The future avenues of artificial intelligence and decision-making in business management

Os futuros caminhos da inteligência artificial e da tomada de decisão na gestão empresarial

Abstract

Purpose:  In response to the growing integration of artificial intelligence (AI) and decision-making (DM) in business management, this study endeavors to provide a comprehensive understanding of the theoretical foundations, research trajectories, and emergent themes within this transformative intersection. By elucidating the evolving landscape of AI-driven decision-making, the research aims to offer valuable insights for scholars and practitioners, fostering informed decision-making practices and strategic advancements in contemporary business contexts.

Originality/value:  Methodologically, the study casts elements for AI and DM by conceptualizing, examining, and reviewing the field’s integration. The study also highlights the theoretical roots and classifies the main research themes in the literature strand.

Design/methodology/approach:  The study conducted a bibliometric analysis of 494 journal articles at the intersection of AI and DM in business management. It conducted two bibliometric analyses: co-citation analysis and co-occurrence analysis. The study also performed a qualitative review to criticize the obtained quantitative results.

Findings:  This research contributes to the domain’s understanding in three major ways. First, the theoretical roots by showing the most cited references. Second, the meta-analysis shows five pioneering studies in the literature suggesting the following research stages. Third, four distinct research themes are identified: 1. industry and society impact, 2. business strategies, 3. technological applications, and 4. decision systems. Lastly, the results highlighted research topics for future qualitative, quantitative, and mixed methods studies and provided recommendations for future research agendas alongside methodological theoretical and empirical guidelines for further investigations.

Keywords:
bibliometric analysis; decision-making process; artificial intelligence; literature review; performance management

Keywords:
análise bibliométrica; processo de tomada de decisão; inteligência artificial; revisão da literatura; gestão de performance

Resumo

Objetivo:  Em resposta à crescente integração da inteligência artificial (IA) e da tomada de decisão (TD) na gestão empresarial, este estudo fornece uma visão abrangente das bases teóricas, trajetórias de pesquisa e temas emergentes dentro dessa interseção transformadora. Ao elucidar o cenário em evolução da tomada de decisão impulsionada pela IA, a pesquisa visa oferecer insights valiosos para acadêmicos e profissionais, fomentando práticas conscientes na tomada de decisão e avanços estratégicos em contextos empresariais contemporâneos.

Originalidade/valor:  Empiricamente, este artigo apresenta elementos para a IA e a TD, conceituando, validando e discutindo a integração do campo, lança luz sobre as raízes teóricas e identifica os principais temas de pesquisa na literatura.

Design/metodologia/abordagem:  O estudo conduz uma análise bibliométrica de 494 artigos de periódicos na interseção entre a IA e a TD na gestão empresarial, realizando duas análises bibliométricas: análise de cocitação e análise de coocorrência, além de realizar uma revisão qualitativa para analisar os resultados quantitativos obtidos.

Resultados:  Esta pesquisa contribui para a compreensão do domínio de três maneiras principais. Primeiro, as raízes teóricas são reveladas através das referências mais citadas. Segundo, a meta-análise mostra cinco estudos pioneiros na literatura, sugerindo os próximos estágios de pesquisa. Terceiro, quatro temas distintos de pesquisa são identificados: 1. impacto na indústria e na sociedade, 2. estratégias empresariais, 3. aplicações tecnológicas e 4. decisão orientada. Por fim, os resultados destacam tópicos de pesquisa para futuros estudos qualitativos, quantitativos e de métodos mistos, tanto de forma teórica quanto empírica.

INTRODUCTION

The integration of artificial intelligence (AI) and decision-making (DM) within business management has gained momentum among scholars and practitioners. The infusion of AI into the strategic DM process is a vital shift from traditional DM paradigms and highlights a transformative era in corporate strategy and new business practices (Dwivedi et al., 2021). Historically, decision models relied heavily on human cognition, historical data analysis, and predefined rules (Adelekan et al., 2024). AI, by definition, includes machine learning, deep learning, neural networks, generative AI, and natural language processing, which has led to a new era of data-driven for the DM process (Korzynski et al., 2023). These technologies empower organizations to analyze large datasets, discern complex patterns, and derive actionable insights in real-time (Vrontis et al., 2022).

The DM within the business context is the process of selecting the most suitable choice from available alternatives based on predetermined conditions and goals. It involves the methodical examination of provided information, the use of judgmental conditioning, and prediction to determine which choice might be most to your advantage. Adequate DM is critical in business since it influences vital business performance metrics, including earnings, return on investment, and consumer demand (Leung et al., 2019). The merger of AI and DM technology has developed into a powerful technique for complementing decision processes in organizations worldwide. AI algorithms may analyze and evaluate vast amounts of data at an incomparable speed, recognize complex relations borders, and provide actionable realtime insights. By employing decision support systems supported by AI tools, a business may benefit from cutting-edge technical strategies while boosting DM to increase competitiveness in a fast-changing business environment (Stone et al., 2020; Schneider & Leyer, 2019).

AI facilitates predictive and prescriptive analytics, enabling organizations to forecast market trends, anticipate consumer preferences, optimize resource allocation, and mitigate risks with unprecedented accuracy (Milano et al., 2014; Hasan, 2024). It is important to highlight specific situations where AI tools enhance business DM. For instance, in supply chain and retail management, AI can optimize inventory levels and forecast demand with high accuracy, enabling all management levels (e.g., strategic, tactical, and operational) to make better decisions in a timely manner. This directly reduces costs and improves customer satisfaction through faster response times (Gupta et al., 2024). In the manufacturing segment, AI enhances traditional DM by enabling advanced predictive maintenance. By analyzing real-time machinery data, AI can foresee potential failures, reducing downtime and maintenance costs. This proactive approach optimizes maintenance schedules and improves operational efficiency, increasing productivity and cost-effectiveness (Van Horenbeek & Pintelon, 2014). Through iterative learning processes and algorithmic refinement, AI continuously improves its DM capabilities, fostering organizational agility, reducing uncertainty, and increasing resilience in an ever-evolving business landscape (Wu & Shang, 2020).

In the business context, AI also catalyzes innovation and competitive advantage, as shown by Bokhari and Myeong (2022), which illustrated the relationship between AI, social innovation, and smart decision-making within the smart cities context. The AI tools enable organizations to automate their routine tasks, optimize internal processes, and unlock new horizons for growth and operational efficiency. Verganti et al. (2020) showcased two major technological companies embedded into AI daily application and their internal performance. The impact extends across the entire value chain, from resource optimization to sustainable development and market segmentation (Ahmad et al., 2021; Helo & Hao, 2021).

Moreover, as shown by Davenport et al. (2019), AI accelerated personalized customer experiences that are shaping the future of marketing in the industry through targeted marketing campaigns, recommendation systems, and conversational interfaces. Also, based on AI tools, organizations gain deeper insights into consumer behavior, market dynamics, and competitive positioning, thereby enabling informed DM and competitive advantage in saturated markets.

The proliferation of AI tools has disrupted conventional decision models by challenging established norms and methodologies. From this perspective, it is possible to notice the use of AI in areas considered conservative due to sensible data. Kraus and Feuerriegel (2017) applied deep neural networks for financial decision support, and Leo et al. (2019) noticed an increase in machine learning for bank risk management. Unlike traditional rule-based systems, AI algorithms can uncover nuanced patterns and correlations within datasets, unveiling insights that may elude human analysts. This capacity enables organizations to make data-driven decisions based on empirical evidence and probabilistic reasoning, transcending the limitations of heuristic approaches. However, AI in business is not immune to barriers; Nishant et al. (2020) indicated that AI for sustainability is challenged by 1. overreliance on historical data, 2. increased cybersecurity risk, and 3. uncertain human behavioral response.

In the same stream, AI introduces novel ethical and regulatory considerations into DM processes, including algorithmic bias, privacy concerns, and accountability. As AI tools become increasingly autonomous and sophisticated, stakeholders must navigate these complexities to ensure responsible and equitable deployment of AI in business contexts. In scenarios where AI autonomously defines organizational decisions, it is worth noting that unexpected results may interfere with business DM processes. For instance, the potential risks of using AI are not merely related to technical issues; they can also negatively affect social, economic, and ethical dimensions (Cha, 2024). The concept of AI governance emerges to guide practitioners in developing and maintaining AI, adhering to regulatory principles. Responsible AI governance ensures that systems and technologies support individuals and organizations’ long-term strategies, safeguarding key stakeholders (Corea et al., 2022; Hickok, 2022).

Nevertheless, the intersection of AI and DM in business management represents a dynamic field ripe for exploration and innovation. By elucidating the theoretical underpinnings, identifying key research themes, and delineating avenues for future inquiry, scholars can contribute to advancing our understanding of this symbiotic relationship. Through interdisciplinary collaboration and empirical investigations, we can harness the transformative potential of AI to drive informed decision-making, foster organizational resilience, and create sustainable value in an era of unprecedented change. In this intersection, this study reveals a systematic literature review to understand the juncture of AI and DM in business management.

The present research aims to answer the following research questions:

1. What are the theoretical roots of research on artificial intelligence and decision-making in the business management domain?

2. What are the main research roots and themes within artificial intelligence and decision-making publications?

3. What are the new research avenues for expanding the artificial intelligence and decision-making field?

A bibliometric analysis of 494 journal articles in the field was performed to address these questions. Based on the methodological procedures from Bandeira et al. (2022), a tryad survey was conducted with the following methods: 1. quantitative analyses, co-citation analysis, and co-occurrence analysis, 2. qualitative review of the articles to obtain deeper insights into 3. the quantitative results.

According to the authors, this study represents a pioneering effort to conduct network analysis within the AI and DM domains, employing the selected methodology. The motivation behind this research emanates from the imperative to comprehend the evolution of this field and discern pivotal literature associated with specific themes. Also, employing a network analysis technique via CiteSpace software facilitates the identification of promising research themes through thematic clusters and the systematic mapping of the knowledge structure within the field.

This research offers several significant contributions. Primarily, it elucidates the knowledge structure in the literature by analyzing significant characteristics, central themes and clusters, key references, and pivotal junctures in the evolution of the interplay between AI and DM within a business context. Secondarily, it unveils a promising research area requiring pragmatic solutions from real-world organizations using AI. Additionally, this study underscores broader topics within DM that warrant further exploration.

Thirdly, this research employs a unique consolidation of three distinct methodological techniques related to network analysis of complex data, effectively identifying key references, thematic clusters, and progression within the context of AI and DM. These techniques—network analysis, map visualization of co-citations, and content analysis—serve as a foundation for researchers to advance future theoretical and empirical research employing qualitative, quantitative, or mixed methods research. The decision to employ exploratory network analysis is grounded in the belief that a comprehensive understanding can be attained when exploring a field of study through such techniques. By delineating these relationships, a broader spectrum of research themes and topics related to AI in DM can be comprehensively understood. These combined techniques offer an innovative means to visualize and analyze the diffusion of AI within the DM field, thereby supplying valuable insights into the mechanisms that either facilitate or impede this process.

This study endeavors to address empty space in the literature in multiple matters. Much of the existing AI research has not explicitly focused on amplifying potential pathways through enhanced DM processes. Specific challenges and opportunities related to AI in DM may have been ineffectively studied. Previous research efforts may have predominantly concentrated on AI and DM within specific regional or national contexts, thereby accentuating the emergency for a more global perspective or studies refining regions that have not received scholarly attention. Network analysis can furnish valuable insights into the relationships and interactions between various stakeholders in the field. While studies may have predominantly utilized cross-sectional designs, affording a snapshot of the situation at a given point in time, longitudinal studies could prove indispensable for understanding the evolutionary trajectory of AI and DM within the realm of business management.

The article is structured as follows: The following section details the methodological procedures for the bibliometric analysis. The section after that presents the results and discusses them. The authors conclude in the final section with concluding remarks, limitations, and new research opportunities.

METHODOLOGY

Data collection

This research is characterized as a bibliometric and exploratory analysis and aims to examine the theoretical roots, main research themes, and evolution of the intersection of AI in DM (Bandeira et al., 2022). Data were collected from the Scopus scientific database in April 2024. This scientific database was selected due to its recognition as the most comprehensive resource in the business management field (Mongeon & Paul-Hus, 2016). Publications were identified through a Boolean search by running a query for the following keywords: (“artificial intelligence” AND “decision-making” AND “business” OR “management”). The search terms were applied to the title, abstract, and keywords of each publication. Additionally, papers were filtered by source, document type, and publication stage (“journal”, “article”, and “final”) to identify the most influential contributions in the field. A ten-year timeframe, from 2014 to 2024, was used, and only publications in the English language within the accounting, business, and management areas were considered.

A total of 494 publications matched the search criteria after an independent manual review by two researchers to confirm the validity and relevance of the research string. A cleaning process was conducted to remove duplicate papers and those not focusing on AI and DM. The 494 publications were selected, enabling the identification of commonly addressed bibliometric information. As illustrated in Figure 1, citation metrics span from 2014 to 2024 and exhibit a prominent increase over the years.

Figure 1
Distribution of total publications (TP) and citations per cited publication (C/CP)

The number of publications exploring AI and DM has risen, especially after 2020, with 368 of the analyzed papers (74.49%) being published in the last five years, and 244 (49.39%) from 2021 to 2023. These results indicate a growing interest in the theme among scholars in the field. The number of citations per cited publication was calculated for each year to gauge the impact of the papers. Among the sampled documents, 399 publications – representing that 80.76% papers – received citations. The analysis of citations per cited publication reveals an increase in citations in 2021 and 2022.

An analysis of worldwide collaboration regarding the social structure reveals that publications were received from 72 countries, with a central focus on China, the United States, the United Kingdom, India, and France, among others, as illustrated in Figure 2. Despite this broad international participation, including scholars from Asia, Europe, and the Americas, there remains a noticeable gap in academic contributions from prominent regions such as Africa (e.g., Ghana, South Africa, Marrocos) and Latin America (e.g., Argentina, Peru, Chile).

Figure 2
Country collaboration map

Highlighted collaboration efforts between countries underscore the global interest in the intersection of AI and DM, with notable partnerships including the United Kingdom and India (n = 8), China and Australia (n = 7), China and the United States (n = 7), the United Kingdom and France (n = 6), and India and France (n = 5). This collaborative landscape reflects a significant global engagement in this area of research.

The top 10 most representative countries also provide interesting insights for those looking to find real-world applications and research trends. For instance, in Brazil, many papers are dedicated to practitioners. Coelho et al. (2022) reveal advancements in using AI in the industrial environment to enhance DM processes and achieve a competitive advantage. Moreover, Rocha and Kissimoto (2022) analyze how companies use AI to improve the flexibility and reliability of their operations, transform daily DM processes, and boost competitiveness.

Data analysis

Co-citation analysis

To enhance clarity and precision within the theoretical foundation of AI and DM in the domain of business management, the authors conducted a co-citation analysis. This method is founded on the principle that two articles are interconnected if both are referenced in subsequent papers. Co-citation analysis entails examining a list of articles within the sample to identify and quantify the frequency of concurrent usage of two specified terms, facilitating the identification of relationships. It is important to note that the number of citations directly correlates with the strength of the association (Aria & Cuccurullo, 2017). Moreover, co-citation analysis operates under the assumption that co-cited articles share a conceptual bond or similarity (Ferasso et al., 2020).

A co-citation network was constructed concurrently with a temporal analysis using CiteSpace software, a widely recognized academic tool for bibliometric analysis that enables researchers to discern patterns in scholarly data. In both perspectives, nodes serve as visual representations of references, while links denote the frequency with which two or more papers are cited together (Chen et al., 2012; Cui et al., 2018).

The metadata of 494 documents retrieved from the Scopus database were subjected to network analysis using CiteSpace version 4.0.R3. To determine the most appropriate parameters for the network, the authors conducted multiple tests based on chronological intervals, the number of top-cited references, and criteria thresholds. These tests are recommended to manage the complexity of the network and prevent obscuring its structure (Liu et al., 2015; Chen et al., 2019a).

An evaluation of the co-citation network with temporal slices of one, two, three, and five years, and the top-cited documents limited to 20, 30, and 40 references from each slice revealed that the optimal parameters were a two-year slice with the top 40 references. Additionally, node types were designated as references, link styles were configured in the Cosine class, and the final scope was constrained to the specified time intervals. Upon rerunning the procedure, empty spaces were observed, and the range period automatically adjusted from 2014 to 2024.

Finally, the co-citation network yields two outputs: 1. a graphical representation of key references elucidating the field domain, and 2. a time-zone view enabling the identification of structural trajectories, emerging trends, and pivotal moments in the literature presented.

Keywords co-occurrence nnetwork

A keyword co-occurrence network analysis was conducted to define the primary research themes within the realm of AI and DM in business management. This analytical approach provides a comprehensive understanding of the conceptual framework of the field by examining the interactions and interconnections among keywords. Keywords are deemed related if they appear together in the list of author-assigned keywords (Bornmann et al., 2018). Moreover, a stronger association is inferred between keywords representing core topics. Thus, the keyword co-occurrence network analysis is predicated on the premise that keywords share a connection when they co-occur, thereby enabling the classification of the research field into thematic clusters based on robust linkages among sampled keywords.

To execute the keyword co-occurrence network analysis, VOSviewer software version 1.6.18 was utilized. Leveraging the visualization capabilities of clustered networks and overlay views, the authors imported metadata from 494 publications to generate maps based on textual data extracted from abstracts (Perianes-Rodriguez et al., 2016). A total of 3,696 terms were identified, from which the software extracted 88 terms meeting the predefined threshold. Following the approach advocated by Ferasso et al. (2020), a cleaning process was undertaken to eliminate duplicate and unlinked topics, resulting in 73 validated terms. Additionally, to enhance the interpretability of results, the minimum group size was set to five, and smaller groups were merged.

Qualitative literature review

In order to explore the convergence of AI and DM domains from multiple perspectives, the authors undertook a qualitative literature review. This approach aimed to integrate diverse viewpoints and capture the essence of theoretical foundations, trajectories, and thematic trends within the studied field. The selection of the most influential publications followed the methodology outlined by Ferasso et al. (2020), facilitating an in-depth understanding of the subject matter and paving the way for delineating future research agendas.

A partially mixed-methods approach was used, combining two qualitative methods (co-citation analysis through CiteSpace and co-occurrence network via Bibliometrix and VOSviewer) with a qualitative technique (literature review). This allowed a robust and synthesized analysis to elucidate both conceptual and intellectual composition within the presented research domain (Cobo et al., 2011; Aria & Cuccurullo, 2017).

The analyses were conducted based on inductive analysis and bibliometric analytics and cross-checked by the authors (Snyder, 2019; Zupic & Cater, 2015). This approach provided a nuanced understanding of the subject matter and fostered robust insights into the interplay between AI and DM within the business management background.

RESULTS

Theoretical roots in AI and DM field

A co-citation network was constructed to delineate the theoretical foundations and cluster the most influential publications within the fields of AI and DM. Figure 3 presents the co-citation network, generated by CiteSpace software, comprising 786 nodes and 2,283 links derived from 28,992 unique references within the sample.

Figure 3
Co-citation network in the Al and DM research field

Influential references are identified based on their total citation count and their centrality within the network. Nodes represent references most frequently cited by the sampled documents, while links are established in accordance with the nodes’ betweenness centrality, which gauges their capacity to connect with other nodes (Chen et al., 2010). Moreover, pivotal connections within the network are identified based on levels of betweenness centralities; higher-level nodes indicate a greater capacity to connect with other nodes and influence the literature.

In the present research, Dwived et al. (2021) is the most cited article in the network. This study highlights the rapid pace of AI development and its implications across multiple sectors, bringing together insights from leading experts on the impact of AI tools on the future of industry and society. The second most cited research is from Tambe et al. (2019), which demonstrated advanced data science techniques to close the gap between AI and human resources; the authors proposed practical responses to the main challenges in the area by following three main principles – causal reasoning, randomization and experiments, and employee contribution.

The third influential article is by Syam and Sharma (2018) derived from theory to practice to identify the impact of machine learning and AI on personal selling and sales management, the author’s findings indicate that AI tools can be an active decision-facilitator but the human role is required in business-to-business context to understand social interactions.

While the fourth most cited research is the work by Yazdani et al. (2017) that illustrated the DM process for reducing costs and improving business competitiveness, the paper proposed supplier selection criteria that enable better decisions from the business manager. The fifth most cited document (Zimmer et al., 2015) reveals the major characteristics of the sustainable supplier management (SSM) with a focus on formal models to support DM; the authors’ findings indicated four single models that influence SSM: 1. qualitative approach, 2. mathematical programming, 3. mathematical analytical, and 4. AI.

To offer a longitudinal insight into the theoretical underpinnings of AI and DM, the authors constructed a time-zone visualization. Figure 4 delineates the trajectory of these theoretical roots from 2014 to 2024. Within the time-zone network, cited documents are organized into vertical time zones corresponding to their publication years (Chen et al., 2012), enabling the recognition of tendencies in the domain across different temporal slices.

Figure 4
Time-zone view of the co-citation network

The visualization reveals a surge in scholarly activity within the AI and DM realm from 2018 to 2021, as evidenced by notable connections and peaks. Noteworthy is the centrality observed across each time slice, suggesting that the contributions of Huang et al. (2018), Tambe et al. (2019), and Rai (2020) serve as foundational works for subsequent advancements in the field.

To gain further comparative insights into the impact of the selected publications on the AI and DM field, as well as their global influence, the authors employed the local citation score (LCS) and global citation score (GCS). The LCS denotes the number of citations a publication has received within the research domain of interest, while the GCS indicates the number of citations a publication has garnered across all documents indexed in the Scopus database (Chen et al., 2019b). An LCS high value means the publication’s significance within the investigated research field. Similarly, a high GCS value indicates that the paper has gained multidisciplinary recognition in the literature. Table 1 presents the ten most cited publications from the 494 selected documents, along with their LCS and GCS scores.

Table 1
Top ten most cited publications

Author	Title	Journal	LCS	GCS
Dwivedi et al. (2021)	Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice, and policy.	International Journal of Information Management	3	1.092
Tambe et al. (2019)	Artificial Intelligence in human resources management: Challenges and a path forward.	California Management Review	11	402
Syam and Sharma (2018)	Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice.	Industrial Marketing Management	7	387
Yazdani et al. (2017)	Integrated QFD-MCDM framework for green supplier selection.	Journal of Cleaner Production	0	319
Zimmer et al. (2015)	Sustainable supplier management: A review of models supporting sustainable supplier selection, monitoring, and development.	International Journal of Production Research	0	318
Vrontis et al. (2022)	Artificial intelligence, robotics, advanced technologies, and human resource management: A systematic review.	International Journal of Human Resource Management	3	296
Lu et al. (2020)	Developing a digital twin at building and city levels: Case study of West Cambridge campus.	Journal of Management in Engineering	1	256
Kannan (2018)	Role of multiple stakeholders and the critical success factor theory for the sustainable supplier selection process.	International Journal of Production Economics	2	251
Angelis and Ribeiro (2018)	Blockchain adoption: A value driver perspective	Business Horizons	1	229
Verganti et al. (2020)	Innovation and design in the age of artificial intelligence.	Journal of Product Innovation Management	4	228

The five publications with the highest LCS level are Tambe et al. (2019), Syam and Sharma (2018), Vrontis et al. (2022), Dwivedi et al. (2021), and Vendraminelli and Iansiti (2020), representing the most influential articles in AI and DM field of research. The publications with the highest GCS level are Dwivedi et al. (2021), Tambe et al. (2019), Syam and Sharma (2018), Yazdani et al. (2017), and Zimmer et al. (2015), three of which also have the highest LCS.

Promising research themes and directions

To delineate the primary thematic clusters within the AI and DM domain, a co-occurrence network analysis was conducted utilizing the abstracts of the selected publications. Figure 5 illustrates the term co-occurrence network, while Figure 6 depicts the temporal evolution of terms within the network, both generated using VOSviewer software. The term co-occurrence network facilitated the identification of four principal themes.

Figure 5
Term co-occurrence network on AI and DM domain

Figure 6
The evolution of terms in the co-occurrence network

The first cluster comprises 13 terms and is associated with AI’s impact on industry and society. The second cluster encompasses 28 terms primarily focused on business strategies related to AI tools. The third significant theme, consisting of 20 items, pertains to technological applications. Finally, the fourth thematic cluster (comprising 12 terms) is centered around decision systems.

Figure 6 addresses the chronological development of terms within the co-occurrence network. The analysis highlights that researchers have extensively explored topics such as data-driven methodologies, blockchain technology, ethics, data analytics, deep learning, innovation, technological development, Industry 4.0, circular economy, resource management, COVID-19, knowledge management, machine learning, big data, and behavioral research in recent studies.

Also, it is noteworthy to discuss the emergence of a circular economy within the context of AI studies, suggesting a growing awareness of sustainability and environmental considerations within technological advancements. Similarly, the inclusion of COVID-19 within research discourse underscores the urgency for advancements in crisis management strategies, reflecting the contemporary socio-economic challenges and the imperative for adaptive and resilient approaches. Subsequently, the subsequent section presents the four themes identified through co-occurrence.

Industry and society impact

AI’s influence on both industry and society unfolds through decision support systems, supply chain management, and economic theories. As decision-makers navigate complex landscapes, AI emerges as a pivotal tool, reshaping traditional frameworks and optimizing processes. From streamlining supply chains to enhancing maintenance strategies, AI’s transformative power reverberates across sectors, promising efficiency, and informed decision-making. This cluster epitomizes the symbiotic relationship between AI advancements and societal evolution, heralding a new era of innovation and interconnectedness.

Business strategies

The intersection of AI and business strategy takes center stage, unveiling a multifaceted landscape of DM, information systems, and economic dynamics. As organizations strive for competitive advantage, AI emerges as a linchpin, enabling strategic optimization and cost-effective solutions. From resource management to sustainable development, AI-driven strategies redefine traditional paradigms, fostering agility and adaptability in the market. This research theme represents the strategic imperative of embracing AI as a catalyst for growth and resilience in the modern business ecosystem.

Technological applications

At the forefront of technological innovation, this cluster embodies the myriad applications of AI, such as big data analytics, deep learning algorithms, neural networks, genetic algorithms, differential evolution, autonomous swarm intelligence, quantum-AI fusion, and others. As organizations harness the power of AI to unlock insights and drive innovation, the boundaries of possibility expand exponentially. From healthcare to human resource management, AI permeates diverse domains, revolutionizing processes and augmenting capabilities. This theme represents a paradigm shift towards data-driven DM and disruptive technological adoption.

Decision systems

AI emerges as a central force in DM, navigating complexities and mitigating risks in an increasingly volatile landscape. From disaster management to risk assessment, AI-driven approaches offer unparalleled insights and foresight, empowering decision-makers to confidently navigate uncertainty. Amidst the backdrop of global challenges such as COVID-19, AI’s role in crisis management and efficiency optimization becomes increasingly indispensable, underscoring the imperative for adaptive and resilient strategies. This cluster embodies the ability of AI to drive informed DM and shape a more resilient future.

Promising research directions for future inquiries

In this study, the authors intended to unfold the theoretical intersection of AI and DM considering the context of business management. Based on the theoretical roots and the presented conceptual and intellectual structure, Table 2 lists the future avenues for researchers within each of the four research themes.

Table 2
Future research avenues

Research themes	Main research questions	Suggested literature
1. Industry anc society impact	How do AI-driven decision support systems impact supply chain management in different industries and regions, considering cultural and regulatory differences? What are the economic implications of AI-driven decision-making for stakeholders? How do AI tools shape industry dynamics and societal structures in the context of emerging economies? How can quantum-AI fusion reshape manufacturing processes, supply chain management, and urban infrastructure development? What are the socio-economic implications of autonomous swarm intelligence in disaster response and environmental conservation? What role does AI play in enhancing workforce productivity and job creation in the context of Industry 4.0? What precautions or safety measures should be taken when using AI tools in business decision-making processes?	Pournader et al. (2021); How and Cheah (2024), Al Naqbi et al. (2024)
2. Business strategies	How can neurosymbolic AI synthesis optimize product development processes? What opportunities do federated learning networks offer for the decision-making process?	Akshansh and Vijaykumar (2023); Paragliola et al. (2023); Abu Daqar and Smoudy (2019); Trindade et al. (2023)
2. Business strategies	How can businesses leverage AI tools to personalize customer experiences and drive customer loyalty and retention? What emerging AI-driven business models are reshaping traditional industries?	Akshansh and Vijaykumar (2023); Paragliola et al. (2023); Abu Daqar and Smoudy (2019); Trindade et al. (2023)
3. Technologica applications	What are the potential applications of synthetic data ecosystems in addressing data bias and promoting inclusivity in AI-driven applications? How can AI-powered analytics and predictive modeling improve decision-making processes in areas such as healthcare, finance, and energy management? How can AI-driven decision support systems optimize decision-making processes in industries?	Dennehy et al. (2023), Benaben et al. (2020)
4. Decision systems	How can evolutionary swarm AI protocols optimize risk management strategies and crisis response plans? What role can consciousness-aware AI frameworks play in fostering ethical leadership and responsible AI governance? How can AI-driven decision-making enhance operational efficiency and resource allocation in organizations across various sectors? What are the challenges and opportunities in integrating AI algorithms into decision-making processes to improve sustainability and environmental stewardship?	Giraud et al. (2022); Madanaguli et al. (2024), Farrokhi et al. (2020); Schneider and Leyer (2019)

As for future investigations on the research theme of industry and society impact, further research implications may involve examination into the impact of AI in decision support systems on supply chain management for a wider range of industries and the countries they operate in. Longitudinal case studies can further help track the change over time. The comparison between multiple case studies on different contextual grounds might provide qualitative information about socio-cultural factors relevant to the perception and implementation of AI, resulting in relevant outcomes. Moreover, applying mixed-method research involving qualitative interviews with quantitative analysis could produce a clearer picture of the social aspects behind the processes at stake and reveal both positive results and potential challenges for the stakeholders implicated.

In the research theme of business strategies, the synthesis of neurosymbolic AI presents an opportunity for fertile research into the development of optimized and feasible product development processes. In this manner, industry-specific, in-depth case studies are recommended. Additionally, the business strategies theme attempted to present a case analysis of the investigation on the development of AI business models through longitudinal studies tracking the adaptation and performance of businesses using such models over long periods, providing comprehensive overviews of their workability and the effect on the industry. Regarding methodology, it is recommended to incorporate qualitative research methods, such as ethnography, to grasp the human aspects of AI in customer service in a more profound and distinctive light and quantitatively analyze the trends of customer patronization in the marketplace.

Further potential research for technological applications regarding AI analytics and predictive modeling is the thorough examination of ethical and responsible deployment and utilization of technologies in critical areas such as healthcare, finance, and energy management. Meta-analyses of current studies can point out the existing loopholes and most common methodologies, assisting in conducting a more profound and replicable study in the future. Moreover, cross-disciplinary cooperation between AI developers, ethicists, and social science professionals can widen the perception of responsible AI development and utilization and ethical AI deployment, considering the intricacies of the human factor that AI presents to society.

Finally, in the sphere of decision systems, it is possible to conduct qualitative studies about consciousness-aware AI frameworks for ethical leadership and responsible governance. Applying action research methods will allow for the coproduction of AI solutions to risk management and crisis management in practice settings, which will help to bridge the gap between research and practice by using the knowledge generated in the process. In addition, comparative case studies of diverse sectors can provide recommendations on the feasible application and value of AI-driven DM for organizations to increase performance efficiency and responsiveness.

CONCLUSION

This research aimed to synthesize advancements at the intersection of AI and DM by analyzing the theoretical framework, identifying theoretical roots, main research agendas, trajectories, and research themes within the business management domain. Likewise, this paper delineated future research avenues in the field. The AI and DM fields of study have proved to be in the spotlight, and future research directions are possible to increase academic and practitioners’ contributions in the context of business.

This study examines how AI and DM are addressed within business management literature. To answer the research questions, the authors conducted a citation and co-citation analysis, a keywords co-occurrence network, and a bibliometric analysis with a qualitative literature review of indexed studies from Scopus. A partially mixed method approach was used, building two qualitative methods (co-citation analysis and co-occurrence network) in consonance with qualitative technique (literature review), allowing a robust and synthesized analysis empowering the findings to elucidate both conceptual and intellectual compositions in the domain.

The study identified key theoretical roots and seminal works in the field, including the main cited contributions by Tambe et al. (2019), Syam and Sharma (2018), Vrontis et al. (2022), Dwivedi et al. (2021) and Verganti et al. (2020). The co-occurrence analysis identified four thematic clusters: 1. industry and society impact, 2. business strategies, 3. technological applications, and 4. decision systems.

However, the study is not without limitations. First, the research data was delimited to publications in the Scopus scientific database; future research needs to examine other databases to validate the findings in this research. Second, the keywords and selection criteria used several keywords to cover the literature. However, further research may consider using different combinations to shape the data collection stage. Third, other research streams could be explored in the AI and DM process, such as relationships with ESG (Bandeira et al., 2023a; Bandeira et al., 2023b), circular economy (Ferasso et al., 2023) and digital transformation (Valdivia et al., 2024). Finally, the authors cross-checked the content of this research; however, interpretations of the sampled papers during the qualitative analysis may vary from one author to another.

Based on quantitative bibliometric techniques and qualitative literature review, this paper extends the current state of the art and development in the AI and DM domain, contributing to international business literature by identifying emerging avenues for future inquiries. Despite its limitations, this study advances the field of AI and DM in business management by providing insights into key references, thematic clusters, and future research trajectories.

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Acknowledgement

This paper was funded by the Coordination for the Improvement of Higher Education Personnel (Capes) under grant no. 88887.804293/2023-00 – Finance Code 001 and the National Council for Scientific and Technological Deveopment (CNPq).

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Publication Dates

• Publication in this collection
  29 Nov 2024
• Date of issue
  2024

History

• Received
  25 May 2024
• Accepted
  30 July 2024