Artificial intelligence an essential factor for the benefit of companies: systematic review of the literature

Abstract

The implementation of artificial intelligence and 4.0 technologies helps business leaders make better decisions to reduce costs, improve team performance, and increase productivity. It also enhances the quality of work life of workers, allows the optimization of the information obtained from the equipment in real time, in this way even unscheduled maintenance can be avoided. A systematic review of the literature regarding the applications and benefits that artificial intelligence has given to companies is presented, the information was obtained from databases such as Ebsco, Scopus, MDPI, ScienceDirect. Analyses, concepts, and contributions that have benefited the understanding of the need to embrace new technologies 4.0 and artificial intelligence are presented, for the benefit of companies and workers.

Keywords:

• Artificial intelligence
• industry 4.0
• decision making, productivity, engineering, systematic review

REVIEWING EDITOR:

• Dr Zude Zhou, Academic Editor, Wuhan University of Technology, Wuhan, China

SUBJECTS:

• Industry & Industrial Studies
• Industrial Engineering & Manufacturing
• Engineering Technology

1. Introduction

Artificial intelligence (AI), considered a powerful tool for solving engineering problems, helps reduce human workload (Refaat Hassan et al., 2023; Singla et al., 2022; Utkina et al., 2023). The use of new technologies is very advanced in society, so their application in various fields such as data science, cybersecurity and artificial intelligence has become both useful and essential (Tobarra et al., 2021) (see Figure 1).

Figure 1. Technologies associated with artificial intelligence.

AI has gained a lot of popularity in the field of simulation technology applied to intelligent diagnostics; allowing industrial processes to be verified and managed in real time, ensuring the accuracy of data information (Li, 2022).

It has revolutionized several industries such as energy, food, and construction, offering as a competitive advantage greater efficiency in decision-making, better customer service and a reduction in projected costs. The future of business globally is somewhat promising with advances in learning algorithms, the integration of emerging technologies, and the improvement of natural processing language. If organizations have the thinking to drive technological innovation, they can achieve primacy and generate positive social impact (Masoodifar et al., 2023).

AI has gained a lot of popularity in the field of simulation technology applied to intelligent diagnostics; allowing industrial processes to be verified and managed in real time, ensuring the accuracy of data information (Li, 2022).

Artificial intelligence has revolutionized several industries such as energy, food, and construction, offering as a competitive advantage greater efficiency in decision-making, better customer service, and a reduction in projected costs. The future of business globally is somewhat promising with advances in learning algorithms, the integration of emerging technologies, and the improvement of natural processing language. If organizations have the thinking to drive technological innovation, they can achieve primacy and generate positive social impact (Masoodifar et al., 2023).

The advent of the new industrial revolution 4.0 and the intervention of artificial intelligence are helping industries around the world to embrace a digital transformation. The intervention of robotics, the Internet of Things (IoT), big data, computer simulation, cloud computing, cybersecurity, virtual reality provides strategies and methods that drive companies to successfully implement artificial intelligence in their daily lives (Vergara et al., 2021).

The focus of artificial intelligence is to improve product quality and productivity, reduce operating costs, provide sufficient information for decision-making, as well as alert people to possible failures in the operation of equipment or service. Industries that use AI to process data transmitted from IoT-based devices and equipment connected to each other, will have the ability to fully track end-to-end activities and processes (Banitaan et al., 2023; Jan et al., 2023; Javaid et al., 2022).

This article aims to propose a common definition and characterize the application of artificial intelligence in industrial engineering through a systematic review of the literature. A systematic review of the literature is conducted in Section 2. Similarly, Section 3 describes the methodology used for the literature search. Finally, in Section 4, a bibliometric analysis is carried out to identify patterns in publications on artificial intelligence for the benefit of companies.

2. Literature review

2.1. Collaborative robotics in companies (Cobots)

Collaborative robotics is also known as “Cobots” and refers to a partnership between a robot and a person on the same production line working simultaneously (Javaid et al., 2022). These collaborative robots are equipped with a handheld device that allows an operator to exclusively control the robot (Bi et al., 2021).

Artificial intelligence and robots are becoming more accessible and performing better, due to technological advancement and the new generation of processors. The functionality of robots is no longer entirely to perform one or more repetitive activities, but to be able to achieve self-learning and respond quickly to changes. These types of changes bring benefits such as reduced expenses and a better user experience (Borboni et al., 2023). AI and collaborative robotics have managed to find creative answers to the problems faced by businesses of all sizes and in all industries. Robots, when powered by AI, can solve problems and adapt new business strategies for the benefit of customers (Li et al., 2022).

Today, robotics is an inspiration for new projects in terms of collaboration between organizations. There are organizational techniques and solutions, such as sensory systems and direct programming, which can help recognize the presence of people and prevent potential collisions in work areas, that is, human-robot communication in real time without the generation of an accident (Zbigniew et al., 2019) (see Figure 2).

Figure 2. Applications of collaborative robotics in companies.

Collaborative robotics and modern technologies provide the opportunity to make workplaces safer and more productive for operators. A form of collaboration is when the human and the robot work together without any physical obstacle, which brings benefits to companies, since they can be sure that the development of the activities complies not only with the safety of the worker, but also that the applications of collaborative robots adhere to the four work modes instituted in the ISO/TS 15066:2018 standard, which addresses the safety of the Applications of collaborative robots in the enterprise (Kóczi & Sárosi, 2022; Pizoń et al., 2022; 2022).

The implementation of a Cobot in a production plant involves a series of tasks, as well as costs of the Cobot itself, installation, training, employee training, maintenance, electricity use, and programming related to the operation and operation of the Cobot (Kravets, 2020). All of this implies a high-risk decision-making for long-term investment, but with a potential adaptation to a new era of automation and continuous improvement.

Cobots and new 4.0 technologies are allowing operators to have an immediate response capacity and be able to adapt to the urgent needs of organizations (Javaid et al., 2022). Cobots provide a significant advancement for the automation of industries, including small and medium-sized businesses due to their small scale (Salunkhe et al., 2020). Hence, it has been estimated that sales of collaborative robots would increase their sales annually by an average of 12% between 2017 and 2022 (Taesi et al., 2023).

While Cobots and new technologies have brought considerable increases in an organization’s productivity and the interaction between them and humans has generated an important issue, which is trust. This issue remains key to managing the depth and breadth of products produced between worker and machine, ensuring regulatory practices and processes with more certainty for the organization (Pinto et al., 2022).

The competitiveness of companies at the international level depends on their ability to implement new technologies in their production processes, with actions that are friendly to the workforce and that guarantee a workplace with moral stability (Calitz et al., 2017; Cheng et al., 2021; Chromjakova et al., 2021). Robots and Cobots can self-diagnose thanks to the implementation of AI, which allows them to generate an efficient production environment due to making better decisions in real time, avoiding problems such as material handling, packaging, or palletizing (Aydin et al., 2020; El et al., 2022).

There are more than 8400 Cobots operating in more than 55 countries worldwide, and they continue to prove to be efficient in all types of manufacturing and logistics environments, especially for SMEs (Lefranc et al., 2022). The U.S. market has been increasing in manufacturing and assembly processes, ranging from 100 million dollars in 2016, to a projection for 2025 of 12 million dollars; This indicates that the integration of collaborative robotics is a good strategy for companies in the long term and brings benefits to human resources and their safety (Market & markets, 2023).

2.2. Security of information systems in companies

Cybersecurity is a safeguard, providing protection to electronic information systems, networks, and data. The pace at which cyberattacks are carried out destabilizes cybersecurity and makes it impossible for security experts to combat each new cyberattack (Akhtar & Feng, 2021; Dawson, 2021). While, with the entry of the new Industry 4.0 and the internet, crimes and cyberattacks have increased alarmingly, strategically they have also been effectively decreased with the use of neural networks, intelligent agents, and expert AI-based systems (Alneyadi et al., 2023; Das & Sandhane, 2021; Quirumbay et al., 2022; Vermesan et al., 2021) (see Figure 3).

Figure 3. Elements of cybersecurity.

AI under the framework of cybersecurity, helps organizations to observe, detect and counter threats while maintaining the reliability of information by implementing machine learning algorithms, which are the most appropriate way to provide solutions to possible computer attacks (Tao et al., 2021). Enterprises are gathering high-level information to test and analyze data to identify the most common attacks to counter persistent threats (Adam et al., 2016; Yang et al., 2017).

The advent of wireless communication network technology such as autonomous robots, the Internet of Things, machine learning, artificial intelligence, and virtual reality, brings a high speed in the transaction of information and excellent reliability (5G) of live information, which helps to detect anomalies in systems and provide solutions as quickly as possible (Arjoune & Faruque, 2020; Oleksiewicz, 2022).

The first sources of vulnerability are the lack of maintenance of legacy systems, updating old technology and software. To control this problem, companies must be in constant communication with the operational part and suppliers of the systems, so that they are renewing themselves and do not present gaps in their systems and therefore vulnerability (Clim et al., 2022).

AI and the new industrial revolution 4.0 are helping companies to improve their information systems, to be able to detect malicious attacks right now and make the best decisions with effective solutions. This allows companies to believe that artificial intelligence is there to optimize and meet large-scale objectives and be at a world-class technological level.

2.3. Predictive maintenance in companies

Artificial intelligence in predictive maintenance (PM) is an important element when talking about smart manufacturing in the new industrial revolution 4.0. There are learning methods in AI such as Deep Learning (DL), which can replace conventional methods with modern diagnostic methods (Abood et al., 2022).

PM is one of the fundamental parts of smart manufacturing, as it reduces line stoppages, maintenance costs, increases the safety and life of the equipment, achieving considerable benefits to the company (Zonta et al., 2020). Increasingly, the PM has gained value by proposing the integration of different research related to the acquisition, distribution, and security of information. The impact of maintenance represents a total of 15%–60% of the total costs of a company’s operations, so the implementation of Industry 4.0 shows its differentiation in terms of the growth of predictive maintenance over corrective maintenance (Mulders, 2017).

Predictive maintenance is considered one of the most important strategies for managing and building a comprehensive system based on Industry 4.0 (Almobarek et al., 2023; Villa et al., 2022). Applying AI and 4.0 technologies in predictive maintenance ensures that machines can produce products of the required quality and quantities at a lower cost and in the shortest possible time (Mesarosova et al., 2022). Another of the capabilities of new technologies related to predictive maintenance is to determine the right time to execute maintenance using statistical methods and artificial intelligence (Pech et al., 2021).

In industries, equipment maintenance is an important key, not carrying it out, impacts the operation time of the equipment and its efficiency. That is why faults must be identified and resolved, avoiding unscheduled process stoppages. AI and machine learning (ML) are promising tools to combine them with predictive maintenance and thus sponsor failures in production systems (Carvalho et al., 2019; Saman et al., 2023).

While state-of-the-art technologies are beneficial for the company, at the beginning a significant amount of historical failure data is required for learning to begin and characterize them, that is, ML and DL algorithms must be trained to work properly, so that conditions are efficiently controlled for production systems (Niu et al., 2020). Therefore, predictive maintenance has focused on detecting and diagnosing faults that have alleviated traditional drawbacks (Drakaki et al., 2022) (see Figure 4).

Figure 4. Contributions of predictive maintenance.

Predictive maintenance in organizations has been favored with the advent of Industry 4.0 and is an important part of smart manufacturing. Machine learning has replaced conventional methods of diagnosis with more modern methods where the company has benefited considerably (Fonseca et al., 2023).

2.4. Process automation in organizations

Robotic process automation is one of the techniques available to companies looking to improve performance (Pedretti et al., 2021). The application of artificial intelligence can effectively improve information analysis and processing capacity in automated control, improving production efficiency and reducing some problems that are not possible with traditional technologies (Chen, 2021).

Today, many industrial teams are responsible for much of the basic work that is done in companies, they are used from system automation, process robotics and even natural language translation to respond to customer requests (Subramayam & Patagundi, 2018).

In the fast-paced environment of organizations, automation has gained importance within construction processes and manufacturing processes, these have benefited both industrial and medical organizations, as they use digital systems, algorithms, and technological devices such as the Raspberry PI 4 (Kumar et al., 2022).

Artificial intelligence and robotics coupled with automation are making their way into almost all functions of the organization and human resources; They are helping to reduce the execution time of people in the process, increasing efficiency and productivity (Meduri & Yadav, 2021). The technologies are designed to provide a possibility to optimize the management of processes and improve the development of the organization by providing a new level of efficiency and quality of customer service (Uskenbayeva et al., 2019).

Robotic process automation (RPA) has become one of the most widely adopted technologies by companies as a solution for employees to focus on more complex tasks, while delegating individual and routine tasks to digital systems (Costa et al., 2022) (see Figure 5). Automation promises communication service providers (CSPs) the ability to speed up the labor-intensive manual processes of their network operating centers (NOCs), providing opportunities for operators to achieve network performance and give time to innovative ideas in their own NOCs (Deepika et al., 2022).

Figure 5. Phases of robotic process automation.

Automation and technologies based on the Internet of Things (IoT) must be combined with an appropriate organizational structure to maximize the advantages of manufacturing technology. From having a linear system to an organizational network for the achievement of strong organizational innovation (Qilin & Yue, 2022).

RPA allows efficiency in companies, this efficiency is an essential task of business leaders concerned with organizational survival and success, generating industrial competitiveness (Siderska, 2020). The integration between development and operations allows for improved communication and collaboration. Industry 4.0 also helps companies to develop their products and services quickly, focusing on testing and continuous monitoring through the automation of activities, allowing processes to be digitized quickly and safely (Suescún et al., 2021).

Automation is offering benefits that improve quality and productivity, reduce waste, and give stability to the production process. Employing machine learning helps monitor and perform real-time analytics for continuous improvement (Ayadi et al., 2022).

2.5. Artificial intelligence-assisted decision-making

With the development and application of new technologies, companies can employ Big Data and Technology 4.0 to support them in making crucial decisions and solve major global development problems to increase their competitiveness (Liu & Hu, 2022). Decision-making as the central axis of business management is very dynamic, which is why they play a decisive role in the company’s operations (Sourdin, 2018) (see Figure 6).

Figure 6. Elements of decision-making in companies.

Transparency in AI decision-making is the degree to which a system releases objective information about a working method. Transparency refers to the availability of information perceived by workers (Eslami et al., 2018). AI has become a powerful tool that allows complicated tasks to be completed efficiently (Praveenraj et al., 2023).

Wireless communication can be used for the installation of modern equipment, allowing for better management of physical spaces, which motivates the development or experimentation of new ways of working (Xia, 2021). This type of communication contributes to the speed of assisted digitalization and, with artificial intelligence, gives way to the introduction of the company to a digital society (Ivanovich, 2021).

While AI can improve the social well-being of companies, it also creates decision-making dilemmas such as algorithmic discrimination, information bias, and unclear results. This type of impact can be caused by incomplete data and management errors, which are the main sources of ethical risks. To solve these risks, there are governance strategies from the perspective of management, research, and development (Baker, 2020; Guan et al., 2022).

Artificial intelligence is based on computer vision, intelligent voice, data processing, images, videos, and other technologies, which generates a positive impact on organizational management. Big data information has become a symbol of the new era of information control for better decision-making (Kang & Zeng, 2022). After entering the information age with Big Data, people can make better decisions based on the results of data analysis and generating an efficient demand in the collection of information, making the processes fully automated.

IoT and AI have increased the degree of equipment, as well as improved the interconnectedness of all things (Ibrahim et al., 2022). The application of these technologies will set a new technological trend (Peng & Krutasaen, 2022). The use of artificial intelligence provides a competitive advantage for different areas, ranging from smart cities, influencing governance, and promoting human capabilities (Alloulbi et al., 2022).

Technological advances are increasingly interfering with companies and people’s daily lives, whether by facilitating industrial processes or creating new methods of solving problems. AI is seen as a technology that comes to revolutionize the different processes of organizations, expanding the panorama for better decision-making (Mirbabaie et al., 2021).

2.6. Technologies and artificial intelligence in business

The future of AI in business holds promise with new breakthroughs in the creation of new learning algorithms, integration of emerging technologies and natural languages, which have improved industrial processes. AI is also a discipline that is making inroads into the business arena where companies use technology as a commercial strategy (Waqas & Amin, 2022) (see Figure 7).

Figure 7. Advances in artificial intelligence business.

One of the branches benefiting from AI is the automotive industry; AI is used strategically to support business sustainability so that a market can exist at any time without having a physical point between seller and buyer (Sabin et al., 2023). Telecommunications are a key tool for business, allowing organizations to communicate efficiently with customers by providing excellent service (Banu et al., 2022).

Big Data and the Internet of Things have provided the business industry with new interactions that have improved business understanding with the construction of new digital platforms and structures, which have benefited the way people work (Abu, 2023). Also, the inclusion of platforms based on digitalization and artificial intelligence provides an impetus for the emergence of new business models that help improve efficiency in terms of flexibility in business services (Zhang et al., 2022).

While artificial intelligence and new technologies are helping managers make the best decisions in their companies, the success of a business model is determined to provide not only a competitive advantage, but also to be accepted by society (Dehler et al., 2021). While technologies are helping companies to have better control of information, the use of intelligent business (BI) applications is a key component, which modern companies need to handle huge amounts of data in each of their production processes (Farooqi & Khozium, 2022).

Some governments globally are implementing cloud operations, developing online platforms, e-commerce, banking, and e-signatures, being today a whole new level of government and industry efficiency (Dirican, 2015).

There are countries around the world, such as China, that are continuously developing technologies focused on intelligent business management, including information technologies and Big Data, reducing management costs, economic risks and improving labor efficiency. All these benefits have led to an expansion in the commercial reach of companies (Li, 2022).

The use of artificial intelligence has generated business opportunities in the markets with the implementation of marketing philosophy; and they have ample potential for many fields, due to the rapid development of technologies, artificial intelligence and automation which can be expensive but useful (Horák & Turková, 2023; Nicolau, 2021; Rajagopal et al., 2022).

3. Methodology

In this article, an exhaustive literature search was initially carried out in several renowned databases, such as Ebsco, Scopus, MDPI and ScienceDirect (see Table 1). It focused on several key research topics, ranging from collaborative robotics in business environments, information systems security, predictive maintenance, automation, artificial intelligence applied to decision-making, to technologies and artificial intelligence in the business environment.

Table 1. Search process to obtain information.

Database
Ebsco		Scopus	MDPI		ScienceDirect
Search topics
Collaborative robotics in companies	Security of information systems in companies	Predictive maintenance in companies	Process automation in organizations	Artificial intelligence-assisted decision-making	Technologies and artificial intelligence in business
Information analysis
Author’s name	Magazine name	Year of publication		Keywords	Country of publication or origin

Subsequently, an information matrix was developed based on the findings collected from these investigations in the databases. This matrix included parameters such as the author’s name, the journal in which it was published, the year of publication, relevant keywords, and the country of origin of the research or publication. Importantly, we prioritized information in English, with some inclusions in Spanish, and limited the search to a period of 9 years, from 2015 to 2023 (see Table 2).

Table 2. Literary search results matrix.

Author	Journal	Year	Keywords	Country
M. Refaat	Ain Shams Engineering Journal	2023	Artificial intelligence, Flow network, components assignment problem, reliability evaluation, ANN	Egypt
D. Singla	Mol Cell Biochem	2022	CNN, DSC, Imaging, Cancer	USA
M. Utkina	The Law, State and Telecommunications Review	2023	Artificial Intelligence, Intellectual property, Intellectual property law	Ukraine
L. Tobarra	Applied sciences	2021	Cloud learning environments, gamification, learning analytics (LA), data privacy management, standards, game-based education, cybersecurity	Spain
Y. Li	Procedia Computer Science	2022	Intelligent diagnosis, artificial intelligence network, automobile fault diagnosis, the neural network	China
M. Masoodifar	Journal of International Trade, Logistics and Law	2023	Artificial Intelligence, global business, communication, automation, customer service	Turkey
O. Vergara	Computation and Systems	2021	Artificial Intelligence, industry 4.0, Iberoamerican countries, robotics, augmented and virtual reality, IoT, big data	México
M. Javaid	Journal of industrial integration and management	2022	Artificial Intelligence, Industry 4.0, applications, advancements, features	India
Z. Jan	Expert Systems with Applications	2023	Artificial Intelligence, Industry 4.0, applied computing, information systems	Australia
S. Banitaan	International Journal of Advanced Computer Science and Applications	2023	Artificial Intelligence, Industry 4.0, intelligent manufacturing, industry analysis	USA
M. Javaid	Cognitive Robotics	2022	Collaborative robot, Cobots, robot, industry, manufacturing	India
Z.M. Bi	Robotics and Computer-Integrated Manufacturing	2021	Collaborative robots (Cobots), Human-machine interface (hmi), Human-robot collaboration (hrc), industrial robots, safety assurance and integrated systems	China
A. Borboni	Machines	2023	Collaborative robots, human-robots interaction, Cobots, artificial intelligence, machine learning, deep learning, reinforcement learning	Italy
Y. Li	Prog. Biomed. Eng	2022	Control, estimations, adaptations, interactive, robot, human, learning	United Kingdom
Z. Pilat	Journal of Automation, Mobile Robotics and Intelligence Systems	2019	Collaborative robots, robotization, industrial applications, safety, law, standards	Poland
D. Kóczi	Annals of Faculty Engineering	2022	Robot safety, human-robot collaboration, Cobot safety, Cobot review, standard	Hungary
J. Pizón	Advances in Science and Technology Research Journal	2022	Outsourcing, Cobots, lean management, VSM, Industry 5.0	Poland
J. Pizón	International Scientific-Technical Conference Manufacturing	2022	Manufacturing Systems, Digital twin, Cobots, artificial intelligence, virtual reality	Poland
A.G. Kravets	Springer	2021	Robotic, mathematical model, collaborative robotics, human-robot, industry 4.0	Russia
C. Taesi	Robotics	2023	Cobot, control, perception, applications, collaborative robotics	Italy
O. Salunkhe	International conference on advances in production management systems	2020	Collaborative application, gripper, framework, SII-Lab	Sweden
A. Pinto	Human behavior and emerging technologies, Hindawi	2022	Human-robot interaction, human computer, industry 4.0	Portugal
F. Chromjakova	Journal of competitiveness	2021	Process, ethics, workplace, productivity, human-machine cooperation	Czech Republic
Y. Cheng	IEEE Robotics and Automation Letters	2021	Industrial robots, human-centered robotics, assembly, task planning	USA
A. Calitz	SA Journal of Human Resource Management	2017	Industrial robots, human-centered robotics, assembly, task planning	South Africa
S. El-Zaatari	Journal of intelligent manufacturing	2021	Learning from demonstration, reinforcement learning, collaborative robots	United Kingdom
Y. Aydin	Transactions of the Institute of Measurement and Control	2021	Physical human-robot interaction, collaborative drilling, dynamic characterization, Cobot, admittance control, stability, transparency, augmented reality	Turkey
G. Lefranc	Procedia Computer Science	2022	Cobots, Industry 4.0, robotics	Chile
Markets and Markets	Market research report	2023	Collaborative robot market, market dynamics, collaborative robot ecosystem	United Kingdom
M. Shoaib	EAI Endorsed Transactions on Creative Technologies	2022	Artificial Intelligence, Cyber-attack, Deep learning, cybersecurity, intrusion detection, machine learning	China
M. Dawson	Sciendo	2021	Artificial intelligence, cybersecurity, workforce, risk management	USA
D. Quirumbay	CTU scientific and technological UPSE	2022	Deep learning, internet of things, artificial intelligence, neural networks, cyber security	Ecuador
S. Alneyadi	Sustainability	2023	Reading skills, creative written expression skills, digital learning	United Arab Emirates
R. Das	Journal of Physics: Conference Series	2021	Artificial Intelligence, Intelligence Agents, Neural networks, Smart Cyber Security methods	India
O. Vermesa	River Publisher	2021	Artificial intelligence (AI), Industrial internet of things (IIoT), Machine learning, Deep learning, Neural Networks, Machine vision, Smart robots, AI at the edge, Silicon-born AI Industrial sectors: automotive, semiconductor, industrial machinery, food and beverage, transportation	Norway
F. Tao	EAI Endorsed Transactions on Creative Technologies	2021	AI, Cybersecurity, ML, DL	China
S.A. Elnagdy	Proc. - 3rd IEEE Int. Conf. Cyber Secur. Cloud Comput. CSCloud 2016 2nd IEEE Int. Conf. Scalable Smart Cloud, SSC 2016	2016	Cyber security, risk taxonomy, cybersecurity insurance, financial industry, cloud computing	USA
J. Yang	IEEE Xplore	2017	SD Printing, additive manufacturing, artificial intelligence, machine learning, algorithm, printability checking, prefabrication, path planning, cloud service platform, attack detection, security, real-time control	United Kingdom
Y. Arjoune	IEEE Xplore	2020	5G wireless communication, deep learning, energy efficiency, channel coding, scheduling, cybersecurity	USA
I. Oleksiewicz	Przeglad Europejski	2022	Artificial intelligence, human, threat, European Union, development	Poland
A. Clim	Sensors	2022	Smart city, cybersecurity, industry 4.0, industry 5.0, Internet of Things, smart factory, smart mobility	Romania
A. Abood	IIETA	2022	Deep learning, electromechanical system, motor, predictive maintenance, deep learning	Iraq
T. Zonta	Computers & Industrial Engineering	2020	Industry 4.0, predictive maintenance, remaining useful life, conditional-based maintenance, Artificial intelligence	USA
M. Mulders	PWC & mainnovation	2017	Predict the unpredictable 4.0, maintenance 4.0, cloud, data collection	Netherland
M. Almobarek	buildings	2023	Predictive maintenance, Industry 4.0, Quality 4.0, decision tree algorithm, chilled water system, HVAC, commercial buildings, industrial engineering, engineering management	United Kingdom
V. Villa	Sustainability	2022	Internet of things (IoT), sustainable buildings maintenance, management, smart buildings, artificial intelligence	Italy
J. Mesarosova	International Scientific Journal about Logistics	2022	Predictive maintenance, Predictive Maintenance Maturity Matrix, Industry 4.0, Logistics 4.0, case study.	Slovak Republic
M. Pech	Sensors	2021	Intelligent sensors; maintenance; smart factory; Industry 4.0	Czech Republic
T. Carvalho	Computers & Industrial Engineering	2019	Predictive maintenance, Machine learning, PdM, Systematic literature review, Artificial intelligence	Brazil
M. Saman	IEEE Access	2023	Transfer learning, domain adaptation, fault diagnosis, fault detection, fault prognosis, predictive maintenance	Sweden
S. Niu	IEEE Transactions on Artificial Intelligence	2020	Transfer learning, machine learning, domain adaptation, distant domain transfer, cross-modality transfer learning	USA
M. Drakaki	Journal of Industrial Engineering and Management	2022	Predictive maintenance, induction motor, fault detection, fault diagnosis, machine learning, deep learning, Industry 4.0	Greece
T. Fonseca	Data in Brief	2023	Data Predictive maintenance Appliances Machine Learning	Portugal
A. Pedretti	Brazilian Archives of Biology and Technology	2021	Artificial Intelligence, Robotic Processes Automation, Machine Learning, Data Analytics, Power Distribution Utilities	Brazil
S. Chen	Hindawi- Wireless Communications and Mobile Computing	2023	Artificial Intelligence, Big Data, Machine Learning, Optimization Algorithms, Internet of Things	Malaysia
N. Subramayan	Organization & Management Quarterly	2018	Artificial Intelligence, Automation, Benefits and Challenges of Artificial Intelligence and Automation, Interface between Human and Artificial Intelligence	India
S. Kumar	AIP Conference Proceeding	2022	Auditory system, Energy consumption, Household appliances, Thermal instruments, Information technology, DC motors, Artificial intelligence	India
Y. Meduri	Delhi Business Review	2021	Automation, HR Technology, Workplace, Culture, Human connect, HRM	India
U. Raissa	IEEE Xplore	2019	Robotic Process Automation (RPA), administrative business process, business process	Kazajistán
D. Da Silva	Sciendo	2022	RPA, robotic process automation, software robot, digital worker, adoption, implementation	Portugal
BR. Deepika	International Journal of Design	2022	Automation, Network performance, Trust, Telecommunication	India
W. Qilin	Scientific Programming-Hindawi	2022	Automation design, manufacturing, internet of things	China
J. Siderska	Sciendo	2020	Robotic Process Automation, RPA, digital transformation, software robots, digital technologies, artificial intelligence	Poland
E. Suescún	Revista Facultad de Ingeniería	2021	DevOps Industrial, DevOps and Industry 4.0, Systematic Mapping, Agile Development, Industrial software	Colombia
R. Ayadi	Hindawi-Wireless Communications and Mobile Computing	2022	Deep learning, automation of industry, sensors	Saudi Arabia
L. Liu	Hindawi-Mobile Information Systems	2022	Big Data, Artificial Intelligence, Decision-Making, Technology	China
T. Sourdin	UNSW	2018	Artificial Intelligence, decision-making, technologies	Australia
M. Eslami	CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems	2018	Advertising Algorithms, Algorithmic Transparency, Process Communication, Ad Explanation, Algorithmic Authority	USA
D. Winster	E3S Web of Conference	2023	Artificial Intelligence, Data Collection and Preprocessing, Transparent Decision Making	India
L. Xia	Hindawi- Wireless Communications and Mobile Computing	2022	Wireless communication, Artificial Intelligence, Decision making	China
S. Neizvestny	Sociological Journal	2021	Artificial intelligence, social relationships, decision-making systems, digital society, security, cyber management.	Russia
H. Guan	Behavioral Sciences	2021	Artificial intelligence decision making; ethical risk; risk factors; mechanism of action	China
J. Baker	AI and Ethics	2021	Artificial intelligence, Ethics, Management, Social impact, Humanity	Austria
X. Kang	Hindawi-Wireless Communications and Mobile Computing	2022	Big Data, Artificial Intelligence, Decision Making	Thailand
Y. Saleh	Hindawi-Journal of Environmental and Public Health	2022	Artificial Intelligence, Decision-Making Processes	Iraq
Y. Peng	Hindawi- Mathematical Problems in Engineering	2022	Artificial Intelligence, Internet of Things, Decision Making	Thailand
A. Alloulbi	Hindawi-Mathematical Problems Engineering	2022	Artificial Intelligence, Decision Making, Smart Cities, Internet of Things	Turkey
M. Mirbabaie	Health and Technology	2021	Artificial Intelligence, AI, Diagnostic, Healthcare, Digital Health, Critical Review	Germany
M. Waqas	KASBIT-Business Journal	2021	Artificial Intelligence, Machine learning, Marketing Strategies, Business to consumer firms	Pakistan
B. Sabin	University of Târgu Jiu, Economy Series	2023	Artificial intelligence, modalities of artificial intelligence integration, market, sales	Romania
J. Faritha	Hindawi- Computational Intelligence and Neuroscience	2022	Artificial Intelligence, Business Markets, Prediction model	India
M. Abu	Management and Economics Review	2023	Artificial intelligence (AI); Ports community; Critical success factors	India
J. Zhang	Journal of Business Models	2022	Ecosystem Legitimacy, Business Model, Digitalization	Finland
J. Dehler	KIT-Karlsruhe Institute of Technology	2021	Technology legitimacy, wind power Germany, structural topic model, natural language processing, text mining	Germany
N. Farooqi	Intelligence Automation & Soft Computing	2021	Business intelligence, business analytics, artificial intelligence implementation, universal business intelligence model	Saudi Arabia
C. Dirican	Procedia Social and Behavioral Science	2015	Robots, Robotics, Artificial Intelligence, Hologram, Mechatronics, Business, Economics	Turkey
H. Li	Hindawi-Mobile Information Systems	2022	Big Data, Artificial Intelligence, Business Management	Malaysia
J. Hórak	SHS Web of Conferences	2023	Artificial intelligence, robotics, business opportunities, manufacturing company, finance	Czech Republic
A. Nicolau	Revista Romana de Economie	2021	artificial intelligence, decision-making process, management	Romania
N. Krishman	Hindawi-Complexity	2022	Artificial Intelligence, Business culture, Decision-Making process	India

Likewise, a qualitative analysis of the collected data was carried out, to identify the frequency of keywords relevant to the research topic. This analysis was performed using the word cloud generation technique using MAXQDA software (see Figure 8). Then, the five terms most related to the topic of study were examined to determine the total number of documents and citations associated with each one (see Table 3). Similarly, an analysis of the number of articles relevant to the research topic was conducted, covering the period between 2015 and 2023 (see Figure 9).

Figure 8. Frequency of keywords in the scientific literature.

Figure 9. Number of publications on artificial intelligence for the benefit of companies.

Table 3. Number of articles and citations per year.

Words	Cybersecurity	Cobots	Intelligence Artificial	Robots	Maintenance	Total
Total Documents	16	27	127	42	31	243
Total mentions	426	1268	3033	2249	1101	8077

Finally, an analysis was carried out that presents the total number of publications per year and their respective number of citations (see Table 4). In addition, a detailed analysis of the different articles was carried out to identify the 10 documents with the highest number of citations, including the year and journal of publication. This analysis yielded results on the degree of relevance of innovative topics related to the main research.

Table 4. Most cited scientific articles and journals where they were published.

Year	Journal	Article name	Number of citations
2015	Proc. Soc. Beha. Scie	The impacts of robotics, artificial intelligence on business and economics	518
2018	Proce. Ch. Confe. Hum. Fact. Compu. Syst	Communicating algorithmic process in online behavioral advertising	186
2019	Comp. Indus. Engi	A systematic literature review of machine learning methods applied to predictive maintenance	1020
2020	Comp. Indus. Engi	Predictive maintenance in the industry 4.0: A systematic literature review	631
2020	Enge. Manag. Produc. Serv	Robotic process automation-a driver of digital transformation?	175
2021	Sens	Predictive maintenance and intelligent sensors in smart factory: review	227
2021	Robot. Compu. Integr. Manuf	Safety assurance mechanism of collaborative robotic systems in manufacturing	178
2021	Hel. Tech	Artificial intelligence in disease diagnostics: A critical review and classification in current state of research guiding future direction	137
2022	Ind. Inte. Mana	Artificial intelligence applications for industry 4.0: A literature-based study	174
2023	Exp. Syst. Appl	Artificial intelligence for industry 4.0: Systematic review of applications, challenges, and opportunities	99

4. Results

4.1. Review and analysis of information

To carry out the qualitative analysis, the MAXQDA software, version 2020, was used. Its word cloud generation functionality made it possible to produce a visual representation that organizes words from text and ranks the most frequent terms. Figure 8 shows the 40 most common keywords identified in the scientific literature manipulated in this study.

An analysis was also carried out with MAXQDA, which shows the words most related to artificial intelligence, the number of documents and the total number of times they are mentioned (see Table 3). The highest word is artificial intelligence with 52% of the documents and a mention of 37% and the lowest is cybersecurity with only 6% of documents and a mention of 5% in total.

Publications related to the importance of artificial intelligence as a beneficial factor for companies have seen an annual intervention, with a total of 91 products related to this topic, as indicated below. 56% of all research is concentrated in the years 2022 and 2023, suggesting that this is an innovative field in the field of engineering and that it is being incorporated as an integral part of 4.0 technologies in organizations (see Figure 9).

Below is the number of articles and their respective citations according to the year of publication. Citation results were obtained using the Google Scholar search engine (see Table 5). Between 2021 and 2022, a total of 55 articles were identified out of a set of 90, representing 61% of the updated literature related to Artificial Intelligence. On the other hand, in 2019 a significant number of citations were recorded, reaching a total of 1092 related to work on Cobots, decision-making and artificial intelligence in organizations. In 2020, there were 926 citations related to robots, cybersecurity, and real-time forecasting.

Table 5. Number of articles and citations per year.

Year	Journals	Number of citations
2015	1	518
2016	1	43
2017	3	230
2018	3	203
2019	3	1092
2020	8	926
2021	21	723
2022	34	499
2023	16	198
Total	90	4432

Finally, among the 90 documents analyzed, the 10 most cited articles were identified, considering the year of publication, the name of the journal where they were published, the title of the article, and the total number of citations received (see Table 4). In 2019, 1020 citations related to artificial intelligence and its applications in machine learning, predictive maintenance, industrial process management, robotics, and robotic collaboration (Cobots) were recorded. The year 2015 stood out with 518 citations on topics related to the impact of robotics and artificial intelligence on the economy and business. It is noteworthy to note that, since 2023, research related to Industry 4.0 and its application in artificial intelligence, the impact of digital environments, technological advances in collaborative robots, predictive maintenance 4.0, artificial intelligence in business, and artificial intelligence and marketing in the automotive industry are being published.

5. Discussion

Based on the literature review, close collaboration between humans and robots is now a reality thanks to recent technological advancements. For the past two decades, there has been a clear separation between the human workplace and the domain of robots. However, in the next five years, this dynamic will change radically. Robots will be able to integrate into our everyday environments, whether in our homes, workplaces or industries, ensuring safety at all times.

A new generation of robots, equipped with distributed sensors, has emerged in the last five years. These robots can operate independently thanks to their sensitive joints. For example, if a person approaches and touches the robot, it will automatically stop its movement when it recognizes the human presence in its proximity. Thanks to these improvements, the collision rate has been drastically reduced, while operational efficiency has increased thanks to continuous learning (Lv et al., 2022). Cobots represent an important step forward in automation for various industries, including small and medium-sized businesses, thanks to their smaller size, cost, and versatility (Salunkhe et al., 2020; Weckenborg & Spengler, 2019).

The cyber threat landscape has become increasingly complex, outpacing traditional security measures. To cope with this evolution, it is crucial to implement a robust cybersecurity intelligence framework. This framework would provide organizations with real-time information on emerging threats, allowing them to protect their critical assets effectively. Including identifying vulnerabilities in infrastructure, detecting suspicious behavior, and collaborating on the disclosure of potential threats with stakeholders, this comprehensive approach ensures a proactive defense against ever-evolving cyber threats (Saeed et al., 2023).

In the context of artificial intelligence, one of the common challenges is the scarcity of labeled data on failures in industrial manufacturing processes. To address this challenge, an unsupervised learning approach is required, particularly the use of algorithms such as K-means clustering. This approach makes it possible to identify anomalies accurately without relying on labels, as collecting all fault data in a large-scale factory is impractical. However, it is important to recognize that incorrect alerts or missed interventions may arise if expert knowledge is not considered. Therefore, visual analysis plays a crucial role in the integration of knowledge-based techniques in unsupervised processing environments, thus contributing to improving the labeling process more effectively (Cheng et al., 2022; Steenwinckel et al., 2021).

The introduction of artificial intelligence (AI) in project management represents a significant breakthrough that can boost performance in crucial areas such as risk management, resource allocation, and decision support. So far, the application of AI in project management has been broad, ranging from automating tasks to data analysis, creating predictive models, and supporting informed decisions. Despite these advances, effectively combining AI techniques with agile methodologies to drive agile and intelligent project management remains an uncharted area. The integration of AI algorithms into agile environments promises to improve both decision-making and collaboration within the project team (Zhang et al., 2024).

6. Conclusions

Collaboration between robots and humans, coupled with modern technologies, offers the opportunity to make workplaces safer and more productive for operators. Deploying Cobots involves several tasks and costs, leading to high-risk decisions for long-term investments, but with the potential to adapt to a new era of automation and continuous improvement. Although Cobots and new technologies have considerably increased the productivity of organizations, trust is still a key issue in the interaction between worker and machine, being essential to manage production safely and efficiently.

Cybersecurity is presented as an essential safeguard to protect electronic information systems, networks, and data. The alarming rise in cyberattacks, exacerbated by the entry of Industry 4.0 and the internet, has created a constant challenge for security experts. However, the strategic use of neural networks, intelligent agents, and AI-based systems have proven effective in countering these threats. Implementing machine learning algorithms, powered by AI, helps organizations detect and neutralize threats while maintaining the reliability of information. The rapid evolution of wireless communication technology, coupled with artificial intelligence and machine learning, has made it possible to detect anomalies in systems and provide solutions efficiently.

Artificial intelligence in predictive maintenance (PM) emerges as a crucial element in the smart manufacturing of the new industrial revolution 4.0. Learning methods such as Deep Learning (DL) are replacing conventional methods with modern diagnostics. Predictive maintenance becomes a fundamental part of smart manufacturing by reducing line downtime, maintenance costs and increasing the safety and life of equipment, generating considerable benefits for companies. The implementation of Industry 4.0 highlights the importance of predictive maintenance over corrective maintenance, being a key strategy for building comprehensive systems.

Transparency in AI decision-making is essential to ensure that systems provide objective information about their operation, which influences workers’ perception. Although AI has become a powerful tool for completing complicated tasks efficiently, and wireless communication allows for better management of physical spaces, its implementation poses ethical challenges, such as algorithmic discrimination and information biases. However, governance strategies from various perspectives are emerging to address these risks. The combination of AI with technologies such as IoT is transforming organizational processes and broadening the landscape for more effective decision-making, although it requires an ethical and transparent approach to maximize its benefits and minimize its risks.

Finally, we can say that the implementation of artificial intelligence (AI) in companies has proven to be a transformative factor in the way they operate and make decisions. As AI technologies continue to advance, their impact on businesses has become more significant and diverse. Some key points about AI in business include:

(1) AI has enabled the automation of repetitive and routine tasks, increasing operational efficiency and allowing employees to focus on higher value-added tasks; (2) AI provides advanced analytical skills, such as predictive analytics and real-time processing of large volumes of information, helping businesses make more informed and evidence-backed decisions; (3) AI enables organizations to personalize their products and services according to individual customer preferences, improves their customer experience, and fosters loyalty, (4) AI contributes to optimizing supply chain management by anticipating demand, identifying patterns in inventory, and refining logistics; (5) AI can streamline the research and development process by analyzing scientific and technical data, simplifying the execution of new products and services; (6) AI has the ability to analyze customer sentiment and sentiment on social media and online comments, offering valuable insights into brand perception and helping to adjust marketing strategies; (7) The incorporation of AI also poses ethical and regulatory challenges, such as data privacy, the possibility of algorithmic biases, and liability in case of erroneous automated decisions.

Artificial intelligence has great potential to drive efficiency, innovation, and competitiveness in companies. However, its implementation needs to be approached with care, considering both its benefits and the associated challenges. Collaboration between AI experts and business professionals, along with an ethical and responsible approach, is essential to get the most out of this technology in the business environment.

Author contributions

“Conceptualization, M.D. (Marco Díaz) and R.R. (Reina Román); methodology, S.R. (Santos Ruíz) and H.S. (Herson Santos); validation, M.M. (Mario Morales) and G.C. (Gabriela Cervantes); formal analysis, S.R. (Santos Ruíz) and H.S. (Herson Santos); investigation, M.D. (Marco Díaz) and R.R (Reina Román); resources, M.D. (Marco Díaz) and R.R. (Reina Roman); data curation, M.M. (Mario Morales) and G.C. (Gabriela Cervantes); writing—original draft preparation, M.D. (Marco Díaz) and R.R (Reina Román); writing—review and editing, R.R. (Reina Román); visualization, G.C. (Gabriela Cervantes); supervision, H.S. (Herson Santos); project administration, M.D. (Marco Díaz) and R.R. (Reina Román). All authors have read and agreed to the published version of the manuscript.” All authors have read and agreed to the published version of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The dataset generated and/or analyzed during the current study are not publicly available but are available through the corresponding author upon reasonable request.

Additional information

Funding

This research received no external funding.

Notes on contributors

Marco Antonio Díaz Martínez

Marco Antonio Díaz Martínez Experto en la investigación sobre tecnologías 4.0 aplicadas en las organizaciones industriales.

Reina Verónica Román Salinas

Reina Verónica Román Salinas Experto en la investigación de cadenas de suministro y sus aplicaciones en la ingeniería industrial.

Santos Ruíz Hernández

Santos Ruíz Hernández Experto en la investigación sobre la planeación estratégica y productividad en las empresas.

Herson Santos Ruíz Domínguez

Herson Santos Ruíz Experto en la investigación sobre la planeación estratégica y productividad en las empresas.

Gabriela Cervantes Zubirías

Gabriela Cervantes Zubirías Experto en la investigación sobre la administración de operaciones y desarrollo organizacional en las empresas.

Mario Alberto Morales Rodríguez

Mario Alberto Morales Rodríguez Experto en la investigación sobre la administración de operaciones y desarrollo organizacional en las empresas.