Artificial Intelligence and Industry 4.0? Validation of Challenges Considering the Context of an Emerging Economy Country Using Cronbach’s Alpha and the Lawshe Method

Abstract

Background: Artificial Intelligence has been an area of great interest and investment in the industrial sector, offering numerous possibilities to enhance efficiency and accuracy in production processes. In this regard, this study aimed to identify the adoption challenges of Artificial Intelligence and determine which of these challenges apply to the industrial context of an emerging economy, considering the aspects of Industry 4.0. Methods: To achieve this objective, a literature review was conducted, and a survey was carried out among professionals in the industrial field operating within the Brazilian context. The collected data were analyzed using a quantitative approach through Cronbach’s alpha and the Lawshe method. Results: The results indicate that to enhance the adoption of Artificial Intelligence in the industrial context of an emerging economy, taking into account the needs of Industry 4.0, it is important to prioritize overcoming challenges such as “Lack of clarity in return on investment,” “Organizational culture,” “Acceptance of AI by workers,” “Quantity and quality of data,” and “Data protection”. Conclusions: Therefore, based on the achieved results, it can be concluded that they contribute to the development of strategies and practical actions aimed at successfully driving the adoption of Artificial Intelligence in the industrial sector of developing countries, aligning with the principles and needs of Industry 4.0.

1. Introduction

Currently, Artificial Intelligence (AI) has been an area of great interest and investment in the industrial sector, offering numerous possibilities to increase the efficiency and accuracy of production processes [1,2,3]. With the availability of large amounts of data and the development of increasingly sophisticated Machine Learning algorithms, companies have found AI to be an important tool for improving decision-making and optimizing the performance of their operations [4]. AI has been applied in various industrial areas such as manufacturing, logistics, and energy, among others, with significant results in terms of cost reduction, increased productivity, and improved quality of the products and services offered [5].

Considering the importance of AI, it is worth highlighting Industry 4.0, which is a concept that has gained prominence in the industrial scenario, promoting the integration of advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and big data to enhance the efficiency and quality of production processes, as well as creating new business opportunities [6]. Through Industry 4.0, companies can implement a smart factory capable of using real-time data to promote vertical and horizontal integration of production processes, from product conception to delivery to the end customer [7]. However, the implementation of Industry 4.0 requires overcoming challenges, such as system integration and a shift in organizational culture that values collaboration and experimentation, along with a more agile and flexible management approach [8].

AI has become one of the most promising technologies in recent years [9]. Alongside Industry 4.0, AI has the potential to transform how companies produce, market, and deliver their products and services [8]. In this context, it is important to study the relationship between AI and Industry 4.0 to understand the implications of these technologies for the business world. Despite the increasing adoption of advanced technologies such as AI and Industry 4.0 in the industrial scenario, there is still a lack of in-depth studies that analyze the effects of these technologies and investigate the relationship between these two areas [6]. Analyzing this relationship is important to guide companies and governments in decision-making to avoid potential negative consequences. Understanding the impacts of AI on Industry 4.0 is crucial for the successful implementation of these technologies and the continuous improvement of production processes. Although there are studies that investigate AI in specific industrial areas such as manufacturing, logistics, and maintenance, there is a need for more comprehensive and integrated research that considers the relationship between AI and Industry 4.0 as a whole. This includes studying the effectiveness and efficiency of AI in production processes, optimizing data usage, and developing cybersecurity solutions to ensure the reliability and integrity of Industry 4.0 systems.

In this context, considering the specificities and characteristics of emerging economies, it is worth noting some challenges for technology and industrial development. According to data from the World Bank, insufficient infrastructure, lack of qualified human capital, and ineffective regulations are some of the main challenges negatively affecting the competitiveness of these countries. According to the Global Innovation Index 2020, only 3 out of the 50 most innovative countries in the world are low-income countries, and emerging countries represent only 17% of global spending on research and development (R&D), compared to 79% of developed countries. Additionally, many of these countries heavily rely on the export of raw materials and commodities, making it difficult to diversify their economies and adopt more advanced technologies. Another contributing factor to this situation is political instability, which affects investor confidence and can result in a lack of funding for innovation projects. Therefore, according to the importance of the context of emerging economy countries presented, this study considered the opinion of managers working in the industrial context of Brazil.

The adoption of Artificial Intelligence technologies in Industry 4.0 emerges as a problematic issue due to the challenges of various characteristics that hinder or limit this adoption. Therefore, the guiding question of this research was as follows: what are the challenges faced by industries operating in emerging economies regarding the adoption of AI in the context of Industry 4.0? Based on this scenario, to address this research question, the study’s objective was defined as identifying in the literature the challenges of adopting Artificial Intelligence and which of these challenges are relevant to the industrial context of an emerging economy country when considering the aspects of Industry 4.0.

In addition to this introductory section, this article is structured into four more sections. Section 2 presents the literature review, which addresses the main theories and research already conducted on Artificial Intelligence and Industry 4.0, identifying the knowledge gaps that the current research intends to fill. Section 3 discusses the Methodological Procedures that describe how the research was conducted, including information on the sample, the instruments used, and the data collection and analysis procedures. The following sections present the results with the associated discussions and, finally, the conclusions that synthesize the main research findings and the discussion of their implications for theory and practice. In the end, the list of references used in the study is presented.

2. Background

Industry 4.0 is a concept of automation and digitalization that seeks to integrate industrial production with information and communication technologies (ICT) [10]. The goal is to create intelligent production systems capable of making autonomous decisions based on real-time data and improving the efficiency and productivity of operations [11]. Industry 4.0 utilizes technologies such as the Internet of Things (IoT), Artificial Intelligence, big data, and advanced robotics, among others, to create a highly connected and flexible production environment [12]. This concept has the potential to profoundly transform the way industrial production is carried out, from product conception and design to manufacturing and distribution, and has been identified as one of the key trends for the future of the industry.

Corroborating with Industry 4.0, Artificial Intelligence (AI) emerges as a field of computer science that aims to develop systems capable of performing tasks that require human intelligence, such as learning, reasoning, perception, language, and decision-making [5,13]. AI uses algorithms and mathematical models to process large volumes of data, identify patterns, and draw conclusions from them [14]. Some examples of AI applications include speech recognition, image analysis, and fraud detection [15]. AI can be divided into different approaches, such as Machine Learning, neural networks, and fuzzy logic, among others [16]. Although there are still challenges to overcome for AI to reach its full potential, it has shown to be a promising area with significant impact in various sectors, such as healthcare, finance, transportation, and industry [17].

Considering this context of the relationship between Industry 4.0 and Artificial Intelligence, it becomes important to analyze the challenges in the adoption of AI in Industry 4.0, especially when considering the context of emerging economy countries. Below are some challenges identified in the literature.

2.1. Challenge 1—Lack of Clarity in Return on Investment

According to Bouanba, Barakat, and Bendou [18], in a study conducted on Moroccan logistics companies, 80% of the sample of companies do not use Artificial Intelligence strategies to improve supply chain performance. One of the causes for this high percentage is the issue with “budgeting,” as not all companies are willing to make a significant investment in AI due to the belief that there will be no return on their investment. As a result, these companies lose competitiveness and face disadvantages in terms of labor costs, time, and money. Deiva, Ganesh, and Kalpana [19] classify and discuss specific challenges of AI for a successful implementation of this technology, and economic challenges are presented as a barrier to adopting this technology. Small and medium-sized enterprises face challenges in embracing the benefits of Artificial Intelligence due to financial constraints. Additionally, an analysis conducted by the International Data Corporation (IDC) reveals that only 30% of organizations have achieved a 90% AI implementation rate, highlighting the low success rate as another significant obstacle to high investment. Supporting this perspective, Li et al. [20] investigate the relationship between Artificial Intelligence (AI), big data (BD), and advanced digital technologies (ADT) and reveal their potential integration in the design and implementation of intelligent energy management systems (SEM). According to the study, AI can be used for intelligent energy management, including energy generation forecasting, demand forecasting, demand-side management (DSM), optimized energy storage operation, energy theft detection, predictive maintenance and control, energy price forecasting, weather-related energy forecasting, and building energy management. However, to achieve these objectives, some challenges are present, such as quantifying the relationship between AI integration and economic benefits.

2.2. Challenge 2—Organizational Culture

Hradecky et al. [21] conducted a study on the application of Artificial Intelligence in the exhibition sector of the events industry, resulting in the finding that the European exhibition industry is slowly adopting AI, with some factors acting as motivators and inhibitors of this adoption. Among these factors is the organizational culture, where within the organizational dimensions, the support of top management is crucial for making crucial decisions and creating an environment conducive to innovation. However, it becomes evident that there is a lack of vision and progressiveness among CEOs regarding the adoption of AI, as the implementation of this technology is often left out of the strategic plan of many companies. Top management, in many cases, does not see AI as a way to reduce costs and drive strategic advancement, showing insecurity on their part concerning Artificial Intelligence, as they remain distant from this tool. According to the study by Rejeb et al. [22], there is a need to understand AI in the agri-food sector and ensure improvements for this industry, as it is one of the main contributors to the economy of any country and faces challenges such as climate change, unprecedented technological innovation, and increasing demands for sustainability, traceability, and transparency. However, for the implementation of AI applications to achieve productive and strategic benefits, such as task automation, profitability, and improved quality and safety of food, some challenges are listed by the authors. Among the challenges addressed are the organizational barriers to AI adoption, with a perception of reluctance to adopt new technologies in this sector, creating uncertainties about the value of AI in this industry. Wellsandt et al. [23] propose an approach regarding the predictive maintenance and the growing need for the adoption of technologies that automate this process to predict and prescribe maintenance actions. The study suggests the interaction between predictive maintenance systems through an intelligent digital assistant, where this assistant is Artificial Intelligence. However, the adoption of hybrid augmented intelligence in a predictive maintenance system faces challenges, including the need for convincing managers who have budgetary control to understand the benefits of cost and time reduction and increased quality, among others, and who are receptive to this technology and capable of justifying the investment. If this challenge is not overcome, there is a risk of compromising the fulfillment of AI expectations. According to Bouanba, Barakat, and Bendou [18], in their study on Moroccan logistics companies, a significant challenge in managerial choices is the persistence of the traditional management method. According to the authors, it was concluded that most managers are still rooted in traditional managerial strategies in their decision-making processes, ignoring AI technologies in agile innovations to improve supply chain performance, demonstrating that the concept of implementing these technologies is still not popular in most Moroccan logistics companies.

2.3. Challenge 3—Acceptance of AI by Workers

Ahmad et al. [24] conducted a study on seven different energy fields and their various applications, including electricity production, energy supply, electric distribution networks, energy storage, energy savings, new materials and energy devices, energy efficiency, nanotechnology, and energy policy and economy. Within the context of energy supply, utilities offer the possibility of providing their customers with renewable and affordable electricity while also promoting more efficient energy use among customers. In this scenario, there are many challenges regarding the versatility of developing AI applications and improving the quality of data training for ML algorithms. Among these challenges is the resistance of human workers who are sometimes associated with challenges such as a lack of trust, as the unpredictability in AI performance causes concerns. According to Johnk, Weissert, and Wyrtki [25], employees need to perceive AI as a tool and understand its applications. This context is part of the process of AI acceptance by workers since they can view this technology as an ally in performing their functions. By acquiring adequate knowledge on how to work with AI, employees will know how to utilize the technology to propose solutions within the organization, understand the possibilities of use, where and how it should or should not be applied, and have appropriate expectations regarding the results expected from AI. Wellsandt et al. [23] emphasize the importance of convincing factory floor and office employees to adopt a Hybrid Augmented Intelligence Assistant called DIA (Digital Intelligent Assistant) to obtain organizational benefits. The use of information, advice, recommendations, or actions provided by DIA is essential for employees to benefit from the assistance. However, it is crucial that DIA can enhance the individual tasks of employees, avoiding usability issues or lack of reliability; otherwise, it will not provide significant benefits. Failure to meet employees’ basic needs, such as workplace privacy protection and non-monitoring of individual performance, will result in greater distrust and lower acceptance of DIA by employees. Therefore, to achieve effective hybrid augmented intelligence, measures that increase the assistant’s reliability are necessary.

2.4. Challenge 4—Data

Baduge et al. [26] study the application of AI in architectural design and this sector covers the entire building lifecycle, from the conceptual phase to its completion. It highlights that AI has great potential, but it is necessary to consider the associated factors and challenges to fully harness its potential. Obtaining a high-quality dataset is a crucial consideration to address the problem at hand. This will allow exploring all the capabilities of algorithms and obtaining effective results in the field of architecture. Deiva Ganesh and Kalpana [19] point out that the absence of adequate standard information in the system hinders the construction of a successful AI framework. Adequate work with data is necessary for the use, inspection, or storage of large volumes of data in AI applications. To perform these tasks, it is essential to have new and efficient technologies. If there is no concern about this data organization structure, the performance of AI applications can be compromised. Javaid et al. [27] divide the study into five categories on the necessary conditions for AI adoption, emphasizing the following data-related needs: data availability, data quality, data accessibility, and data flow. According to the study, data availability is a crucial aspect of training and the effectiveness of Artificial Intelligence (AI) models in generating accurate predictions. Experts emphasized that the nature of data has a significant impact on AI preparation. Structured data, such as those arranged in two-dimensional relational structures, are more convenient for implementing conventional AI systems. In contrast, unstructured data, such as sound, visual, or graphical records, are essential in advanced AI applications, such as object detection. Regarding data quality, it is addressed that for models to function well and be trained effectively, high-quality data are required, and these data need to be suitable for specific use. It is highlighted that organizations sometimes struggle with the quality of historical data, needing to improve data preparation, data processing, and data quality assurance. Concerning data accessibility, it is necessary, according to the study, that this access is fast and practical, suggesting that organizations facilitate this access by centralizing data and allowing access to authorized personnel from various sources so that AI specialists responsible for dealing with AI can easily manage and use this material for their specific purposes. The need for a good data flow is also addressed, which plays a crucial role for AI specialists in transferring data from their source to their appropriate use. When this data flow is automated and smooth, it becomes easier to implement and maintain AI-based systems as they can process data continuously. This continuous approach allows AI systems to be updated and improved over time, providing more accurate and up-to-date results. By ensuring an optimized data flow, it is possible to maximize the potential and effectiveness of AI systems.

Jöhnk, Weissert, and Wyrtki [25] address the implementation of AI in agriculture, where solutions using Artificial Intelligence enable farmers to enhance production efficiency, improve crop quality, and reduce product time to market. The application of AI-based techniques, such as hyperspectral imaging and 3D laser scanning, plays a crucial role in monitoring and maintaining the health of crops [28]. These advanced AI-driven technologies allow for the collection of a large volume of accurate data, providing detailed information on the health status of plantations, and facilitating analysis and decision-making. However, building efficient AI systems depends on the availability of a substantial amount of data for training and accurate predictions. Although spatial data are widely accessible, especially in agricultural areas, obtaining temporal data is more challenging. Most data related to crops are available only once a year during the cultivation period. Additionally, maturing data infrastructure takes time, resulting in a time-consuming process of building robust Machine-Learning models. Soori, Arezoo, and Dastres [7] conducted a study on Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in robotics applications and highlight the challenges related to data collection. In the field of robotics, the challenge of obtaining large volumes of high-quality data for training AI and ML algorithms is one of the main obstacles faced. Data collection, labeling, and annotation can be time-consuming and costly, and the presence of noise and biases in the data can compromise the accuracy and reliability of models. This challenge is particularly relevant in the field of robotics, where data acquisition can be complex and subject to uncertainties and interferences. Efficient strategies need to be developed to address these difficulties and ensure the quality of data used in AI and ML systems for robotics. In Wellsandt et al.’s [23] study on Hybrid Augmented Intelligence in predictive maintenance with intelligent digital assistants, Conversation-Driven Development is a process in which employees contribute data from real conversations to train an assistant. Over time, the assistant becomes more accurate and capable of predicting correct responses. However, if the solution is not well accepted, there will be fewer conversations and opportunities to train the assistant, resulting in a downward spiral where employees avoid using it, and developers cannot improve its reliability, further reducing its usage.

2.5. Challenge 5—Data Protection

According to Ahmad et al. [24], intelligent manufacturing involves an interconnected network for the exchange of knowledge between production and machining units. Network communication, mainly through the Internet, requires data authentication and information at various global identity points and encryption. Ensuring the integrity of the system and the process is essential for the development of network structures and smart manufacturing. Additionally, data protection is a significant challenge currently, especially considering the growing role of AI in the energy sector. The energy industry faces vulnerabilities and risks of cyberattacks and data theft across its infrastructure. Before addressing data as a fundamental part of the national economy, it is crucial to ensure cybersecurity as an essential protective measure. Deiva Ganesh and Kalpana [19] point out in their research that systems in AI approaches differ from humans because they cannot understand the nuances of situations and derive appropriate meaning from them. Due to their limitations in understanding the inputs and outputs they process, these systems become vulnerable to unpredictable errors. This barrier, in turn, facilitates cyberattacks and risks that can affect application domains. To prevent the misuse of information systems, it is necessary to implement standard regulations, ethical guidelines, and appropriate policies. According to Hradecky et al. [21], in the study on Artificial Intelligence (AI) and organizational readiness to adopt AI in the exhibition sector of the event industry, regarding data management and privacy, researchers point out that large exhibition spaces are in direct connection with local authorities and policymakers. AI technologies rely on a large amount of data, and policymakers are committed to protecting the privacy of their customers. This collaboration between exhibition venues, authorities, and policymakers is essential to ensure the responsible and safe use of AI technologies. Jöhnk, Weissert, and Wyrtki [25] highlight the inappropriate use of results obtained through data used in AI technologies that can cause irreparable damage. Thus, to avoid unethical AI outcomes, organizations must be aware of biased learning and input data used. It is important to recognize that unconditional reliance on biased AI results can lead to accountability for discrimination, even unintentionally. To enhance the ethical readiness of AI, measures, and protocols must be established to prevent discrimination, thus reducing the associated liability risks. Li et al. [20] emphasize that the deployment of Artificial Intelligence (AI) faces significant challenges, especially regarding data security and cybersecurity. These challenges are particularly relevant when considering the application of AI in various sectors, including the use of AI models in renewable energy sources, such as wind, solar, geothermal, hydropower, oceanic, bioenergy, hydrogen, and hybrid energy. The successful integration of AI in these contexts requires careful measures to ensure data protection and the prevention of cyber threats, aiming to ensure the efficiency and operational safety of these innovative technologies. In Rejeb et al.’s [22] study, it is addressed that data privacy protection is a critical challenge in AI-based agri-food systems due to the absence of adequate privacy standards. The responsibility for decisions made by AI and the need for regulations on data processing and analysis are relevant issues to be addressed. Robust measures and specific regulations are indispensable to ensure privacy protection and promote the trust of those involved in these AI systems.

2.6. Challenge 6—Skilled Workforce

According to Ahmad et al. [24], AI process automation technologies represent a valuable opportunity for organizations, as they facilitate the workflow of frontline employees. However, it is essential to recognize that significant challenges still need to be overcome. Among these challenges, the need for trained employees capable of dealing with the complexities of Artificial Intelligence stands out. Additionally, the qualification of employees to work effectively with Artificial Intelligence is a crucial aspect to be addressed. Confronting these challenges is fundamental for organizations to fully leverage the benefits provided by AI process automation. According to Deiva Ganesh and Kalpana [19], the widespread adoption of AI technologies in smart infrastructure-based networks presents significant challenges related to network connectivity and monitoring. The development of AI systems requires advanced approaches that improve the interaction between humans and computers and facilitate the flow of information. However, the absence of adequate infrastructure and a skilled workforce has been an obstacle to the effective adoption of this technology in most organizations. Jöhnk, Weissert, and Wyrtki [25] emphasize that employee qualification through upskilling emerges as a fundamental strategy for the effective implementation of AI projects. This process involves acquiring interdisciplinary skill sets, ranging from knowledge in statistics, data management, and analysis to data engineering and expertise in specific domains. Given the scarcity of qualified AI specialists in the job market, investing in employee training becomes an imperative need for organizations. This approach aims to fill the skills gap and promote efficient and sustainable use of AI in organizations.

2.7. Challenge 7—Infrastructure

According to Ahmad et al. [24], in the context of challenges in energy systems and Artificial Intelligence, the deployment of intelligent production technology faces challenges in synchronizing between old and new equipment, especially due to the incompatibility of communication protocols. To support the new machines in the energy sector, adopting a new protocol to replace the old ones is necessary. This approach allows for coordinated operation between existing and new equipment, resulting in reduced equipment replacement and favoring the economic efficiency of the system. Deiva Ganesh and Kalpana [19] emphasize in their study that the lack of adequate intelligent infrastructure for information acquisition and storage, along with limited network assistance and high investments, has led many organizations to struggle in transforming their practices efficiently. Successful implementation of AI systems requires advanced approaches that enhance the interaction between humans and computers, thus facilitating the flow of information. However, the insufficiency of suitable infrastructure has emerged as a significant challenge in the widespread adoption of this technology by most organizations. According to Hradecky et al. [21], connectivity plays a fundamental role as infrastructure to enable the implementation of AI in the context of the exhibition sector. The adoption of AI in exhibitions is closely related to the technological capabilities that allow for the creation of autonomous networks. Javaid et al. [27] highlight some limitations of using AI in agriculture. For the authors, the lack of integrated and accessible solutions that effectively incorporate AI in agriculture represents a significant obstacle to the widespread adoption of this technology in the sector. Most farmers face time constraints and digital skill limitations to explore AI solutions autonomously. To seamlessly integrate AI into agriculture, it is necessary to integrate these new solutions into the existing systems and legacy infrastructure already used by farmers. This integration is essential to ensure the successful and efficient adoption of AI in agriculture. According to Jöhnk, Weissert, and Wyrtki [25], creating a modular IT infrastructure is essential to enable the integration of new AI applications, as well as to support intensive data training related to AI and testing procedures. In this context, organizations focus on developing three essential resources in IT infrastructure for AI: data storage resources capable of generating and storing large volumes of information, network resources for rapid access, processing, and data transportation, and scalable computing power resources to handle the workload demands of AI. In the study by Li et al. [20], in the context of a Smart Energy Management System (SEM), establishing adequate infrastructure is essential to handle the vast amount of data transmitted in real-time by various connected devices. This infrastructure must be able to efficiently process and analyze the received data to extract relevant information for system monitoring and control. Rejeb et al. [22] conclude in their study that robotic applications often require real-time processing, a task that can be computationally intensive and require specialized hardware. Additionally, to deal with large volumes of data, build models, and make real-time predictions, Artificial Intelligence/Machine Learning/Deep Learning systems require considerable processing power. This need represents a challenge for robotic applications, as robots are limited by energy and computational capacity constraints. Soori, Arezoo, and Dastres [7] point out in their study on the infrastructure of AI deployment in the agri-food sector that there are four categories of obstacles that are considered the main challenges of Artificial Intelligence (AI) in this field. One of these categories is related to AI’s technological constraints, such as connectivity, power supply, bandwidth, security, data validation, and integrity, network latency, response time, flexibility, and the need for big data in AI model training.

2.8. Challenge 8—Economic Factor

Deiva Ganesh and Kalpana [19] highlight that small and medium-sized organizations face difficulties in adopting advances in Artificial Intelligence (AI) due to the investment factor. Only 30% of organizations have achieved a 90% implementation rate of AI, emphasizing the low success rate as a significant barrier to the required investment. Hradecky et al. [21] note that, in the context of AI adoption, the predominant perception among participants is that larger organizations are seen as faster adopters, with greater potential and financial resources to incorporate new technologies, including AI. These larger companies can absorb the risks and initial costs associated with AI implementation and are known for their technological innovation. On the other hand, some participants observed that smaller companies demonstrate an innovative approach to AI adoption, leveraging their agility, quick responsiveness, and willingness to take risks despite budget constraints. Although larger companies have financial and human resources, they do not always excel in terms of agility and may face challenges related to slowness in decision-making processes. According to Jöhnk, Weissert, and Wyrtki [25], the life cycle of an AI application encompasses the step of adapting AI systems according to an organization’s specific context and data. However, AI adoption is a time-consuming and costly process. Moreover, implementing AI requires organizations to invest in building specialized knowledge and overcoming initial uncertainty regarding AI resources and their value.

2.9. Challenge 9—Ethical and Social Structures

Jöhnk, Weissert, and Wyrtki [25] address the ethical issues of AI implementation and emphasize that ethics in the application of Artificial Intelligence (AI) encompass the development of new methods to prevent unethical outcomes that may arise from biased learning or distorted input data. It is of paramount importance for organizations to be ethically prepared, as unquestioning trust in biased AI results can lead to accusations of discrimination, even unintentionally. According to Rejeb et al. [22], the implementation of automation and robotics in the agri-food industry has raised social concerns due to the possibility of replacing workers with machines. Despite the high costs of equipment and specialized labor, investments in these technologies can be advantageous due to the reduced workforce required, which offsets the high initial costs. However, the reduction in human intervention may pose significant challenges in terms of employment patterns. According to Soori, Arezoo, and Dastres [7], the use of AI and robotics entails significant ethical and social challenges. Concerns arise regarding the impact of automation on the job market, as well as the potential for AI systems to exhibit bias and reinforce existing inequalities. Moreover, there are apprehensions about the potentially harmful use of robots, such as in military or surveillance applications, raising issues related to privacy and security. These concerns demand a careful approach and regulatory measures to ensure a responsible and ethical implementation of these technologies.

Table 1. Challenges of using AI technologies.

Code	Challenge	Description	References
D_01	Lack of Clarity in Return on Investment	Understanding what benefits will be added after the implementation of AI, even if it requires significant investment.	[18,19,20]
D_02	Organizational Culture	The lack of vision from managers regarding the adoption of different management methods from the conventional ones and the willingness to adapt to the changes that this may imply.	[18,21,23]
D_03	Acceptance of AI by Workers	The lack of confidence in the solutions proposed by AI methods causes insecurity and low reliability, as well as resistance to adapting to new technologies due to a lack of familiarity or unfamiliarity.	[23,24,25]
D_04	Data	For the proper functioning of AI, a large amount of high-quality data that are suitable for the context in which they will be applied is necessary.	[7,19,23,25,26,27]
D_05	Data Protection	Proper care with the obtained data is essential to prevent misuse of information.	[19,20,21,22,24,25]
D_06	Skilled Workforce	Both the implementation and operation of AI technologies still lack qualified professionals to perform tasks, sometimes requiring organizations to invest in improving these skills.	[19,24,25]
D_07	Infrastructure	It requires high-tech and high-speed processors, robust computer networks, specialized hardware, connection to existing systems, and connectivity.	[19,20,21,22,24,25,27]
D_08	Economic Factor	The investment values in AI are quite significant and require the availability of top management to be willing to make these investments. Larger organizations tend to adopt AI more easily, while smaller institutions feel insecure or unable to make high investments.	[19,21,25]
D_09	Ethical and Social Structures	The adoption of AI raises concerns about job loss, perpetuating the idea of replacing human labor with automation.	[7,22,25]

provides a synthesized overview of the challenges to the use of Artificial Intelligence technologies in the industrial context identified in the literature.

3. Methodological Procedures

To conduct this study, the following four distinct stages were developed: (a) a literature review on the challenges for the adoption of Artificial Intelligence in the industrial context; (b) development of a research instrument (questionnaire) based on the challenges identified in the literature; (c) data collection through a survey with specialized professionals working in the industrial context of an emerging economy country, as well as the use of Cronbach’s alpha and the Lawshe method to validate the challenges in this context; (d) establishment of theoretical and practical discussions and conclusions about the results achieved.

Firstly, the scientific bases considered in the literature review were highlighted: Science Direct, Web of Science, and Scopus. This stage consisted of obtaining a foundation on the theme of challenges for the implementation of Artificial Intelligence in the industrial context. Specific terms and combinations were used to conduct the searches in the aforementioned databases, such as AI Challenges, AI Implementation, AI Application, AI Obstacles in Business, and Limitations of AI Use. Then, the challenges were cataloged and separated to verify which ones are valid in the context of an industry operating in an emerging economy country, as presented in the previous section (Table 1).

Next, a questionnaire was elaborated with the identified challenges, where experts had to respond to each challenge using a 3-point scale, as follows: 1—it is essential to overcoming this challenge to enhance the adoption of Industry 4.0 technologies and concepts; 2—it is important but not essential to overcome this challenge to enhance the adoption of Industry 4.0 technologies and concepts, and 3—overcoming this challenge is not important to enhance the adoption of Industry 4.0 technologies and concepts.

Through emails and social media, 209 invitations were sent to professionals working in the industrial sector to participate in the research. Google Forms was used to generate a link for the questionnaire to be answered. The survey response rate was 16.3%. It is worth mentioning that all participating professionals work in the Brazilian industrial context, which is the focus of this study as an emerging economy country. Among them, 11.76% are from the Northern region, 11.76% from the Northeast region, 14.71% from the Midwest region, 14.71% from the Southern region, and 47.06% from the Southeast region (the region with the highest concentration of industries in the country). Regarding job positions, 38.2% of the respondents are engineers, 20.6% are managers, 5.9% are analysts, specialists, supervisors, CEOs, and technicians, and 2.9% are coordinators, solution architects, Industry 4.0 heads, and consultants. Regarding their years of experience, 23.5% have been in the market for less than 5 years, 20.6% have been in the market between 5 and 10 years, and 55.9% have been in the industrial market for over 10 years.

After the data collection stage, Cronbach’s alpha and the Lawshe method were used to treat the data. These methods were employed to validate the challenges considered in this study in the industrial context of an emerging economy country. This stage was developed considering the guidelines presented by Silva [29] and Rampasso [30,31] and is based on the application of questionnaires to professionals who evaluate each criterion in three categories: “essential”; “important but not essential”; and “not important”, as per the scale used in the questionnaire highlighted above. Initially, the CVR (Content Validity Ratio) is calculated for each criterion in the questionnaire. The CVR values range from −1 to +1, where −1 indicates perfect disagreement and +1 indicates perfect agreement. When evaluating the adverse results obtained through the procedure, it is important to note that a positive CVR is achieved when more than 50% of the interviewees consider the item being analyzed as “essential.” On the other hand, a negative CVR is obtained when less than 50% of the interviewees indicate the item being analyzed as “not important.” When the CVR is equal to zero, it indicates that half of the experts considered the criterion as “essential,” and the other half did not [32]. Then, the CVRcritical is calculated, which is used to verify items that can be excluded from the final composition due to CVR values below the critical limit. The CVRcritical calculation considers the average, variance, and standard deviation parameters. All the aforementioned equations are detailed in

Table 2. Equations using the Lawshe method.

	Equations	Description of Variables
Equation (1)—Content Validity Ratio	$CRV=\frac{ne-\frac{N}{2}}{\frac{N}{2}}$ (1)	ne: number of specialists who consider the criterion as “Essential”; N: total number of specialists who participated in the survey.
Equation (2)—Mean	$\mu =n·p$ (2)	“n” is the number of respondents, and “p” is the probability of marking the item as essential.
Equation (3)—Variance	${\sigma }^2=n·p·\left(1-p\right)$ (3)
Equation (4)—Standard Deviation	$\sigma =\sqrt{n·p·\left(1-p\right)}$ (4)
Equation (5)—necritical	$necrítico=\mu +z·\sigma$ (5)	Significance level 5%, in the standard normal distribution, and the value of z = 1.96.
Equation (6)—CVRcritical	${CVR}_{critical}=\frac{necritical-\frac{N}{2}}{\frac{N}{2}}$ (6)	N: total number of specialists who participated in the survey.

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Finally, after conducting the necessary calculations, debates and discussions were carried out, involving an analysis of the results in light of the literature. Based on these analyses, conclusions were drawn, highlighting contributions to theory and practice, as well as directions for future research.

4. Results and Discussions

According to the procedures adopted and presented in the methods section, initially, Cronbach’s alpha was calculated, which resulted in a coefficient of 0.72 and which, according to [33], is above the minimum acceptable (0.70) to demonstrate the reliability of the research instrument used. After calculating the CVR values for each challenge considered in this study, we proceeded to calculate the critical CVR ${CVR}_{critical}$. It is worth noting that the total sample size in the calculations for this study was 34 industry experts working in Brazil. The calculated ${CVR}_{crítico}$ was 0.336, and this coefficient was considered for the validation analysis of the challenges. Therefore, the challenges that obtained a CVR coefficient greater than 0.336 were considered valid for the analyzed context, and consequently, challenges with a CVR coefficient value lower than this threshold were considered not valid for the industrial context of an emerging economy country, considering the specificities of Industry 4.0.

Table 3. Validation of challenges using the Lawshe method.

Code	Number of “Essential” Evaluations	Content Validity Ratio (CVR)	CVRcritical Validation Reference: 0.336
D_01	29	0.706	✔
D_02	30	0.765	✔
D_03	23	0.353	✔
D_04	33	0.941	✔
D_05	26	0.529	✔
D_06	21	0.235	X
D_07	19	0.118	X
D_08	14	−0.176	X
D_09	15	−0.118	X

presents the results obtained in this study.

4.1. Associated Discussions

By analyzing Table 3, it is possible to identify the validated challenges, according to the opinions of industry experts working in the Brazilian context. Therefore, to enhance the adoption of Artificial Intelligence in the industrial context of an emerging economy country, considering the needs of Industry 4.0, it is important to prioritize overcoming the challenges of “Lack of clarity in return on investment”, “Organizational culture”, “Acceptance of AI by workers”, “Quantity and quality of data”, and “Data protection”.

Regarding the challenge of the lack of clarity in return on investment, it is worth noting that adopting AI-based solutions requires significant investment, and resources need to be allocated to various fronts, such as infrastructure, personnel training, and the development of complex algorithms. Bouanba, Barakat, and Bendou [18] corroborate the perception of this fear of uncertain investment and also observe that the loss of benefits from not adopting this technology is significant when the advantages that Artificial Intelligence can generate are not known. Organizations hesitate to invest without a clear understanding of how AI can improve their efficiency and competitiveness. In agreement with this analysis, Deiva, Ganesh, and Kalpana [19] also highlight that high investments are a barrier to the adoption of AI, preventing the full utilization of the competitive and productive benefits of AI. However, one way to overcome this challenge is to rely on appropriate and tangible metrics that provide confidence and a basis for decision-making and robust investments to increase production efficiency, reduce costs, and improve quality, and customer satisfaction. Thus, feasibility studies, cost-benefit analysis, and economic modeling are crucial to estimate the expected financial and operational impacts. It is worth mentioning that this challenge (D_01) is correlated with the challenge D_08 (not validated); however, in the understanding of the professionals participating in this research, it is essential to overcome the challenge of understanding what are the added benefits after the implementation of AI, even if it requires a significant investment, given the challenge of considering overcoming the high investment amounts in AI and the availability of top management to be willing to make such investments. Therefore, understanding the benefits before considering economic issues in general is important to leverage the adoption of AI in the context of Industry 4.0.

Another important consideration is the organizational culture challenge. Wellsandt et al. [23] state that the active participation of managers is essential to understand the entire AI implementation process, both in terms of structural needs and costs. Thus, this participation can define the success or failure of AI implementation. Therefore, the successful implementation of AI requires a significant cultural shift within companies, involving the adoption of new practices, processes, and mindsets. Resistance to change and a lack of familiarity with AI technologies can hinder its adoption, and Bouanba, Barakat, and Bendou [18] corroborate this context, as traditional management methods can hinder the application of changes and improvements. Therefore, it is essential to promote an organizational culture that values innovation, continuous learning, and experimentation, thus encouraging the adoption of AI and driving digital transformation in the industrial sector of emerging economy countries.

Adding to this, the challenge of acceptance of AI by workers is discussed by Jöhnk, Weissert, and Wyrtki [25], as they emphasize the need for workers to understand the context in which they find themselves and the influence that AI will have on their work. Thus, the introduction of AI in the workplace can generate insecurity and resistance, due to the fear of job displacement or loss of autonomy. Wellsandt et al. [23] argue for the need to communicate to employees the knowledge about the assistance that AI provides and the benefits it can bring, leading to significant improvements in productive activities. Workers may fear a lack of mastery over the implemented technologies and the need to acquire new skills. Therefore, it is essential to promote training and awareness programs to ensure that workers understand the benefits of AI, such as increased efficiency and improved working conditions. Thus, to ensure a smooth transition and positive acceptance of Artificial Intelligence in the industrial environment, it is important to involve workers in the AI implementation process, creating a trusted environment that promotes transparency and worker participation.

Another point to highlight is the challenge related to data, both in terms of quantity and quality, as well as the difficulty in data protection. In this context, authors Baduge et al. [26] highlight that data are fundamental to achieving its full potential, requiring high-quality data that can be shaped to different needs. Ganesh and Kalpana [19] also affirm that the success of AI implementation is directly related to data quality and its absence causes obstacles to adapt to situations. AI relies on large volumes of high-quality data to generate insights and make accurate decisions. However, many companies in emerging countries face limitations regarding the availability and accessibility of such data. Additionally, the protection of sensitive data is a fundamental concern, as data collection and processing can raise issues of privacy and security. Ahmad et al. [24] point out that intense data connection and sharing can leave data vulnerable, and it is necessary to create mechanisms that offer protection and security to achieve greater control over sensitive and important content.

To overcome these challenges, it is necessary to invest in appropriate data infrastructure. In the context of enhancing the adoption of Artificial Intelligence (AI) in the industrial context of an emerging economy country, considering the needs of Industry 4.0, significant challenges have been validated. Organizational culture is characterized as an important challenge, as companies need to promote a culture that favors innovation and the acceptance of new technologies like AI. The acceptance of AI by workers is another challenge, as it involves adapting to changes in the work environment, structuring professional qualifications, and overcoming possible resistance. The quantity and quality of data also prove to be challenges to be addressed, as the effectiveness of AI depends on broad access to relevant and high-quality data for training and learning. Finally, data protection stands out as a relevant challenge since AI deals with sensitive information, requiring measures to ensure data privacy and security. Addressing these challenges is fundamental to enhancing the adoption of AI in the industrial context of emerging economy countries, meeting the needs of Industry 4.0, and driving innovation and competitiveness in the sector, including the creation of reliable databases and the implementation of effective security measures. Additionally, raising awareness and educating users is essential, and data protection policies should be established and followed to ensure compliance with applicable regulations and norms.

4.2. Implications for Theory and Practice

The achieved results present some implications for both theory and practice. From a theoretical perspective, this research expands our existing scientific knowledge, offering valuable insights into the adoption of AI and its challenges. The impacts extend to different research areas since AI has interdisciplinary applications. Furthermore, the research stimulates more in-depth studies into specific aspects of AI implementation and the investigation of innovative approaches and solutions to the challenges faced. The research fosters the emergence of ideas, strengthening the foundations of AI research in the industrial context of emerging economy countries. In other words, it contributes to researchers aiming to broaden the debates in the field.

On the other hand, from a practical perspective, the research provides essential outcomes about the challenges faced by companies in the process of adopting AI, enabling the identification of practical and applicable solutions and strategies. It can assist industrial managers in understanding specific challenges and developing context-adapted solutions, ranging from infrastructure and necessary skills to privacy and ethical concerns. Moreover, this research helps promote improvements in the industry’s efficiency and competitiveness by providing study directions on challenges that hinder process optimization, cost reduction, and the development of innovative products and services so that these challenges can be overcome in the future.

5. Conclusions

In light of the above, the proposed objective of identifying the challenges of adopting Artificial Intelligence in the literature and which of these challenges are valid in the industrial context of an emerging economy country, considering Industry 4.0 aspects, has been achieved. Thus, this research provided significant information about the challenges faced by industries in this context, allowing a deeper understanding of the barriers and obstacles that must be overcome for the effective adoption of Artificial Intelligence. The obtained results contribute to the support of practical strategies and actions aimed at successfully driving the adoption of Artificial Intelligence in the industrial sector of these developing countries, aligned with the principles of Industry 4.0.

In this context, important challenges were validated, such as the lack of clarity on the return on investment, which is a critical challenge, as the implementation of AI requires substantial investments, and companies need to have a clear understanding of the expected benefits and results. Organizational culture is identified as a crucial challenge, as companies need to promote a culture that favors innovation and the acceptance of new technologies like AI. Acceptance of AI by workers is another challenge, as it involves adaptation to changes in the work environment, requalification, and overcoming possible resistance. The quantity and quality of data are also relevant challenges since the effectiveness of AI depends on broad access to relevant and high-quality data for algorithm training and learning. Finally, data protection stands out as a fundamental challenge, as AI deals with sensitive information, requiring strict measures to ensure data privacy and security. Addressing these challenges is essential to enhance the adoption of AI in the industrial context of emerging economies, meet the needs of Industry 4.0, and drive innovation and competitiveness in the sector. It is worth noting that the challenges not validated in this study cannot be considered less important than the validated challenges. The validation considered the opinion of professionals working in the industrial context of an emerging economy country and that, in the opinion of this sample, overcoming the validated challenges is essential for the adoption of AI in the context of Industry 4.0.

The research presented some limitations, and as an exploratory study, its results cannot be generalized to contexts with different characteristics and specificities. It is important to recognize the inherent limitations of this type of research to avoid inappropriate conclusions or generalizations. However, it is crucial to emphasize that this research serves as a starting point for further investigations. This study highlights an initial survey of impactful aspects in the industrial sector that aims to adopt Artificial Intelligence technologies and discuss possible solutions to overcome the most inherent challenges in this context.

As for future research, a comparative analysis of the challenges in implementing Artificial Intelligence in different industrial sectors of an emerging-economy country is suggested. This research aims to compare the challenges faced in the implementation of AI in different industrial sectors, such as manufacturing, agriculture, healthcare, and services. The study seeks to identify patterns and differences between the sectors, allowing a more comprehensive understanding of the specific challenges faced by each of them. Additionally, it proposes to study the impact of cultural and organizational factors on the implementation of Artificial Intelligence in an emerging economy country. This research aims to investigate how cultural and organizational factors affect the implementation of AI in an emerging-economy country. The study explores how cultural norms, organizational practices, and hierarchical structures influence the adoption and acceptance of AI. The results provide insights for adapting AI implementation strategies considering the cultural and organizational particularities of the country. Finally, a study on evaluating the effectiveness of AI policies and regulations in an emerging-economy country is suggested. This research aims to evaluate the effectiveness of government policies and regulations regarding the implementation of AI in an emerging-economy country. The study analyzes how incentive policies, data protection, and governance are implemented and their impact on AI adoption. The research provides recommendations for improving policies and regulations to facilitate the safe and effective implementation of AI in the industrial context of this country.