Artificial Intelligence and Blockchain Integration in Business: Trends from a Bibliometric-Content Analysis

Artificial intelligence (AI) is a technology that can perform complex tasks that require human intelligence, and it holds the potential of exceeding human capabilities (Agarwal et al., 2020; Pandl et al., 2020). AI is one of the main drivers of industrial development as it promotes the integration of emerging technologies in the Fourth Industrial Revolution (IR 4.0) (Goodell et al., 2021; Lim, 2019; Zhang et al., 2021), such as blockchain (Ehrenberg & King, 2020), cryptocurrency (Li & Whinston, 2020), cloud computing (Hsu, 2020), and internet of things (IoT) (Ghaleb et al., 2021). Indeed, the massive amount of data generated by IoT devices, social media, and web applications has fueled the proliferation of AI, wherein the data is utilized in the training of machine learning algorithms (Dinh & Thai, 2018). However, some concerns associated with AI exists. Specifically, privacy has become a critical concern as a result of a series of leaks and misuse of personal data. The Facebook scandal in which millions of users were targeted without consent by Cambridge Analytica, a third-party political firm, is one such example. Other growing concerns with AI include explainability and trustworthiness as the technology does not interact or speak with human users and thus cannot be verified or trusted (Dinh & Thai, 2018).

Likewise, blockchain has gained increasing attention as a technology with a wide range of applications in various fields (Dinh & Thai, 2018; Liu et al., 2019a). Blockchain, which became popular after the emergence of bitcoin in 2008 (Nakamoto, 2008), has remained as a disruptive technology that transforms the way we interact, trace transactions, and automate payments, among others (Roszkowska, 2020). Indeed, blockchain has opened the door of opportunities that enables the direct transference of value between its users in a secure and trusted manner. Moreover, with the execution of smart contracts, the checking of approvals and compliances can become simpler, as blockchain is a shared database that is synchronized across multiple sites, and thus, such activities can be agreed on by each participant in a distributed network (Soleymani & Paquet, 2020). The data in blockchain is stored in blocks with hash values and timestamps, wherein the blocks are created based on a consensus protocol such as a proof of work or a proof of stake (Dinh & Thai, 2018)—proof of stake is less expensive than proof of work due to higher energy efficiency (Karafiloski & Mishev, 2017). More importantly, all transactions are cryptographically signed on blockchain, wherein all mining nodes that hold a copy of the entire ledger verify every single transaction on the blockchain (Nakamoto, 2008; Zheng et al., 2017). Therefore, blockchain is cost effective and secure as it eliminates the need for a centralized authority to verify the transactions (Karafiloski & Mishev, 2017; Zhu et al., 2021).

The developments of AI and blockchain has propelled their integration to revolutionize the next digital generation ignited by IR 4.0. According to Salah et al. (2019), blockchain can offer explainability, privacy, and trust to AI-based applications, whereas AI can enhance scalability and security while resolving the personalization and governance issues for blockchain-based technologies. As indicated in Table 1, AI and blockchain are technically different in various ways, but they can be used to overcome the shortcomings of each other. In that sense, AI and blockchain are the yin and yang of digital business, wherein AI helps the business to understand, recognize, and decide, whereas blockchain supports the business to execute, verify, and record (Morrison, 2016).

In practice, the convergence of AI and blockchain has brought many new opportunities (Makarius et al., 2020). In healthcare, blockchain enables the secure storage of patient data. When access is granted, health professionals gain insights from this data through the patterns churned by AI. Noteworthily, their joint application has helped the healthcare industry to navigate the COVID-19 crisis (Fusco et al., 2020). BurstIQ, a blockchain-based company providing data solutions for the healthcare industry, is an innovative example that provides a health wallet based on blockchain, AI, and big data to manage patient data. The wallet provides patient health records to health professionals so that they can learn more about the patient’s health condition as and when necessary (Daley, 2019). The convergence of the two technologies is also re-inventing the financial services industry by increasing the speed of transactions and enabling trust among transacting parties (Soleymani & Paquet, 2020). Similarly, AI and blockchain has transformed supply chains by digitizing traditional paper-based processes, enabling trustworthy data sharing, and facilitating automated transactions (Yong et al., 2020). IBM’s food trust blockchain technology and AI platform is an example that has assisted small scale coffee and cocoa farmers to speed up their transactions and improve their farming decisions (Barbano, 2017).

In literature, the integration of AI and blockchain has been reported to produce wide-ranging applications for different sectors such as autonomous vehicles, finance, smart cities, and 6G networks. Dinh and Thai (2018) offered a conceptual articulation of integrating the two technologies and organizes their benefits through two categories in the form of AI for blockchain and blockchain for AI, whereas Salah et al. (2019) presented insights on blockchain applications for AI through a critical review of the extant literature, and thus, covers only the latter but not the former category. More recently, Pandl et al. (2020) sought to address this knowledge gap as they shed light on the convergence of AI and distributed ledger technology (or blockchain) using a systematic literature review, and thus, covering both categories. However, none of these reviews unpacked the applications of integrating AI and blockchain for business, which is the key activity underpinning economic activity and growth of a country.

Today, the language of business involves compound concepts such as dematerialization, disintermediation, and designing and producing goods on demand (Kumar, 2019). In this regard, success in the next industrial era requires companies to reconfigure their business models in ways where technology becomes central to their operations in order to address these changing demands in the future of work and marketplaces. AI and blockchain are powerful technologies that are well positioned for this endeavor as they hold the potential to reform existing processes for greater efficiency and seamlessness. Indeed, the paradigm of organizations today is transitioning from a hierarchical to a self-organizing model (Subic et al., 2020). While AI and blockchain have initially focused on the finance sector, companies today have come to realize its potential for other sectors, including agriculture, healthcare, logistics, manufacturing, and supply chains. (Pandl et al., 2020). Yet, no study, to date, have shed light on the peculiarities and opportunities for AI and blockchain integration specifically for business through a scientific consolidation of knowledge, which is arguably important for both future research (e.g., what else should we know) and practice (e.g., what should we do) in the field.

This article aims to address the identified research gap by exploring the utility of AI and blockchain integration for business. In particular, this article endeavors to identify the research peculiarities of the field and to explain how the integration of AI and blockchain can benefit different verticals of business in the era of IR 4.0. To do so, this article carries out a study using a bibliometric-content analysis to identify the publication productivity and the intellectual structure of the field. Following the convention of past bibliometric studies (Donthu et al., 2021b, c; Kumar et al., 2021a, b, c; Lim et al., 2022b, c) and the aims of the article, this study addresses the following research questions (RQs):

This article makes several noteworthy contributions. First, this article sheds light on AI and blockchain integration specifically for business, which to date, remains to no avail, wherein such insights are arguably important given that business is the engine of the economy, and thus, their transformation to remain relevant in the digital era is a high priority. Second, this article elucidates the applications and benefits that these technologies can offer to business, which represent important takeaways for both academia and industry. Third, this article represents the first study to perform a bibliometric analysis on AI and blockchain integration, which is an important endeavor to help interested scholars and professionals to gain a scientific overview of existing research in the field (Donthu et al., 2021a). While prior literature reviews on AI and blockchain avail, they remain limited in several ways. For example, the literature reviews by Omohundro (2014), Karafiloski and Mishev (2017), Dinh and Thai (2018), and Salah et al. (2019) are critical rather than systematic in nature, and thus, they are not replicable due to the absence of a review protocol and remain limited to the selective debates that they have deliberately chose to focus. The literature review by Pandl et al. (2020), though systematic, remains limited to manual, subjective insights. The use of bibliometric analysis herein this review can overcome the shortcomings of prior reviews as this review technique is predicated on statistical analysis on objective data (e.g., authorship, publication, citation) (Donthu et al., 2021a), and thus, represents a noteworthy step forward and a seminal contribution to the field. Table 2 presents the research gaps addressed by this study and maps the contribution of research questions toward the research gaps. The comparison of the focus, method, and contribution of the present review against prior reviews, which further accentuates the novelty of the present review, is presented in Table 3.

The rest of the article is organized as follows. The article begins with a disclosure of the methodology guiding its study, followed by a detailed presentation of the results from a bibliometric-content analysis. The article concludes with key takeaways from the study, with limitations acknowledged and suggestions for future research offered at the end.

The present study adopts bibliometric analysis to determine the performance (e.g., publication productivity, most influential articles) and intellectual structure (e.g., topics, themes, application areas) of the literature on AI and blockchain integration in business. In essence, bibliometric analysis is a well-established scientific method for analyzing a body of literature, wherein bibliometric data (e.g., publication and citation information) are analyzed using quantitative tools (Pritchard, 1969; Donthu et al., 2021a; Mukherjee et al., 2021). The methodology is a recognized method of scientific enquiry having applications in various disciplines, including business (Donthu et al., 2021b, c; Kumar et al., 2021a, b, c; Lim et al., 2022b, c; Zupic & Čater, 2015). Noteworthily, many methods to review the literature avail (Lim et al., 2022a; Paul et al., 2021), but the bibliometric method is the most objective due to its reliance on a review protocol and quantitative analytical techniques—the other review methods either lack a review protocol (e.g., critical) and/or remain limited to subjective interpretations due to the absence of objective analysis techniques (e.g., thematic) (Donthu et al., 2021a; Lim et al., 2022a). Specifically, the present study adopts the four-step procedure for bibliometric analysis recommended by Donthu et al. (2021a), which includes defining the aims and scope for study, choosing the techniques for analysis, collecting the data for analysis, and conducting the analysis and reporting the findings. The overview of the study’s methodology is presented in Fig. 1.

Findings from the bibliometric-content analysis are presented based on the research questions that they address. In particular, findings pertaining to publication productivity, influential articles, prominent themes, and promising application areas are related to the first, second, third, and fourth research questions, respectively, and the sections are organized as follows.

AI and blockchain are two of the hallmark technologies of IR 4.0. Although the two technologies are relatively different in their own ways, the subject of amalgamation has received interest among scholars and professionals alike. Yet, there is a scarcity of research that consolidates and examines the state-of-the-art insights on the applications of integrated AI and blockchain platforms in business. The present study sought to address this gap through a bibliometric-content analysis, and in doing so, provides five key takeaways.

First, the study clarifies that AI and blockchain are IR 4.0 technologies that were introduced in 2016, with early research that integrates the two technologies for business emerging in 2017 but proliferated exponentially only from 2019 onwards. Here, the proliferation of research witnessed reaffirms the study’s contention that AI and blockchain integration holds immense promise for business applications.

Second, the study identifies Mamoshina et al.’s (2018) article on the convergence of AI and blockchain in healthcare, Liu et al.’s (2019b) article on performance optimization for blockchain-enabled IoT systems, and Mao et al.’s (2018) article on credit evaluation system based on LSTM and blockchain as the most influential articles on AI and blockchain integration for business. It is important that prospective authors are cognizant of the seminal articles in the field in order to position and design future research in novel ways to extend the knowledge of prior research.

Third, the study reveals that “blockchain and machine learning”, “blockchain and smart contract”, and “blockchain and security” are the most popular topics for research, and the revelation of “blockchain and machine learning” as the sturdiest association in the corpus is another source of triangulation to highlight the promise and importance of AI and blockchain integration in business, wherein machine learning is the AI technology that seems to be most relevant and sought after for business endeavors.

Fourth, the study unpacks four major thematic clusters that underpins the intellectual structure of research relating to AI and blockchain integration for business, namely IR 4.0 and supply chains, smart healthcare, secure transactions, and finance and accounting. Here, two clusters are generic and thus transcend across industries (i.e., IR 4.0 and supply chains and secure transactions) and two clusters are specific and thus industry focused (i.e., smart healthcare and finance and accounting). The small number of major thematic clusters is an indication that research on AI and blockchain integration for business is relatively new, and thus, research that spurs its continued growth to enrich existing clusters and to propel the emergence of new clusters is highly encouraged.

Fifth, the study reveals 10 areas for AI and blockchain integration in business suggested by existing studies in the field, namely e-commerce; finance and accounting; healthcare; intellectual property right; management; marketing; smart manufacturing; social media; supply chain; and transportation, wherein exemplars of integrating the two IR 4.0 hallmark technologies for business endeavors are offered alongside its potential value and sources.

To this end, this article has contributed in four major ways. First, this article has mapped the publication productivity of AI and blockchain integration for business, showing that the field remains in its infancy and has tremendous scope for growth and future research. Second, this article has identified the most influential articles in the field, thereby laying the foundation for future research to build on the seminal insights curated herein. Third, this article has unpacked the most prominent topics and themes on AI and blockchain integration for business, which enables prospective authors to gain a one-stop state-of-the-art insights of literature in the field. Fourth and finally, this article has revealed 10 application areas for AI and blockchain integration in business, which should be useful for business professionals who wish to leverage on scholarly research to identify areas for business transformation using the two IR 4.0 technologies, including exemplars that they could follow.

This article also delivers important implications to different stakeholders, including business managers, AI and blockchain developers, information technology (IT) vendors, and future scholars. For business managers, the knowledge about the potential of integrating AI and blockchain in business can encourage them to develop and implement projects utilizing both these technologies to foster resilient business operations and improve the performance of firms (Fosso Wamba & Queiroz, 2021; Rubin et al., 2021). AI and blockchain developers also stand to gain acute insights on how integrating these two technologies creates business synergy. Therefore, AI and blockchain developers can collaborate to develop automated and decentralized business applications that offer better governance, higher performance, and greater security of user confidentiality and privacy (Siala & Wang, 2022). IT vendors will also be able to better identify the market for AI and blockchain business solutions and position the value they bring through the identified areas for AI and blockchain integration in business. Finally, future scholars will be able to advance knowledge in the field by building on the current overview of AI and blockchain integration in business and venturing into new areas that would create new streams or enrich existing streams of research in the field. In this way, this article empowers both professionals and scholars with state-of-the-art insights so that they will be aware of the opportunities for AI and blockchain integration to enhance the growth, resilience, and robustness of business practices.

Nonetheless, this article, like any others, concedes its limitations. First, the data in this article is bounded by the accuracy and completeness of its source, and in this case, Scopus. It is important to note that Scopus, as a scientific database, was not developed for bibliometric analysis, and thus, may contain (unintended) errors. To mitigate potential (unintended) errors, the authors have ensured that they cleaned the bibliometric data retrieved from Scopus to the best of their ability, wherein duplicates and erroneous entries are removed, as recommended by Donthu et al. (2021a). Second, AI and blockchain integration is a dynamic field of research and a radical transformation in practice. In that sense, new innovations in its application and integration are expected, and thus, new streams of research are likely to proliferate rapidly. Therefore, prospective authors should not only rely on the review insights herein, but also to keep themselves updated with the latest research in the field, which can be done by using the search string herein this study.

Moving forward, prospective authors interested in conducting new research on the integration of AI and blockchain for business are encouraged to invest their efforts to uncover the application of such an integration from a business rather than engineering lens, as the study herein flagged the scarcity of insights emerging from business research. To fertilize such endeavors, this article calls for new research that answers the non-exhaustive research questions on AI and blockchain integration through a business lens in general and by cluster, which we summarize in Table 11.

In short, this article makes clear that AI and blockchain integration is a global and legitimate phenomenon that holds tremendous promise for business, and that future research in this area should be enriched from the business perspective in order to complement the solutions offered through the engineering perspective and move the field forward. It is hoped that the insights herein this article will be useful for gaining a one-stop understanding of AI and blockchain integration as well as some prospective avenues for future research in the area through a business lens.