow of data from specific databases. This study assesses the benefits and drawbacks of the instruments utilised for bibliometric studies, and conducted analyses using the R program. Multiple studies demonstrate the widespread use of VOSviewer. This program is mostly used to conduct analyses on co-authorship, bibliographic coupling, and co-citation. Recently, the field of GenAI has gained significant academic interest. The current literature is insufficient to conduct these citations and coupling analyses. Descriptive statistics are used instead of detailed bibliometric analyses such as co-citation, bibliographic coupling, and coauthorship. Analyses were conducted using the bibliometrix package in the R software. We examined studies in the Scopus database that included the keywords “business” and “generative artificial intelligence” for the analysis. 198 studies on “generative artificial intelligence” were identified in the business field. First, descriptive statistics on these studies are provided. Subsequently, the data on “annual scientific production” was examined, revealing a significant surge in research on “generative artificial intelligence”. The most productive journals and authors in this field were identified. The dominance factors of the authors were analysed to identify the most influential authors in this field. Finally, the bibliometric analysis section was concluded by determining the most used keywords and the most cited studies. 4. Results 4.1. Descriptive statistics In order to address the initial research inquiry, descriptive statistics encompassing the essential study data are initially provided. These statistics encompass the research quantity, number of writers, annual growth rate, average citations per document, average keywords AE Innovative Applications in Businesses: An Evaluation on Generative Artificial Intelligence 518 Amfiteatru Economic per document, and the most productive authors. The R program’s bibliometric package was utilised for doing descriptive statistics. Table no. 2 outlines fundamental research information, encompassing authors, and inter-author interactions, along with the total number of studies and the average number of citations per study. Table no. 2. Descriptive statistics Main information about data Authors Timespan 2021:2024 Authors 646 Sources (Journals, Books, etc.) 136 Author Appearances 731 Documents 198 Authors’ collaboration Annual Growth Rate % 135.1 Single-authored docs 35 Document Average Age -0.0455 Documents per Author 0.307 Average citations per doc 5.364 Co-Authors per Doc 3.69 Document contents Keywords Plus (ID) 327 Author’s Keywords (DE) 619 The study on GenAI in Business & Management was conducted through a Scopus database search. There are a total of 198 studies in the Scopus database as of December 15, 2023, with publication dates ranging from 2021 to 2024. Table no. 2 reveals that a total of 198 papers related to the field of business, spanning from 2021 to 2024, were discovered in the Scopus database using the search terms “generative artificial intelligence and business”. Despite its recent emergence in the scientific literature, the concept of GenAI is gaining popularity among experts. Studies in this field suggest that there is an annual average increase of 135.1 percent. The research in this field has an average age of -0.0455. The main factor for this phenomenon is the significant volume of articles that were released in early view and predominantly produced in the year 2024. A total of 1,064 citations have been made to research in this area, with an average of 5,364 citations per study. A total of 619 keywords were employed, along with an additional 327 keywords plus (ID). Keyword Plus (ID) phrases or words are not included in the title of an article, but are commonly found in the titles of the article’s references. This enhances the effectiveness of citation reference searches by identifying all articles sharing common citation references using cross-disciplinary searches. A total of 646 writers contributed to these investigations. A total of 35 studies are authored by only one individual. The average number of authors per study was 3.69, with a value of 0.307. 4.1.1. Annual scientific production Figure no. 1, generated using the R program’s bibliometrix package, illustrates the evolution in the number of research publications on the topic of interest. The initial study is a solitary artifact that originates in 2021. There are two studies conducted in 2022. There was a sharp surge in the number of studies in 2023. Approximately 600 writers have made contributions to the research on this particular topic. There was a total number of 1,063 citations of studies in this field. The average number of citations per study was determined to be 5,364. The average age of the research conducted is one year. This illustrates the novelty of this topic and the fact that the majority of studies remain up-to-date. Innovative Application of AI in Business Impacting Socio-Economic Progress AE Vol. 26 • No.66 • May 2024 525 of AI in freeing individuals from routine and boring tasks is not difficult. However, it is thought that the idea of transferring the creativity, which makes individuals feel unique and valuable, to a machine undermines self-perception. Therefore, during the adoption process of GenAI tools in businesses, issues such as employee resistance and demotivation can be encountered. On the other hand, it is also possible for GenAI tools to be associated with deviant workplace behaviours. It is also possible for the time gained from GenAI to be filled with activities such as coffee breaks and virtual slacking. Additionally, today, ChatGPT can even be listed as an author in academic articles. Therefore, research is needed on whether it leads to problems such as ease of use and diminishing productivity. In the process of adopting GenAI, it is considered necessary to develop adaptation and acceptance models (Gupta and Yang, 2023). Conclusions GenAI tools have begun to yield successful results in areas that require uniqueness, such as “productivity, creativity”. Businesses driven by concerns for efficiency and speed show significant interest in GenAI. In order to understand the academic reflections of this situation and examine the current state of GenAI in the business field, literature reviews and bibliometric analyses were conducted. This provides academics and practitioners with a concise research summary. The information obtained from this study aims to support practitioners interested in adopting GenAI solutions in businesses. The analysis was based on a keyword search for “generative artificial intelligence and business” in the Web of Science and Scopus databases, with the assumption that the data obtained from the Scopus database would provide a broader perspective. Therefore, analyses were conducted based on research results obtained from the Scopus database. A search without quotation marks in the Scopus database with the keyword “generative artificial intelligence and business” yielded access to 198 studies. The results of the research are presented in relation to the research questions. RQ1 - In which research areas, by whom, and in which years were studies conducted on GenAI and business? The studies spanned the years 2021-2024, with the majority conducted in 2023. This is because GenAI attracted attention in November 2022 with the launch of ChatGPT. It was observed that 646 authors contributed to the studies, with the number of single-authored studies being 35. Although GenAI is a subject of computer science, its potential is associated with almost all disciplines. Therefore, it can be said that researchers need collaboration to conduct studies. The findings of the study indicate that GenAI tools are used in various areas of businesses such as marketing, customer relations, accounting, human resources, and production management. The general trend in the studies is to understand GenAI tools and their usage. Attempts have been made to determine how these tools can be utilised for the goals of businesses, what advantages they can bring, or what challenges they may pose. This is thought to reflect the conceptualisation of GenAI as a relatively new concept. RQ2 - What are the connections between GenAI and business studies? What types of studies will these connections lead to? It was observed that the majority of the studies consisted of literature reviews and case studies searching for solutions through GenAI in a produced scenario. It can be understood that focusing on the forms of work and their advantages and disadvantages is necessary to determine how these tools can be integrated into businesses. AE Innovative Applications in Businesses: An Evaluation on Generative Artificial Intelligence 526 Amfiteatru Economic However, these studies only encourage businesses to produce solutions through ChatGPT for instant problems. Therefore, the literature is seen to need more empirical studies that involve methodological diversity. Additionally, future studies should examine sector-specific applications in various business fields such as production, marketing, finance, human resources, and determine their contributions to each sector. Moreover, it is considered important to focus on models and policies that can lead socio-technical regulations and facilitate humanGenAI collaboration, which can pioneer responsible AI applications. Furthermore, attention should be paid to developing policies and regulations that can provide a roadmap for practitioners regarding concerns about adopting GenAI tools and the ethical implications and security issues that may arise during their use. RQ3 - What are the opportunities identified in the current literature for the implementation of GenAI in businesses? Businesses can use GenAI to increase productivity, improve customer experiences, innovate, organise marketing strategies and advertising campaigns, accelerate decision-making processes, and automate tasks. As a result, they can gain a competitive advantage and differentiate themselves from their competitors. RQ4 - What are the potential threats or challenges associated with the implementation of GenAI in the business sector? Current challenges to overcome with GenAI include the risk of making mistakes, security issues, bias problems, effects on employee mental health, providing short-term competitive advantages, and creating shortcuts. To benefit from the power of GenAI tools, responsible AI applications must be incorporated, a balance between employees and technology must be maintained, robust and comprehensive security measures must be taken, governance frameworks must be determined, appropriate human resources must be developed, department-specific information management skills must be improved, and data collection and storage policies must be regulated. The desire of businesses to use GenAI tools is increasing. However, like any technology, the successful adoption of GenAI in businesses and achieving positive outcomes requires the right strategy, resource allocation, policies, and effective change management. The importance of making a range of configurations, from employee training to security measures, should be understood. Despite providing significant contributions, this research has some limitations. Firstly, the study may not fully cover all the literature. The studies are limited to databases such as Google Scholar, Scopus, and Web of Science. It is also possible that access to studies not indexed in these databases could not be obtained. The subject of the research is highly dynamic and continuously generates new opportunities and challenges. Therefore, the current findings are based on the literature at that time. The findings on the relationship between business management and GenAI were presented collectively rather than separately by branches. For example, distinctions such as its impact on tourism management or production management were not made. Additionally, an empirical experiment with additional datasets that could provide valuable perspectives on the effects, opportunities, and challenges of applying these technologies to real-world problems was not conducted. This study aims to increase the researchers’ subjective views, and more comprehensive studies can be conducted. 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