1.1 Introduction

The concept behind the Internet of Things (IoT) is to connect several devices, through the internet which can exchange information with each other. Considering that this technology can be applied to industry, it makes this connection between these different devices generates Industry 4.0, which is reputable as the Fourth Industrial Revolution, being the new trend that is being adopted by large corporations to get ahead in the market, characterized by the introduction of information technology in the industry [1].

IoT in Industry 4.0 is basically responsible for the integration of all devices inside and outside the plant, considering that the concept represents the connection as it is a network of physical devices (objects), systems, platforms, and applications with embedded technology to communicate, feel or interact with indoor and outdoor environments [1, 2].

Industry 4.0 is the complete transformation of the entire scope of industrial production through the fusion of internet and digital technology with traditional industry, being motivated by three major changes in the productive industrial world related to the immense amount of digitized information, exponential advancement of computer capacity, and innovation strategies (people, research, and technology) [2, 3].

When it is said that the internet is in the industry, these changes allow everything inside and around an operational plant (suppliers, distributors, plants, and even the final product) to be digitally related and connected, affording a highly incorporated value chain, from the factory floor, is important to relate this to an environment where all equipment and machines are connected in networks and uniquely providing information [3, 4].

For Industry 4.0 to become feasible, it requires the adoption of a technological infrastructure made up of physical and virtual systems, aiming to create a favorable environment for new technologies to be disseminated and incorporated by the industry, with the support of Big Data Analytics technology (Figure 1.1), automated robots, simulations, advanced manufacturing, augmented reality, and the IoT, employing the monitoring of technological trends, assisting managers throughout the entire industrial chain [3, 5].

The Industrial Internet of Things has an IoT and IIoT layer in the industry, provoking a prognostic model, since automation, which in general already exists, answers questions regarding what is happening, what happened, and why it happened, considering its network of physical devices (objects and things, among others), systems, platforms, systems, and applications with embedded technology in industry sectors, aiming to promote automation of manufacturing and, thus, increase the productivity of production lines, generating greater competitiveness with the international industry through intelligent factories (smart manufacturing) [6].

Generating an increasing number of connected devices (in some situations, it even include unfinished products), since the digitization of data from machines, methods, processes, procedures, and intelligent devices, integrates and complements the operational layer of an industrial plant, enabling communication and systems integration and controls and allowing responses and decision-making in real time. Thus, IIoT becomes a prerequisite for Industry 4.0 [1, 7].

The difference between IoT and IIoT is in the sense that the first relates systems that connect things, complement information, normally only produce data, and can be used in any sector of the industry, transforming the second, to manage assets and analyze maintenance trends [8–10].

IIoT forms a critical layer of the production process and can directly connect a product supplier in real time on the production line, which analyzes the quality and use of your product, as well as connecting the input and output logistics chain of materials and control production, in real time, at the optimum point of operation, becoming an application of production and consumption of data, with a critical profile [8–10].

The use of IoT and IIoT proposes the digital factory bringing benefits to productive plants as an improvement in the use of the asset, reduction of operations or asset cycle cost, improved production, reduction of operations or stoppages, improving asset use (performance), increased speed in decision-making, allow the sale or purchase of products as a service, generate opportunity for new business, among several others. Thus, the premise of digitizing all information can lead to a question about the reason and reason for digitizing so much data, since this information is all digitized and there are all the means (networks) for them to travel and exchange information with each other, it is expected that decisions can be made not only between operators and machines, but also between machine and machine, this is called M2M, Machine to Machine, which before were not available in real time and are now needed [8–11].

Thus, the architectures of industrial automation systems, which have adherence to Industry 4.0, manage to integrate different devices in favor of industrial evolution, with more and more sensors, cameras, and systems that will be monitoring the entire industrial production process, evaluating and supervising the performance of equipment, and providing, in addition to the already known layers of operational control and the entire control framework, the IoT and IIoT layer, where it will converge all this data into a Big Data, delivering operational control possibilities (Figure 1.2), with decision-making in prognoses and with the possibility of autonomous actions [10–12].

Optimizing the production process of the industry is the main reason for the application of IoT in the production line of the factories, since the IoT technology and its IIoT aspect allows the equipment that makes up the industrial yard of a company today that can be connected in a network. With the data collected and stored in the cloud, it allows the decision-makers of the companies to have quick and easy access to all the information of the company and its collaborators; in other words, this makes all the industrial machinery work automatically through of highly programmable intelligent sensors [13, 14].

Wherefore, this chapter is motivated and has the purpose to originate an updated overview of IoT and IIoT, addressing its evolution and branch of application potential in the industry, approaching its relationship with current technologies and synthesizing the potential of technology with a concise bibliographic background.

1.2 Relationship Between Artificial Intelligence and IoT

The emergence of solutions and tools with AI (Artificial Intelligence) technology means solutions, tools, and software that have integrated resources that automate the process of making algorithmic decisions. The technology to be used can be anything from independent databases employing Machine Learning to pre-built models that can be employed to a diversity of data sets to solve paradigms related to image recognition and text analysis. Applied in the industry, it can help a business achieve a faster time to evaluate, reduce costs, increase productivity, and improve the relationship with stakeholders and customers [15, 16].

Machine Learning is only part of AI, that is, it is an AI application in which it accesses a large volume of data and learns from it automatically, without human intervention. This is what happens in the case of recommendations on video streaming platforms and facial recognition in photos on social media pages. AI is a broader concept that, in addition to Machine Learning, includes technologies such as natural language processing, neural networks, inference algorithms, and deep learning, in order to achieve reasoning and performance similar to that of human beings [15, 16].

An AI system is not only sufficient and capable of storing, analyze, and manipulating data, but also of acquiring, representing, and manipulating information and knowledge. Including the characteristic to infer or even deduce new knowledge, new relationships between data-generating information about facts and concepts, from existing information and knowledge and to use methods and procedures of representation, statistical analysis, and manipulation to solve complex questions that are often incognito and non-quantitative in nature [17].

The increase in mass data collection over the years, related to IoT devices, has boosted AI, given that the volume of information produced by people has been growing exponentially. But allied with Big Data technology to understand this massive set of data, which serves as a basis for learning the most diverse software, such as Machine Learning. This data revolution favored the AI scenario, i.e., with more information available, more intelligent, and automated ways to process, analyze, and use the data [18, 19].

Big data is the term employed to refer to the enormous amount of data that is produced and stored daily, evaluating that from this abundance of information, there are intelligent systems created to organize, analyze, and interpret (that is, process) the data, which are generated by multiple sources [19, 20], still pondering on predictive analysis as the ability to identify the probability of future results based on data, statistical algorithms, and machine learning techniques. From Big Data, it is possible to do this type of analysis, identifying trends, predicting behaviors, and helping to better understand current and future needs and, finally, to qualify decision-making in machines, equipment, and software, taking technology to a new level. AI is impacting society with machine learning systems, neural networks, voice recognition, predictive analysis, and natural language processing (NLP) and continuously remodeling new aspects of human life [19, 20].

Forecasting and adaptation are possible through algorithms that discover programmed data patterns, the solutions learn and apply their knowledge for future predictions. If a sequence of bits exists, then the AI recognizes the sequence and predicts its continuity. This is also able to correct spelling errors or predict what a user will type or even estimate time and traffic on certain routes in transit (autonomous vehicles based on AI) [17].

Decision-making through data analysis, learning, and obtaining new insights is able to predict or conjecture a more detailed and faster decision than a human being. But it helps to increase human intelligence and people’s productivity. Through continuous learning, AI can be considered a machine capable of learning from standards [21].

Also related to its characteristics in the ability to build analytical models from algorithms, learning to perform tasks through countless rounds of trial and error. In the same sense, NLP provides machines and computing devices the capability to “read” and even “understand” human language [22].

1.2.1 AI Concept

Another characteristic of the basic types of AI is purely reactive, since it acts after the perception of the problem, exemplifying an AI software that identifies the chess pieces on the board and their movement, but has no memory of past movements, ignoring everything before the current movement, that is, it only reacts to the position of the pieces on the board. In the legal field, lawyers focus on more complex aspects of law practice, given the use of text analysis, Jurimetrics, text review, data mining, contract analysis, computational argumentation, and other possible AI-derived features [17, 23], still pondering the characteristics of AI-related to its capacity for intelligent perception, such as visual perception, speech perception, auditory perception, and processing and learning of perceptual information. Reflecting on autonomous cars and virtual assistants, there is not only a programmed answer to specific questions but answers that are more personalized [23–25].

Through AI solutions, it is possible to eliminate boring tasks that may be necessary, but with machine learning, it performs basic tasks, considered human-computer interaction technologies, or even related to the more robust use found in conversational interfaces that use machine learning to understand and meet customer needs [23–25].

Even through AI solutions, it is possible to concentrate diffuse problems where data inform all levels of the operation of a modern company, i.e., it has a lot of material to interpret, so it is necessary to consume this amount of information at scale. Since the extent of the data available today has gone beyond what humans are capable of synthesizing, making it a perfect job for machine learning. Through the data, the information is extracted from various sources of public and private data, still comparing them and making changes when necessary [25].

Through AI solutions, it is possible to distribute data, given that modern cybersecurity leads to the need to compare terabytes of internal data with a quantity of external data. With machine learning, it can automate the process of detecting attacks as cybersecurity problems change and increase, vital for dealing with distributed data problems, assessing that humans are unable to involve their actions around a distribution so wide of information. AI solves dynamic data, which is a valued characteristic, given the major obstacle related to addressing individual employee characteristics, or dynamic problems of human behavior. Through AI, it is possible to use determining complex patterns to help organizations move more quickly and respond better to the changing needs of each employee [26].

Or even, through AI, industrial systems integrate robotics powered by AI, 3D printing technologies, and human supervision, building interactive robot systems leading by AI technologies. This process not only decreases costs and increases efficiency but also generates much safer industrial environments for human workers. The dangerous elements of industrial activities are surpassed by machines [27, 28].

In simpler terms, AI technologies consist of intelligent systems or intelligent machines that mimic human intelligence to operate tasks and can improve iteratively supported on the information it collects. AI technologies manifest itself in various ways in modern contemporary society as chatbots to understand customer issues more quickly and provide more efficient responses or smart assistants to analyze critical data and information from large sets of free-text data to improve programming, or even at home, through recommendation mechanisms providing intuitive recommendations for TV programs supported on users’ viewing habits. However, AI technologies are not deliberate to replace human beings but aims to substantially improve human skills and actions, tasks, and even contributions [17, 23, 24].

AI is related to application areas that involve expert systems or systems based on knowledge, natural language comprehension/translation, intelligent systems/learning, speech comprehension/generation, automatic programming, or even image and scene analysis in real time, among many others. Therefore, it can be evaluated that the technological AI field aims to emulate human beings’ capabilities including problem-solving, understanding natural language, computer vision, and robotics, considering systems for knowledge acquisition, and even knowledge representation methodologies [15].

To obtain the full value of AI, Data Science is necessary (Figure 1.3), consisting of a multidisciplinary field that employs scientific methods to collect and extract value from data, combining skills such as statistics, probabilities, frequency of occurrence of events, observational studies, and computer science, with business knowledge to analyze data gathered from distinct sources [29, 30].

The central principle of AI technologies is to replicate, and then exceed, the processes and conduct humans perceive, notice, see, and react to the world, fueled by several forms of Machine Learning techniques that recognize patterns in data to allow prognosis and predictions. Propitiate a better comprehensive understanding of the wealth of available data, information, and predictions to automate overly complex or ordinary tasks, improving productivity and performance, automating tasks or processes that previously demand human energy, and also making sense of the data on a superhuman scale [31].

Data science makes it a priority to add technological value to business intelligence and advanced analysis as the main technology differential for companies, through the use of demographic and transactional data to foresee and predict how much certain customers and users will spend over their business relationship with a company (or even the customer’s lifetime value), price optimization supported on preferences and customer behavior, or even utilizing image recognition techniques to analyze X-ray digital images searching for signs of cancer [30].

Three elements are leading the development of AI technologies across all sectors, which are the computational high-performance, affordable, and even processing capacity available, assessing the abundance of computing power in the cloud technologies allowing easy access to affordable and high-performance computing power. Large volumes of data available for conduct training, given that AI, require to be trained on a lot of data available to generate the correct predictions, also relating the emergence of distinct tools for labeling data, in addition to the ease and accessibility of storing and processing structured and unstructured data, to train AI algorithms [31].

The benefits of operationalizing AI are related to the cognitive interactions of machine learning techniques with conventional business applications, methods, and processes that can greatly increase productivity and user experience, or even considering AI as a strategic method and competitive advantage related to greater efficiency in processes, doing more in less time, and increasing customer loyalty, creating customized and attractive customer (user) experiences, and predicting commercial results to generate greater profitability [23, 24, 32].

AI applications in people’s daily lives are based on an app that recognizes the content of images and allows a search by typing the name of an object or action, or streaming platforms transcribing audio and generating subtitles for videos, or in an email offering automatic responses smart; or even with regard to online translators who translate texts from signs, labels, and menus with the cell phone camera; or even pondering about streaming platforms that use AI to understand users’ preferences and recommend music and movies, respectively, still relating autonomous cars that drive alone, or even in medicine, advancing cancer studies [26].

The application of AI is present in various segments of the economy; in industry, automation is a keynote for machines that keep getting smarter. With AI, the equipment manufactures and checks the products without having to be operated by a human, that is, it performs repetitive work and has no limitations for their use. Through the GPS (Global Positioning System), the routes suggested by online applications, generally, point out the best path, considering that the AI interprets data provided automatically by other users about the traffic on the roads. Online retailers, using online store algorithms, recognize user purchasing patterns to present offers according to their preferences. Financial institutions use AI algorithms to analyze market data, manage finances, and relate to their customers [33].

Thus, the first industrial revolutions created equipment that replaced manual labor, carrying out the work of many men with greater efficiency and less cost. Currently, in several cases, through the AI employee in tasks, they have been previously seen as “intellectuals”. In any case, the important thing is that AI theater is a reality. In this regard, the understanding of its mechanisms and the understanding of the possibilities that this provides must be expanded. The concept of AI refers to the creation of machines, not necessarily with physical bodies (software that can abstract, create, deduce, and learn ideas), with the ability to think and act like human beings and aim to facilitate everyday tasks [7, 34].

1.2.2 IoT Concept

IoT in the early days corresponded to the connection via the internet in physical objects, such as a toaster, especially sensors. Over the years, the concept of connecting the physical material world with the virtual world has evolved into a technological revolution in order to connect all the objects that people use on a daily basis to the internet (Figure 1.4), describing a scenario in which several things are connected and communicate, through technologies like Wi-Fi. The result is a smarter and more responsive planet [35, 36].

Thus, IoT currently matches a series of hardware that works connected to the internet, from a smart TV to a running watch that measures heart rate and sends this data to an application. However, it is possible to interpret what part of these devices uses, even on a small scale, AI. This technological innovation connects everyday items (smart devices), or smart sensors, to the internet, making the physical world increasingly closer to the digital. Thus, the technology describes the physical objects (things) connected and communicating (transmitting) with each other and with the user, transmitting data (information) to a network, as if it were a broad digital nervous system, i.e., a structure that allows the exchange of information (data) between two or more points [17, 18, 37].

Still pondering that every day, more appliances, watches, means of transport, and accessories are connected to the Internet and other devices, such as smartphones, tablets, and mobile devices that transmit signals and appear to each other. Still pondering that through a connected network, these devices can be connected via the internet with cars, refrigerators, microwaves, trains, airplanes, among other thousands of artifacts (Figure 1.5) [18].

The field of IoT practices has been diversified over time, and currently, the field of applicability and use of IoT is very broad, reflecting on numerous technological resources that have been used to provide connection of devices. Like Bluetooth technology, communication by proximity field (short-range wireless technology, which allows the exchange of information between devices with enabled and compatible NFC) is also a feature used in IoT. Making the devices “talk digitally” to each other, generating more productivity, comfort, information, knowledge, and practicality in general, and their uses and application can include health monitoring or leading real-time information about city traffic, or yet the number of parking lots available in parking, even indicating activities, reminders, or even content on their connected intelligent devices [38].

Nowadays, everyday “things” become intelligent and have their functions and role expanded by crossing data (information), seeing a virtual assistant crossing data from connected intelligent devices to inform, even if not requested, the time (travel duration) it will take to get to work when leaving the house, also relating the interconnectivity of smart IoT devices around the environment and making a digital assistant learn a user’s routine, their times, their location via GPS connection, the connection (link) to the car’s Bluetooth at a singular time (Figure 1.6), and the circumstance that this context has been repeated many times [18].

The IoT exchanges information is essentially derived from three elements that require to be associated with an application to work which are the intelligent devices, the network (structure), and a digital control system. The intelligent devices are all those imaginable equipped with sensors and antennas, among others, providing communication with the other elements such as lamps, bedside lamps, refrigerators, microwaves, cars, coffee makers, and watches, television, among others (Figure 1.7). The network is the means of communication such as Wi-Fi, Bluetooth, mobile data, and fiber optics, among others. The control system causes all data (information) captured from the devices (things) to be processed and then sent (transmitted) to a digital system that controls each aspect analyzed and evaluated [36, 39].

Big Data is the driving technology of IoT, related to data are currently the great creators and destroyers of business value. Since the IoT devices connected to the network are constantly sending, receiving, exchanging, and crossing data, i.e., constantly producing data. As a result, the accumulation, analysis, and use of Big Data are more significant, especially for companies, which have the most expressive production of data with IoT, as it has a large number of objects that can be connected or already connected. In addition, with data and information in hand, companies make fewer mistakes, produce more, and win more customers. To make sense (means of storing, tracking, analyzing, and making use of this large amount) of all this data (information), Big Data analysis has a fundamental role, which is critical for companies of all sizes [19, 40].

Still pondering the seven main attributes that define and differentiate a normal object or device from an item that is part of the great mass of IoT connectivity, these devices and systems include sensors that track and measure activity worldwide. Internet connectivity will be in the item itself (thing/device), probably collecting information over time through sensors, exchanging messages, and files with a Cloud platform. Like any computer, the devices will have some built-in processing power, even if only to analyze and transmit data. Although many of the IoT devices are not yet equipped with special features to become really powerful in processing [41–43].

Efficient energy consumption is related to these devices being able to operate for a certain time or more on their own, using stored energy or staying connected only while used. Cost x benefit ratio is linked to the premise that several objects with sensors (must be relatively inexpensive to purchase and implant) distributed on a large scale to be really efficient, as in the case of food products in supermarkets that must have an indication of validity. Quality and reliability are related that many of the devices must operate exposed to harsh climates for long periods of time [41–43].

Security is given that IoT machines and devices transmit private and detailed information, such as that related to the user’s health, still reflecting that the change from previously inert objects to a reality based on connectivity transforms businesses, products, and workflows to suit consumer trends and needs. In this respect, blockchain technology can promote more digital security (Figure 1.8), so that objects connected to networks are not hacked [41–43].

However, the main potential of IoT is to carry out communication between objects, and people are given the practical nature, via the internet, “things” exchange signals with each other, i.e., mobile and fixed objects gain autonomy to interact with each other and with users. One of the greatest examples of this digital transformation in recent years is the increased use of IoT in homes and work relationships. Another technology that enhances the growth of IoT is AI, guaranteeing more autonomy and learning for objects connected to the internet [44].

1.3 IoT Ecosystem

IoT is basically things, i.e., it is all types of equipment/device/sensor that can be connected in different ways, from a truck to monitor the displacement of product transport fleets, use of sensors in tractors that measure the soil situation and send data to systems responsible for processing this information, and make suggestions for the best areas or times for planting, a boiler temperature sensor in a factory, or the adoption of devices at home, such as thermometers, energy consumption regulators, or home appliance managers, who allow the householder to control this equipment remotely, or even microsensors that monitor the status of patients remotely in hospitals or outside them [45].

In IoT, it is consistent with an environment whose rules deal with both connection and intelligent data collection and processing, since applications allow the coordinated and intelligent use of devices to control various activities, from monitoring with cameras and sensors to managing spaces and of productive processes. The IoT ecosystem is a system composed of a digital space of interaction including digital tools related to data analysis and modeling, as well as digital elements that integrate and interact within it. It is through these interactions and the exchange of information that AI allows these elements to work in an integrated manner, composing an intelligence potential far superior to what each of its elements has separately. The IoT ecosystem involves different agents and processes, such as smart objects [sensors, appliances, cars (Figure 1.9), and factory automation equipment], smart modules (processors and memories), connectivity services (access to the internet or private networks that connect these devices), integrators (systems that combine applications, processes, and devices), enablers (control systems, collection, and processing of data and commands involving objects), and even providers of IoT services [45, 46].

Within an IoT ecosystem, applications that integrate IoT technologies with Big Data technologies are operated, enabling the collection and analysis in real time of large data sets, allowing the development of predictive models for a variety of situations, from consumer behavior to the prevention of factory failures, and optimizing activities on the most varied fronts of activity. IoT technology brings changes both in the development of more pervasive connectivity and in the increase of data processing, derived from the refinement of sensors that allow data collection in different environments. All of this is associated with some practical solution allowing for increased efficiency, reduced human intervention, or even new business models [45], still evaluating that the AI generates a layer to enhance the value generated by the analysis of the different information captured and combined; allowing the automation of the decision-making process and actions in specific situations; bringing significant benefits to the increase in the speed of processes, reduction of the error rate due to human interference, and reduction of costs per transaction, in addition to the possibility of greater absorption of insights at each interaction that feedback and “teach” the AI algorithms (Machine Learning as an example); and making this incrementally more efficient [31].

In the digital transformation of the industry (relating the advent of the Fourth Industrial revolution), AI associates IoT with the combination of the ecosystem for data transmission between devices and the technology for analyzing this information independently, still conceptualizing the emergence of Artificial Intelligence of Things (AIoT). Considering that the IoT concept is related to the various IoT devices that collect data and create a network for transmitting critical information to administrators, on the other hand, AIoT data is processed by resources that analyze the standards providing only the information necessary for making a decision and can even make the necessary decisions without human involvement [17].

Pondering on AI, this uses algorithms to analyze data and resources through aspects such as Machine Learning by automating processes without manual intervention, incorporating with IoT gaining connectivity and capacity for data exchange. The great advantage of the IoT concept is in the various solutions involving machine-to-machine communication, integrated into a single network, where it publish and consume information. Thus, it is through the integration of IoT, with the analysis of broad data sets (Big Data Analytics), and with the performance in ecosystems using AIoT that it is possible to exceed the limits that each of these technologies has individually, developing an advanced solution to support operational management, offering predictive maintenance, and consequently increasing control, quality, and efficiency in business operations [35].

1.4 Discussion

The application of AI in the industry has been increasingly optimizing its results, in an attempt to reach its maximum degree of efficiency. AI advent is the arrangement of several technologies, which allow machines digitally to perceive, understand, act, and learn on their own actions or complement human activities, which has become a broad technology used for machine learning, predictive analytics, augmented reality, robotics, performance diagnostic software, and many others.

With entire procedures performed by machines capable of making decisions based on data, agility and increased productivity are natural consequences. Through AI, industrial production has become faster and more effective compared to human labor. Still considering the possibility of the machines performing tasks that a person would not be able to do, as is the case with dangerous raw materials or microscopic components.

AI works through the integration of factors such as the use of IoT sensors, Cloud Computing, and other technologies present in Industry 4.0, working in sync, devices equipped with AI create complex systems, which correlate the information collected and, with this, seek the best ways to carry out the activities for which they were scheduled. These new technologies are developed to work using the least amount of resources possible, whether in terms of raw material or energy consumption, still relating the point of cost reduction, the mitigation of errors, and waste of the operation.

When addressing AI applications, it is worth mentioning IIoT as a critical technological layer added to the production chain, which allows even the connection with suppliers and the analysis of the performance of its raw materials, still pondering the potential of AI in relation to security alerts, which point to the need for maintenance and performance reports in real time, indicating the best measures to be taken.

Still pondering the aspect in which machines can withstand extreme conditions that would be harmful even if perceived only in the long term for the health of the employees of industry, such as cooling cameras, chemical processes, and management of explosive materials, among others, that can be carried out almost entirely through automation.

The aspects in Industry 4.0 in relation to the digitization processes that guarantee the collection of data that were previously lost, the mitigation of risks in decision making, the optimization of operations, and the gain of agility, among others, are also mentioned. The implementation of complex AI algorithms has been enabling industries to assess and enable problem-solving and decision-making in a more complex and secure way.

Assessing that each sector of the industry receives contributions from AI in a different way, as a logistics and inventory structure can benefit from technology for identification and control of demand, for example; or industries with production chains that have different machinery, as is the case with the automotive industry, since with the use of predictive analysis, they can identify the need for maintenance on their machines.

The benefits are not the only ones since the industry receives an extremely positive impact on the use of AI in its processes. Given that it is possible to point out an increase in the quality of products and services, since AI reduces execution errors and subsequently uses operation data to analyze performance and make improvements; or even more effective new products and services, since the development can also be supported by AI to evaluate the proposed designs, identifying the material variables, the weaknesses to be improved, and the possibility of using augmented reality to make tests before actually putting it into production; or even through data analysis, it is possible to get an agile response to new market demands, considering that the needs and interests of consumers are changing with great velocity.

AI brings great advantages to the industry related to the reduction of errors, because after being trained, intelligent algorithms are able to perform very well tasks that are susceptible to errors in processes executed by humans. The reduction of costs since e-commerce stores or banks use robots (chatbot) for customer service, this allows employees to be allocated in more strategic areas, which can increase profit. So, with fewer errors and employees focused on more important processes, the company will have more time to think about the business and leave other tasks to AI.

Thus, AI through an automated process uses large volumes of data to make decisions, dispensing with human intervention and increasing productivity in different activities.

1.5 Trends

Adaptive Intelligence is about helping to generate better business decisions by integrating the computational power of internal and external data in real time with the computing infrastructure and highly scalable decision science. In this type of systems, relating the adaptive learning, the characteristics are monitored so that there is an adjustment in order to improve the process. The efficiency of these systems depends on methodologies adopted to collect and diagnose information related to needs and characteristics, in relation to how this information is processed to develop an adaptive context. These applications essentially make businesses smarter, allowing them to provide customers with better products, recommendations, and digital services, all of which generating better business results [55].

Digital twins are related to the practice of creating a computer model of an object, such as a machine or even a human organ, or yet a process like a climate. By studying the behavior of the digital twin, it is possible to analyze, understand, and predict the behavior of its counterpart in the real world and to solve issues before they occur. However, to take full advantage of the digital twin’s potential, real systems need not only be networked with each other but also need to develop the ability to “think” and act autonomously [56].

This development tends toward AI, from simple mutual perception and interaction to independent communication and optimization, also requiring integrated information systems that allow a continuous exchange of information, still demanding powerful software systems that can implement them along the entire value chain, and planning and designing products, machines, and plants, in addition to operating products and production systems. The technology of digital twins allows users to act in a much more flexible and efficient way, as well as personalize their manufacturing [57].

Intelligent Edge refers to the place where data is digitally generated, interpreted, analyzed, and treated, i.e., the use of this technology means that analyses can be managed more quickly and that the probability of data being unduly intercepted or leaked is considerably less. This technology refers to the analysis of data and the development of solutions in the place where the data is generated, reducing latency, costs, and security risks, making associated businesses more efficient, still pondering that the three largest categories of Intelligent Edge are the edges of operational technologies, IoT edges, and IT edges [58].

The use of Intelligent Edge technology helps to maximize business efficiency, since instead of sending data to a data center or even to a third party to perform processing, the analysis is performed at the location where the data is generated. This means not only that the analysis can be performed more quickly, but it also means that companies are much more self-sufficient and do not depend on potentially flawed network connections to do their job [58].

Predictive maintenance is one of the most promising branches for industrial applications based on the use of data received from the factory to avoid production failures. This type of system eliminates unnecessary maintenance and increases the probability of avoiding failures, which involves a machine or even a component with sensors capable to collect and transmit data and then analyze it, and perform storage in a database. Then, this database offers comparison points for events, as they occur [59, 60].

The predictive maintenance model aims to periodically monitor the operation of machinery, equipment, and parts in a factory, in order to detect failures before they occur and prevent interruptions in the production line, relating IoT and AI in order to assist in the survey and management of data from all sectors of production, integrating the company’s departments, performing analyzes to take advantage of the useful life of industrial equipment, indicating the real conditions of its operation, detecting possible deterioration of parts and components, and ensuring the reliability and availability of services. This information obtained is used to support decisions and present suggestions for actions and interventions, generating better results than with the use of raw data [59, 60].

1.6 Conclusions

IoT refers to the network of intelligent devices that are concerned with issues of connectivity, competition, and protocols, among other aspects. Relating the respective AI to the branch of cognitive computing caring for principles of data analysis, statistics, and other aspects. Considering that when applied together, it brings results related to the data generated by the IoT and can be processed by an AI software, which will optimize decision-making and contribute to the increase in the agility of the processes.

From the historical point of view, objects (things), people, and even nature, emitted a huge amount of data; however, humanity just could not to perceive, i.e., see, hear, or make sense of them. However, through the IoT and the data collected, humanity began to see, understand, and use it to its advantage with technological advances in practically all sectors of society. It is in this aspect that the IoT came to change the reality of the contemporary and modern world, considering that everything around the environment has intelligence and is interconnected, so that through this technology, it is possible to have access to data, or better, information. Having access to this sea of data, which through the technological potential brought by AI is able to put digital intelligence and transform them into information, i.e., knowledge, and finally, into wisdom.

Starting from the premise that it is possible to perceive the patterns of all these data, society will become more efficient, effective, increasing productivity, enhancing the quality of life of people, and the planet itself. Reflecting on the possibility of generating new insights, new activities promoting even more technological innovation. In this respect, the bridge between data collection (information) and the suitable sharing of that data, with safety and protection digital for all parties, abides the key in technological evolution.

Reflecting on the industrial sector, it is possible to identify a behavioral trend and anticipate the application of a new idea, and this premise shows that the world is heading toward the Fourth Industrial Revolution. This represents the introduction of information technology in industries, correlating a hidden potential that is the use of data, since the good use of this data increases operational efficiency, better decision-making, and even creates new business models.

Finally, IIoT brings together different technologies correlating the Information Technology (IT) initiative for resource management, planning, and decision support systems, Operations Technology (TO) that monitors, analyzes, and controls field equipment, manufacturing, and production procedure, through AI. One of the applications of this is predictive analysis, which makes it possible to forecast a given situation in the future based on information from the past and probability. From this, it is possible to get an AI to perform a certain action corresponding to a specific sensor in the IoT network indicating a specific state of the shop floor, optimizing this activity with increased precision.

Still reflecting on the digitization of processes and the entire production chain of the industry, it is the basis of Industry 4.0, with the layers of IoT and IIoT enabling the planning, control, and even tracking of production, both by digital simulation and virtualization, winning decision-making time and cost reduction. Thus, AI and IoT are tools that drive business and guarantee a competitive advantage with the possibility of generating automated and more agile services, consequently impacting the final consumer.