A Review of the Industry 4.0 to 5.0 Transition: Exploring the Intersection, Challenges, and Opportunities of Technology and Human–Machine Collaboration

Abstract

The Industrial Revolution (IR) involves a centuries-long process of economic and societal transformation driven by industrial and technological innovation. From agrarian, craft-based societies to modern systems powered by Artificial Intelligence (AI), each IR has brought significant societal advancements yet raised concerns about future implications. As we transition from the Fourth Industrial Revolution (IR4.0) to the emergent Fifth Industrial Revolution (IR5.0), similar questions arise regarding human employment, technological control, and adaptation. During all these shifts, a recurring theme emerges as we fear the unknown and bring a concern that machines may replace humans’ hard and soft skills. Therefore, comprehensive preparation, critical discussion, and future-thinking policies are necessary to successfully navigate any industrial revolution. While IR4.0 emphasized cyber-physical systems, IoT (Internet of Things), and AI-driven automation, IR5.0 aims to integrate these technologies, keeping human, emotion, intelligence, and ethics at the center. This paper critically examines this transition by highlighting the technological foundations, socioeconomic implications, challenges, and opportunities involved. We explore the role of AI, blockchain, edge computing, and immersive technologies in shaping IR5.0, along with workforce reskilling strategies to bridge the potential skills gap. Learning from historic patterns will enable us to navigate this era of change and mitigate any uncertainties in the future.

1. Introduction

The IR is better understood as a process of economic transformation rather than a fixed period in a particular setting [1]. This perspective acknowledges the spatial and temporal heterogeneity in adopting IR across global contexts. For instance, while regions such as the United States and Western Europe began undergoing their Second Industrial Revolution (IR2.0) by the late 19th century, other areas, particularly in Asia, including China, India, and Korea, did not commence their First Industrial Revolution (IR1.0) until the 20th century. However, Japan, despite being a latecomer to the IR1.0, accelerated its industrial growth during the Meiji period, becoming a significant player in the IR2.0 by the early 20th century and laying the foundation for its later status as an Asian economic miracle [2].

Such variations in the pace and timing of industrialization prove the importance of viewing IR as a spectrum of changes rather than distinct events. Multiple industrial and technological breakthroughs within this spectrum can overlap within specific geographic regions. Despite the disparate technological leaps across various IRs, several common factors serve as litmus tests for identifying a new IR. These factors include elevated levels of productivity, better transportation, demand for new soft and hard skills, resource augmentation, political stability, and the availability of financial capital for investment. Furthermore, the interplay among these factors critically determines the pace and success of industrial transformations in different regions. For example, the synergy between technological innovation and the development of human capital can significantly accelerate economic growth, while inadequate infrastructure or political instability can hinder progress, leading to uneven development.

The precise start and end dates of IRs remain subjects of debate among historians as the social and economic changes unfold at varying paces across different regions. However, historical analysis reveals four major shifts that have shaped our known civilization. IR1.0, or Industry 1.0, began in the late 18th century with the introduction of water and steam-powered mechanical manufacturing facilities. This era saw the transition from manual production methods to machines, which marked the beginning of industrialization. The invention of the steam engine by James Watt in 1769 was a pivotal moment, enabling the mechanization of production processes and a new era of transportation [3]. At first, this transformation was seen as a cause of poverty and hardship because machines replaced human workers without proper protections or regulations. Companies and profit-driven organizations responded by reducing working hours and wages. However, this shift also led to major societal progress by improving workplace communication and increasing production rates, paving the way for future industrial advancements.

The IR2.0, or Industry 2.0, emerged in the late 19th to early 20th century. This period was characterized by the widespread adoption of electricity and the development of assembly lines in production [4]. During this time, industries began to capitalize on natural and synthetic resources (e.g., rare earth elements, plastics, alloys, and chemicals), which played a pivotal role in producing machinery and tools, paving the way for the automation of factory environments. Major advances during this period included (1) the introduction of signal processing and its application in telephone communication in the 1870s, (2) structural improvements utilizing steel for buildings that resulted in the construction of the first skyscrapers, as well as (3) innovations such as phonographs and motion pictures in the 1890s. Additionally, the introduction of generators and refrigerators gradually replaced the water and steam-powered engines of the IR1.0, which marks a significant transition in energy utilization and production capabilities.

The Third Industrial Revolution (IR3.0), or Industry 3.0, began in the 1970s and is often referred to as the Digital Revolution. This era witnessed the rise of electronics, information technology, and automated production. The development of programmable logic controllers (PLCs) and robotics significantly enhanced automation within manufacturing processes [4,5]. Moreover, the introduction of computers and the internet laid the foundation for the digital transformation of industries and set the stage for the subsequent phase of industrial evolution. One notable consequence of this IR3.0 was the contraction of the blue-collar job market, driven by widespread automation and increased productivity. However, this reduction was not uniform and was geographically localized. The primary reason was that Western nations began to outsource production to relatively low-wage countries, which led to the proliferation of labor-intensive manufacturing jobs within the Asian economy.

The IR4.0, or Industry 4.0, started in the early 2000s and represents a leap in manufacturing and industrial practices characterized by the integration of advanced digital technologies into production processes. Industry 4.0 marked the integration of cyber-physical systems, IoT, big data analytics, cloud computing, and AI, leading to the emergence of smart factories that optimize efficiency, automation, and data-driven decision-making [6,7,8]. IR4.0 technologies offered significant opportunities while simultaneously posing considerable challenges. On the one hand, organizations could leverage these technologies to improve decision-making processes, enhance productivity, and reduce operational costs [9,10]. For instance, the integration of sensor technologies enabled real-time monitoring and control of manufacturing processes, which improved product quality and minimized waste [9]. Despite the advantages of Industry 4.0, small and medium enterprises (SMEs) encountered major challenges, including limited financial resources, workforce skill gaps, and resistance to technological adoption. The high costs of implementation, coupled with a lack of expertise, created barriers to integrating advanced automation and AI-driven decision-making [11,12,13]. Nevertheless, the impact of Industry 4.0 extended beyond just operational improvements; it also had broader implications for sustainability and environmental responsibility. The adoption of Industry 4.0 practices contributed to sustainable manufacturing by optimizing resource use and minimizing waste [14,15].

Building on the foundations of Industry 4.0, Industry 5.0 is emerging as a new paradigm rather than a mere extension. Unlike past industrial revolutions that unfolded over centuries, the rapid pace of technological breakthroughs today justifies recognizing IR5.0 as a distinct era. From that point of view, IR5.0 builds upon the IR4.0 paradigm by emphasizing human–machine cooperation as a central tenet. Industry 5.0 builds on the automation and digitization of Industry 4.0 by prioritizing human–machine collaboration. Rather than solely focusing on efficiency, IR5.0 emphasizes the integration of human cognitive abilities, adaptability, and ethical considerations into industrial systems, fostering a more balanced synergy between humans and technology [16,17].

Some key components of IR5.0 are the human-centric approach, circular economy, and enhanced resilience. This paradigm focuses on the welfare of humans and augmenting us through technology [18]. For example, collaborative robots (co-bots) are designed to undertake repetitive and hazardous tasks, enabling human workers to focus on more innovative and value-added responsibilities [19]. This technological support increases workers’ occupational satisfaction and motivates them to enhance their creative problem-solving abilities [20]. Another defining characteristic of Industry 5.0 is its commitment to sustainability and the circular economy. Recognizing the planet’s finite resources, IR5.0 prioritizes energy-efficient production, waste reduction, and ethical industrial practices. Emerging technologies such as AI, IoT, and blockchain are increasingly adopted to enhance resource optimization, cybersecurity, and environmental responsibility in manufacturing [21,22]. Similarly, resilience, which is the capacity of systems to maintain constant operations in the face of uncertainty or crisis events, plays a pivotal role in IR 5.0. In this context, resilience is not just about recovery but also involves the proactive adaptation and evolution of systems to withstand disruptions across the industry. For example, the widespread use of AI in data acquisition, interpretation, and evaluation in IR5.0 strengthens supply chain networks by implementing advanced technologies such as predictive disruptions, maintenance, anticipating potential failures, and minimizing downtime, thus making the whole supply chain more resilient.

Researchers and industry practitioners are diligently working to ensure the successful integration of these key components in this new era of IR5.0. Continuous efforts to push the boundaries of our capabilities and knowledge are essential to achieve this. Therefore, this paper seeks to establish a critical assessment for understanding the natural progression from the machine-driven, automated environments characteristic of IR4.0 to the more human-centric vision of IR5.0, where collaboration between humans and machines becomes paramount. In this review paper, we discuss the breakthroughs that are visibly leading us toward this goal as well as those that remain relatively unknown. This discussion includes insights from multidisciplinary applications (i.e., science, engineering, ergonomics, psychology, and ethics) and addresses the technologies that are going to shape the world we want to inhabit over the next few decades. In particular, we address the application of IoT, big data, physics-informed machine learning, additive manufacturing, robotics, and human–machine interaction. Furthermore, we discuss advancements in AI, explainable AI, and cyber-physical systems, especially in terms of vulnerabilities and informed decision-making. Throughout each major IR, concerns regarding job security and the necessity for upskilling have been prominent issues, which this review paper also addresses. We will discuss the tools likely to emerge at the forefront of this revolution and examine how upskilling the workforce in utilizing these tools (e.g., extended reality (XR), brain–computer interfaces, generative AI, human–computer interaction, and blockchain) will benefit future growth and adaptation. By analyzing past trends and emerging technological shifts, this study provides critical insights into the challenges and opportunities defining the transition to Industry 5.0.

The remainder of this paper is structured as follows. Section 2 discusses the methods used to determine the scope of this research. Section 3 explores the technological foundation of Industry 4.0, detailing key advancements such as IoT, big data, and cyber-physical systems. Section 4 discusses the emergence of Industry 5.0, highlighting the shift toward human–machine collaboration, sustainability, and resilience. Section 5 examines the socioeconomic implications of this transition, including workforce upskilling and ethical considerations. Section 6 presents key tools and techniques that facilitate this shift, while Section 7 outlines real-world applications and opportunities across various industries. Finally, Section 8 addresses the challenges and future directions of Industry 5.0, concluding with insights on the evolving industrial landscape.

2. Methods

To identify the scope of this review paper, we conducted an extensive bibliometric network analysis. Initially, we retrieved over 30,000 documents from the Scopus database with the keyword “Industrial Revolution”, then filtered them down to approximately 19,000 documents, to include only articles, conference papers, and book chapters. The title, abstract, keywords, and author information of these selected documents were exported in RefWorks (RIS) format. The collected bibliometric data were then sorted, analyzed, and visualized using VOSviewer software 1.6.20 as shown in Figure 1, which is a widely used tool for constructing and visualizing bibliometric networks for journals, authors, and keywords. The bibliometric networks can illustrate different types of relationships, including citations, keywords co-occurrence, co-citations, and co-authorships. In these network visualizations, each item is represented by its label, and the size of each circle reflects the significance or frequency of the keyword or author. The larger the circle, the greater the weight or frequency of the item. Each color represents a cluster of closely related items, and the distance between two keywords approximately indicates their relatedness based on co-occurrence; the closer the keywords are to each other, the stronger their connection. The keyword co-occurrence visualization shown here from the selected 19,000 Scopus-indexed documents on “Industrial Revolution” served as a guideline to outline the scope of this paper. From our analysis, we identified three primary clusters within the keyword map: one centered around Industry 4.0, another focused on AI and the IoT, and the third emphasizing sustainability and human-centered approaches.

The most dominant research cluster we observed is Industry 4.0, a central theme connecting multiple domains such as smart manufacturing, supply chains, digitalization, education and training, and augmented reality. Closely linked to this is the cluster focusing on AI and IoT, which includes machine learning, deep learning, blockchain, cybersecurity, and cyber-physical systems, which highlights the role of AI-driven automation in industrial transformations. An emerging presence of Industry 5.0, positioned between these two major clusters, suggests a gradual shift from pure automation towards more collaborative interactions between humans and AI systems. The third major cluster emphasizes sustainability and societal impacts, placing humans at the core. This cluster includes critical topics such as sustainable development, climate change, circular economy, and economic growth. This reflects the increasing emphasis on balancing technological progress with environmental and social responsibility. The visualization also highlights historical and economic dimensions of industrial revolutions through keywords history, economics, energy, agriculture, and that indicates the current research extends beyond purely technological aspects.

Similarly, we generated a bibliometric author network (Figure 2) which highlights the prominent researcher collaborations and thematic groupings in the industrial revolution literature. Here, several distinct, interconnected clusters were identified and represented by a specific color. As shown in the large, densely connected red cluster, which identifies a core group of highly influential authors who frequently collaborate within their robust collaborative network. Smaller clusters of various colors (such as blue, green, and purple) reflect additional researcher groups, likely indicating regional or thematic specializations (e.g., agriculture, energy, history, biasness, economics). Additionally, the presence of smaller or isolated clusters suggests emerging research topics or specialized areas that are currently peripheral but may represent promising directions for future research.

3. Technological Foundation of IR4.0

The technological foundation of IR4.0 is primarily based on the convergence of IoT, digital twin of industrial processes, cloud computing, robotic systems, and advanced analytics. However, all these technologies did not appear overnight. Rather, they gradually matured for decades and reached a point where seamless integration became feasible at scale. For instance, sensor technologies have existed for years, but their miniaturization and plummeting costs now enable real-time, accurate data collection across industries. Similarly, the once-theoretical concepts of digital twins and XR (Extended Reality) have become increasingly applied and fundamentally altered how products are designed, tested, produced, and consumed. This shift from isolated technological breakthroughs to interconnected, data-rich ecosystems laid the foundation for the current time in which every machine, process, and worker is digitally aware of and capable of their improvement.

3.1. IoT

IoT is one of the key components of IR4.0, which represents the interconnectedness of the device network to exchange and transmit data [23]. In an industrial environment, IoT allows interaction between devices, sensors, equipment, and systems, also called the Industrial Internet of Things (IIoT). IoT provides real-time insights, stimulates automation of decision-making, and helps to innovate manufacturing or supply chain processes [24]. One of the most popular IoT contributions to IR4.0 is real-time monitoring of the process. Sensors equipped in machines collect multiple streams of data, such as temperature, vibration, pressure, and humidity, to ensure the system operates within designated thresholds. Nowadays, companies worldwide utilize IoT smart devices to monitor equipment performance, predict the necessity of maintenance, and perform diverse functionalities to reduce operation idle time and increase productivity [23].

In addition, IoT contributes to process optimization by supporting remote access and distinguishing process bottlenecks, root causes, and potential improvement areas. Within supply chain management framework, IoT provides visibility among supply chain entities by generating smart logistic solutions from material to product delivery. Production floor IoT devices such as Radio Frequency Identification (RFID) [25], Ultrawide Band (UWB) [26], Global Positioning System (GPS), vision systems, condition monitoring sensors, proximity sensors, pressure sensors, temperature sensors, actuators help to track the work in progress status, locate objects, identify bottlenecks, manage inventory and maintain safety and security. However, despite having advancements and widespread use cases in IoT, there are major challenges such as data security, interoperability, and expansion [27]. Research is currently being conducted to ensure better encryption, standardized protocols, and edge computing to overcome those challenges.

In addition to IoT technologies that enable device interconnectivity and real-time management of industrial systems, human–machine interaction (HMI) is increasingly essential in industrial environments. HMI acts as a critical component within the IIoT framework, particularly enhancing real-time process monitoring and bridging the gap between human operators and automated systems. Industrial environments frequently utilize HMIs for intuitive and efficient operator control, often integrating them seamlessly with Supervisory Control and Data Acquisition (SCADA) systems. SCADA systems facilitate centralized data collection, process visualization, and remote control over multiple operations. Additionally, Graphical User Interfaces (GUIs) improve usability by providing interactive dashboards that display real-time data, alerts, and processing insights which enables quicker and better-informed decision-making by users.

3.2. Big Data

Nowadays, it is quite common that thousands of tiny sensors on a production floor generate such vast amounts of data that traditional storage and analysis methods cannot cope. Modern manufacturing encounters both its challenges and greatest opportunities in this domain. Clive Humby stated, “Data is the new oil”, although some argue data are even more valuable [28]. By transforming raw streams of logs, readings, and performance metrics into actionable insights, the utilization of big data has become a true game changer for IR4.0. The amount of data generated worldwide has exploded, and its promise to drive productivity growth is visible in every sector [29].

The integration of big data analytics approaches and frameworks allows predictive analysis, through which organizations can predict anomalies proactively. Some popular tools at present are Apache Hadoop ecosystem, Apache Spark, Time series databases, Azure IoT analytics, NoSQL databases, and communication protocols like Message Queuing Telemetry Transport (MQTT), which facilitates lightweight messaging and streaming. Smart machines linked to centralized systems can dynamically transmit data that can be analyzed and integrated to forecast potential failures [30]. The deployed algorithms analyze both historical and current data streams to identify potential patterns of anomalies. This approach is extensively used in credit card fraud detection, demand forecasting, inventory management, intrusion detection, cybersecurity, and manufacturing. Consequently, these interconnected IoT devices consistently generate diverse data and support quality management processes through real-time online anomaly detection, thus ensuring seamless production with enhanced quality products [31].

3.3. Digital Twin of Industrial Processes

The concept of digital twins was once considered science fiction, but it is now a reality with many real-world applications. Digital twins essentially generate virtual replicas reflecting physical entities, processes, and systems. This digital replica is interconnected with physical systems in real-time through sensors and datasets to perform simulation, analysis, and optimization tasks of the process by utilizing Cyber-Physical Systems (CPS) concept [32]. Rather than relying on theoretical models or best-guess estimates, both virtual and physical entities can pull in large amounts of real-time actual sensor or simulated data and allow engineers to experiment with new configurations to predict potential breakdowns in a no-risk virtual sandbox. This dual reality not only reduces the time and cost typically associated with iterative prototyping but also creates a feedback loop that continuously refines itself and increases efficiency and resilience [33]. For instance, in the automotive manufacturing process, such twins assess new materials, compositions, production methods, and innovation. In the production aspect, such twins can optimize processes by monitoring real-time data, detecting insufficiency, and predicting potential failure by comparing them with the standard virtual twin. NVIDIA, one of the leaders in this field of digital twin architecture, recently introduced “Mega”, a blueprint within the Omniverse designed to help develop, test, and optimize AI and robotic fleets on a large scale in a digital twin environment before any real-world implementation [34]. Advanced warehouses and factories are now using extensive virtual fleets of autonomous mobile robots (AMRs), robotic arm manipulators, and humanoid robots working alongside human operators [35] for testing and validation before implementation (Figure 3). This demands thorough simulation-based training to streamline operations, enhance safety, and reduce interruptions. Such a continuous data acquisition environment provides a manufacturing process with automated derivation of optimization measures and parameters [36]. Moreover, remote control has emerged as a significant application in manufacturing, defense, and healthcare. It can lower non-value-added transportation costs and ensure acceptable safety in systems where local access is limited and hazardous [37].

3.4. Physics-Informed Machine Learning (PIML)

PIML integrates machine learning models with principles of physics, designing algorithms that adhere to the governing laws of thermodynamics, fluid dynamics, and materials science [38]. Training deep neural networks requires large datasets, which are not always accessible. Here, the laws of physics serve as a complement to training the neural network of low-dimensional data. PIML facilitates the simulation or analysis of systems by leveraging limited datasets from complex physical systems. One primary application area of PIML is simulation acceleration, which is highly expensive in computation. Although there has been good progress in simulating multi-physics problems using the numerical discretization of partial differential equations (PDEs), the incorporation of noisy data into algorithms poses numerous challenges. These difficulties arise from the complexity of mesh generation and the high-dimensional constraints influenced by parameterized PDEs. PIML researchers are actively working to overcome these obstacles by merging data with mathematical models and implementing them through neural networks or other kernel-based regression methods [39]. Currently, there is a lot of research focusing on the integration of PIML in predictive maintenance, manufacturing process control [40,41], metal additive manufacturing [42,43], climate modeling [44], prognostics, and health management [45].

3.5. Additive Manufacturing

Additive manufacturing (AM), commonly referred to as rapid prototyping, 3D printing, layer manufacturing, and solid freeform fabrication, has transformed traditional manufacturing paradigms over the past decade [46]. AM is used in a variety of industries to rapidly develop a representation of design factors through a 3D model or prototype before releasing the products. The foundation of AM includes a three-dimensional computer-aided design to fabricate an object [47]. This method of manufacturing demonstrated significant cost-effectiveness and architectural flexibility due to the capability to create complex geometric structures for manufacturing customized products that were previously impossible with traditional manufacturing technologies. Additionally, various types of material sources, including liquid, filament, powder, and solid sheet, are utilized to enhance manufacturing through AM. These materials opened a new era for applications in energy, automotive, aerospace, and biomedical fields [46]. For example, AM is utilized to create personalized patient-specific implants, substituting hard tissues or bones by fabricating biocompatible mesh arrays [48]. Despite its initial focus on applications in producing relatively small-scale and intricate parts advancements in technology, decreasing material costs, and the broadening of applications have led to the increasing utilization of AM across various sectors, ranging from miniature robots [49] and prosthetics [50] to large structures such as houses and boats [51].

3.6. Robotic Systems

Robotics systems are another important node in the digitally connected network of IR4.0. Today’s robotic systems are not the bulky, caged industrial robots we used to see decades ago, rather, they are more mobile, agile, collaborative, and work alongside humans as assistants. The adoption of robotics in IR4.0 has significantly increased over the last decade due to more efficient algorithms, machine vision technology, upgraded sensors, and the development of lighter, less expensive, and more powerful chips. These advancements aimed to enhance productivity and produce high-quality products with great accuracy in a short amount of time [52]. One of the key contributions of robotics systems for IR4.0 is process automation. Automated Guided Vehicles (AGVs) are a popular type of robotics that can move and operate autonomously. They are used in industrial applications to transport heavy or hazardous materials in factories and warehouses. Common types of AGVs in the manufacturing process include fork trucks, unit loaders (AGVs with roller tables for transporting trays), and tuggers (AGVs that pull carts) [53]. This type of small robotic system can reduce the physical effort involved in handling heavy loads and compensate for the limited strength of human operators [54].

However, while existing AGVs follow predefined paths, the emergence of AMRs shows a more efficient and intelligent approach to material handling. AMRs utilize advanced AI, computer vision-based navigation, and adaptive decision-making algorithms to work dynamically compared to existing AGVs. The AMRs show improved autonomy in our industrial environments. Moreover, robotic systems offer consistency via automated solutions that minimize performance errors. Due to automation and remote-control capabilities, we can operate robotic systems in different environments to ensure consistency and robustness [55]. Nowadays, collaborative robots (co-bots) of various forms are integrated into the industrial framework, offering safety and efficiency while working alongside human operators.

3.7. Immersive Technologies

Immersive technology or XR is a broad terminology that includes AR (Augmented Reality), VR (Virtual Reality), and MR (Mixed Reality). These technologies merge physical environments with virtual worlds and enable users to have intuitive and immersive experiences. Industries have heavily invested in XR to improve training, manufacturing processes, and operational efficiency. As XR systems in the industry evolve, they show the potential for a future where virtual environments integrate with daily routines. Allowing individuals to attend professional meetings, remote factory visits, and even doctor visits without physical presence. Ongoing research and developments in this field show that these technologies are increasingly applied to enhance user experiences in education [56], marketing [57], entertainment [58], and healthcare [59]. For example, AR has been used in medical task simulations, demonstrating its potential in healthcare training and design evaluation [60]. Moreover, machine learning models can be leveraged to enhance XR-based training by optimizing real-time decision support and predictive analytics for medical simulations [61].

As XR applications continue to expand, research on user experience in human–machine interaction has gained significant attention. Effective interaction design in XR environments requires an understanding of cognitive load, usability, and engagement to enhance user satisfaction and task performance. Recent UX research trends emphasize adaptive interfaces that respond to users’ physiological and behavioral cues to ensure a more seamless and personalized experience [62]. Advancements in haptic feedback, eye-tracking, and spatial audio are being integrated to improve immersion and reduce sensory conflicts that can lead to discomfort or fatigue. Therefore, focusing on user experience in XR settings is becoming crucial for optimizing interaction flow, reducing cognitive strain, and ensuring that virtual environments align with users’ expectations and real-world applications. These UX-driven insights play a key role in refining XR technologies to create intuitive, efficient, and engaging experiences across various industries.

4. Emergence of IR 5.0

While we benefit from the connectivity and automation introduced by Industry 4.0, a new paradigm is emerging that transcends technology. IR5.0, the next phase of the industrial revolution, places human creativity and well-being at its core, driven by data analytics and AI, while emphasizing sustainability, resilience, and ethical responsibility. Building on the solid technological foundation of IR4.0, IR5.0 recognizes that industrial revolutions are neither abrupt transformations nor rigid binaries, but rather ongoing progressions shaped by our needs and available resources. As we strive to meet those needs, we risk exhausting our mental and physical capacities with information overload, and we aim to tackle this challenge in this new era by human-centric innovation. This revolution also addresses environmental concerns by emphasizing sustainability and the circular economy, which were absent in previous eras. All these advancements are helping us address complex engineering challenges and medical inquiries and enabling us to live longer and even envision life beyond our planet.

4.1. Symbiosis of Human and Machine Intelligence

The symbiosis between humans and machines involves collaboration to achieve results that surpass individual limits. We humans are sentient beings, while machines are preprogrammed to perform specific tasks repeatedly. Essentially, humans can be viewed as machines with consciousness, and both possess different types of intelligence. Just as someone who is color blind cannot understand the concept of a rainbow, no matter how detailed the description is, machines powered by the most advanced AI similarly cannot inherit consciousness. Machines can produce reasonable outputs across various formats, but this is merely the statistical mapping of their learning. Due to these limitations, this symbiosis is important, where machine intelligence excels in high-speed processing, pattern recognition, and predictive analytics, allowing humans to focus on tasks that require insight, creativity, and ethical judgment [63]. For example, in advanced manufacturing environments, it is common that AI-driven systems monitor real-time production data to uncover hidden inefficiencies, while human engineers remain crucial for interpreting context-specific nuances and making strategic decisions [64]. Likewise, clinical support platforms utilize machine learning algorithms to analyze extensive medical databases, helping physicians diagnose complex conditions more accurately and swiftly. However, the ultimate authority lies with medical professionals, who integrate empathy, ethical considerations, and personal experience into patient care [65]. We should nurture the idea that diverse teams outperform homogeneous ones, similar to the collaboration between people and machines. We believe organizations can pursue two goals: first, creating an intellectual division of labor to enhance processing capabilities, and second, promoting a culture that embraces collaborative and trustworthy hybrid intelligence. By merging the rapid processing abilities of machines with human adaptability and moral reasoning, this collaborative approach can increase productivity and ensure that innovation remains human-centric and responsible.

4.2. Emotional Intelligence

Emotional intelligence refers to one’s ability to manage their own emotions while understanding the emotions of those around them through self-awareness, self-regulation, motivation, empathy, and social skills. Leaders with high emotional intelligence can defuse conflicts, empathize with team members’ concerns, and foster an inclusive environment where innovative ideas thrive [66]. Across industries, emotionally attuned leaders excel at balancing data-driven strategies with interpersonal nuances so that human considerations are not overshadowed by technical objectives. This focus on human-centric skills aligns with the broader principles of IR5.0, where technological advancements and emotional well-being converge to create a more balanced, sustainable ecosystem.

It was long believed that only humans possess emotional intelligence, setting us apart from machines. However, modern machines can leverage the vast amount of data available to respond not just to raw data but also to multimodal outputs. One such use case is emotional intelligence for computers, where machines can actively interpret emotions through machine learning algorithms. Today, computers are becoming more adept at understanding emotions through specialized research on emotional intelligence called affective computing [67]. Through affective computing, systems and devices can recognize, interpret, process, and simulate human experiences, feelings, or emotions. When computers are capable of analyzing data such as facial expressions, gestures, tone of voice, and keystroke dynamics, researchers call this artificial emotional intelligence. This capability enables humans and machines to interact more naturally, resembling human-to-human interactions. As the field of artificial emotional intelligence continues to evolve, many companies actively use affective computing to enhance their services and products. Affectiva, an emotion-recognition software company, enables advertisers and video marketers to gather real-time facial expressions through Affdex [68]. By comparing these expressions with a robust emotion database and benchmarks, clients gain actionable insights to refine their content and media investments. Realeyes, meanwhile, uses webcams, computer vision, and AI to analyze viewers’ facial expressions when they watch videos, allowing brands like Coca-Cola and Hershey’s to evaluate and improve their advertisement performance [69]. At the MIT Media Lab, BioEssence developed a wearable device that tracks changes in heart rate to identify stress, pain, or frustration and then emits calming scents to calm users [70]. Such advancement in artificial emotional intelligence is becoming increasingly important to steer us toward a deeper understanding of human emotion so that emerging technologies remain closely aligned with human well-being.

4.3. Environment, Sustainability, and Circular Economy

The idea of smart factories, robots working alongside humans, and personalized mass production may seem futuristic, but these innovations, either partially or fully, have been in existence for years. What sets IR5.0 apart is not just the integration of these technologies but its focus on sustainable technology so that industrial progress aligns with our environmental responsibility. While IR4.0 focused more on digitization and automation, IR5.0 represents a broader shift toward balancing technological advancements with sustainable development. As was already discussed, IR5.0 does not view progress purely through the lens of efficiency and speed. Instead, it acknowledges the urgent need to rethink how industries operate in a world with finite resources. This shift influences everything from energy policies and supply chain management to manufacturing processes and product life cycles. Governments and corporations now recognize that sustainability is no longer optional but essential for long-term economic and environmental stability.

A strong commitment to sustainability is visible across industries. We can see that countries are increasingly investing in renewable energy sources such as solar, wind, hydropower, and nuclear alternatives to reduce dependence on fossil fuels. The auto industry provides a clear example of this transformation. The global shift from internal combustion engines to electric vehicles (EVs) is not just about reducing emissions but also about redefining the energy sector. Just a decade ago, large-scale battery storage was considered impractical, but advancements in lithium-ion technology have proven otherwise. According to the International Energy Agency (IEA) report, global energy storage demand is projected to rise from 850 GWh as of 2023 to 10 TWh by 2035 [71]. Of this demand, 90% comes from automakers such as Tesla, BYD, General Motors, and Ford, as they are investing heavily in their fleet electrification [72,73]. On the other hand, miners around the world are working on extracting raw materials such as lithium, nickel, and cobalt to meet their rising demand [74]. At the same time, traditional automakers such as Toyota and Honda are also exploring hydrogen-powered alternatives to reduce long-term reliance on traditional fossil-based energy sources [75].

In addition to energy and transportation, this focus on sustainability is reshaping the entire manufacturing landscape as well. Consumers are now increasingly aware of product life cycles, which is pushing industries to shift from the traditional “take–make–dispose” model to a circular economy approach that emphasizes reuse, recycling, and remanufacturing [76]. This shift is not limited to physical goods only, it extends to digital tools, services, and even software. Companies are adopting reverse logistics systems to optimize the collection and reusing of industrial and consumer waste [77]. At the same time, advanced manufacturing methods, such as AM, are helping minimize waste by enabling precise, on-demand production without the excess material loss associated with traditional manufacturing techniques.

Similarly, to support this sustainability philosophy, new business models are emerging across industries. For example, instead of selling products outright, some companies are adopting a “product-as-a-service” model [78]. A Chinese electric vehicle manufacturer, NIO, has introduced an innovative battery-swapping model as an alternative to conventional charging [79]. This approach addresses a key concern for EV owners, which is the lengthy recharging process. It allows customers to quickly swap depleted batteries for fully charged ones in refueling stations. Additionally, it alleviates worries about battery longevity and performance, offering a more seamless user experience and customer-centric business model. Beyond convenience, this strategy also promotes sustainability by centralizing battery ownership, which enables more efficient recycling and material reuse.

Government policies are also playing a crucial role in shaping this transition. Regulatory frameworks such as the European Green Deal, which aims for carbon neutrality by 2050 [80], and the U.S. Inflation Reduction Act (2022) [81], which offers tax incentives for green technology, are accelerating the shift toward sustainable industry practices. In addition, the United Nations’ Sustainable Development Goals (SDGs) [82], particularly SDG 9 (Industry, Innovation, and Infrastructure), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action) closely align with the sustainability and human-centric innovation vision of IR5.0. Collectively, these international policy frameworks reinforce the direction towards sustainable manufacturing practices, responsible consumption, innovative infrastructure, and climate-conscious industrial growth, thereby supporting the broader societal objectives that define Industry 5.0.

4.4. The AI Revolution

Today, there is a growing emphasis on personalized solutions and human-centric innovations. Every individual is unique, and we react differently to different stimuli, which makes generalized approaches increasingly outdated. Therefore, the demand for personalized solutions is growing, but such solutions require vast amounts of data. Data have always existed, but not in abundance or in a usable format to make it useful for intelligent decision-making. Due to that, previously, we could not use data to extract meaningful information with traditional statistical analysis or available AI tools to provide highly personalized solutions. This is where AI comes into play, with the immense power of knowing the unknown and revealing the unseen. The concept of AI has existed for centuries, frequently portrayed in science fiction as humanoid robots or supercomputers that control the world. However, understandings of AI today are not just limited to robots, but an ecosystem powered by sensors, algorithms, and computational devices.

The mathematical foundation of AI was laid by Alan Turing, who introduced the concept of a Universal Machine, now known as the Turing Machine [83]. Between the 1950s and 1970s, early AI programs such as Logic Theorist [84] and General Problem Solver attempted to solve mathematical and logical challenges. However, computational limitations led to an “AI winter” where progress stalled. AI research revived in the 1980s with the introduction of machine learning and neural networks, with some exciting works in backpropagation [85], speech and image recognition, and robotic applications. Despite this progress, limited data and processing power continued to slow development. Following that, in the 21st century, a big transformation happened in this field, fueled by big data, advanced algorithms, and enhanced hardware capabilities. A breakthrough came in 2006, when Geoffrey Hinton pioneered groundbreaking deep learning research [86]. In 2011, IBM Watson defeated Jeopardy! champions Ken Jennings and Brad Rutter [87], then in 2012, the ImageNet [88] competition showed AI’s ability to outperform humans in image recognition. In 2016, AlphaGo [89] shocked the world by defeating a Go grandmaster with its very unusual but intelligent “Move 37”. Meanwhile, commercial AI assistants such as Siri and Alexa further embedded AI into everyday life and solidified AI’s role in mainstream technology.

The AI boom accelerated significantly in 2020 with the launch of OpenAI’s GPT-3, which showcased AI’s ability to process natural language at an unprecedented scale. This latest AI revolution is driven by two major forces: algorithmic advancements and hardware improvements. The transformer architecture, introduced by Google researchers in 2017 [90], led to breakthrough AI applications such as ChatGPT, DALL-E, Meta’s LLaMA, Google’s Gemini and many more. These technologies are now capable of generating art, predicting protein structures, and even performing basic human tasks using operator agents that seemed impossible just a few years ago [91]. Meanwhile, hardware advancements have helped massive AI computations, with companies such as NVIDIA, AMD, and Intel developing chips capable of trillions of operations per second. Looking ahead, industries are advancing toward agentic AI, which will essentially be an autonomous system capable of replacing human labor in tasks such as scheduling, coding, and web browsing. The ultimate goal is artificial general intelligence (AGI) [92], which would allow AI to reason and think across multiple domains like a human. Therefore, this shift in AI development is no longer just about making machines act like humans; it is about integrating AI into our daily lives, businesses, and industries.

This recent advancement in AI research is accelerating the transition from IR4.0 to IR5.0 at an unprecedented pace. In previous industrial revolutions, technological shifts took decades or even centuries to fully materialize. However, AI-driven automation, intelligence, and adaptability are compressing this transition into just a few years. Today, we see collaboration between humans and AI-powered co-bots in manufacturing, where robots no longer just replace human labor but work alongside humans. Additionally, governments and corporations utilize AI to analyze climate data, predict natural disasters, and accelerate drug discovery, which typically takes years but can now be optimized by AI to find solutions within days or even hours. AI is also reshaping transportation and logistics. We are witnessing the rise of driverless taxis, automated freight transport, and Tesla’s full self-driving technology, which push the boundaries of autonomous mobility. Meanwhile, in the energy sector, AI is optimizing grid management, forecasting renewable energy, and improving battery efficiency, thereby contributing to sustainable industrial growth. All these products, tools, and advancements are not only related to efficiency and automation; they are also about freeing up human time for more meaningful work, such as decision-making, creativity, and innovation. As AI continues to evolve, the line between human and machine intelligence is blurring, which makes it essential for us to adapt, learn, and integrate AI into our skill sets. The current growth and pace clearly indicate that upskilling is no longer optional but necessary to stay relevant in the workforce. Therefore, in this IR5.0 era, we need to work with AI to redefine how we work, innovate, and interact with the world around us.

5. Socioeconomic Implications of IR4.0 to IR5.0 Transition

The transition from IR4.0 to IR5.0 represents a significant shift with a renewed focus on human-centricity, sustainability, and resilience [93]. This transition is driving substantial changes in workforce requirements and skill sets across industries. In addition, the workplace culture and employment approach are being reassessed. People are becoming increasingly concerned about their physical and cognitive well-being. We are also observing a faster innovation cycle, lower production costs, and new business models that are boosting our productivity and economic growth.

5.1. Workforce Upskilling

As automation and digitalization advances, there is an increasing need for workers to adapt and acquire new competencies. According to a study by the World Economic Forum, by 2030, 59% of all employees will need reskilling due to the adoption of changing technologies [94]. It is now essential for workers to go beyond technical skills and conventional qualifications; they must also cultivate adaptability, creativity, and technological fluency to succeed in a rapidly changing landscape driven by innovation and global challenges. This shift necessitates a focus on continuous learning and development to ensure workers remain relevant in the evolving industrial landscape. The demand for technical skills such as programming, data analysis, and experience with emerging technologies like AR, VR, and XR is likely to increase [95]. IR5.0 also emphasizes human-centricity and highlights the importance of soft skills such as creativity, critical thinking, and emotional intelligence [96].

Organizations are increasingly recognizing the need to invest in their existing workforce through upskilling and reskilling programs rather than solely relying on hiring new talent [97]. To address these needs, companies and educational institutions have been developing new training paradigms. Also, a systematic approach to workforce development that considers the interrelated challenges of skill shortages and technological advancements is essential [98]. This could involve partnerships with educational institutions to develop curricula that are responsive to industry demands. Furthermore, training programs and bootcamps can help individuals and organizations identify skill gaps and tailor training initiatives accordingly [99]. With advancements in AI, robotics, and digital tools reshaping industries, employers now expect employees to be proficient in using technology. The need for knowledge in AI, cybersecurity, and automation tools is skyrocketing. Some argue that due to the recent surge in AI capabilities, there may be job cuts, which is not false. However, to keep pace with market needs and remain relevant, people should not fear being replaced by AI. Instead, we should focus on exploring how to leverage AI to generate more employment opportunities and enhance its effective utilization. Therefore, competition lies not with AI, but with those who know how to effectively and efficiently use AI to automate and augment their skills. Moreover, the use of AR and VR for immersive learning experiences, alongside on-the-job training and microlearning modules for skill development, is increasing [100]. The intent is to create a workforce that is not only technically proficient but also adaptable and innovative, capable of working alongside advanced technologies while providing uniquely human insights and problem-solving abilities [93].

5.2. Ergonomics

IR5.0 brings a renewed focus on ergonomics, particularly in the context of human–machine collaboration. With the growing integration of IoT, data-centric work, and remote setups, working from home has become more popular nowadays. Moreover, during the COVID-19 pandemic, the world saw a significant shift in workplaces from office settings to home-based environments, which accelerated the adoption of human-centric practices in various industries. In addition, as workplaces become more technologically advanced, there is a growing need to design environments that enhance both human well-being and productivity [101]. To keep up with that, physical ergonomics in IR5.0 are changing due to the integration of smart technologies. Sensor-based systems and wearable devices are being used to monitor and analyze workers’ movements and postures in real-time, enabling personalized ergonomic interventions [102]. This approach not only helps in preventing musculoskeletal disorders but also contributes to increased productivity and job satisfaction. Similarly, cognitive ergonomics is gaining prominence as IR5.0 emphasizes the importance of human-centric design in complex technological environments. Research indicates that well-designed human–machine interfaces can significantly reduce cognitive load and improve decision-making processes [103]. For instance, the use of AR in industrial settings has shown promises in enhancing worker performance and reducing both mental and physical strain. The transition also introduces the concept of collaborative robots, which are designed to work alongside humans safely. This creates a new need for ergonomic design that considers the physical and cognitive interactions between humans and robots [95,104,105]. The rapid technological changes and new work paradigms associated with this transition can also impact employees’ psychological states [106]. Research indicates that organizations implementing mental health initiatives as part of their IR5.0 transition strategies see improvements in innovation, productivity, and employee satisfaction [107].

6. Tools and Techniques

As discussed in Section 2 and Section 3, IR4.0 primarily focuses on integrating cyber-physical systems, IoT, and advanced data analytics to optimize efficiency. In contrast, IR5.0 moves toward a more human-centric model, focusing on collaboration between humans and machines while addressing ergonomics, mental health, sustainability, and resilience. Driving this shift are key tools and techniques that build upon the automation-oriented paradigms of IR4.0 and human well-being and ecological balance. This section highlights these pivotal tools, some of which are inherited from earlier industrial transformations and others newly emerging.

6.1. Data Decentralization

Traditional centralized data storage systems are vulnerable to cyber threats and single points of failure, so decentralized systems distributed across multiple nodes are essential. This decentralized approach is made possible by advancements in edge computing and blockchain-based multi-node decentralized methods.

• Edge Computing

Edge computing enables us to process data locally with enhanced security and reduced latency. Its primary role is faster data analytics and automation through IoT, which benefits the industry through real-time quality control and predictive maintenance. This approach supported IIoT applications by facilitating quick and localized decision-making. For example, traditional cloud models often struggle with delays and bandwidth costs in latency-critical scenarios such as self-driving vehicles and autonomous robots [108]. Furthermore, efficient resource utilization through edge computing is another major advantage, as it optimizes network resource usage by processing time-sensitive data on-site and sending only relevant information to the cloud [109]. As we transition from IR4.0 to IR5.0, edge computing architectures evolve from focusing solely on operational efficiencies to emphasizing more human-centric and sustainable outcomes, such as personalized processes and reduced carbon footprints through localized data processing. For example, in smart manufacturing environments, edge AI can dynamically adjust robotic assistance based on workers’ physical strain levels and thus promote ergonomic safety and well-being. Furthermore, in the context of sustainability, edge computing prioritizes energy-efficient processing, reducing carbon footprints by minimizing unnecessary data transmission and optimizing resource consumption locally. Another key transformation is the shift towards federated learning and distributed intelligence at the edge, which ensures privacy-preserving AI applications. This is particularly relevant in human-centric environments where sensitive biometric or operational data needs to be processed locally rather than transmitted to centralized servers.

• Blockchain

Blockchain technology could complement edge computing by providing a secure, transparent, and decentralized data-sharing mechanism, which is one of the focal areas in IR5.0. Blockchain is an immutable, shared ledger that guarantees data integrity, traceability, and trust in complex industrial systems. With the help of a blockchain-based edge computing framework, it is possible to eliminate the concept of a single trusted entity and ensure that every time a user or server wants to enter the system, the authentication process is carried out over the network automatically, as depicted in Figure 4 [110]. This technology is important for merging advanced technologies such as AI, IoT, and cyber-physical systems. In the context of IR5.0 human–machine collaboration, blockchain supports secure data sharing, smart contracts, and decentralized decision-making. These features boost operational efficiency and reduce the dependency on centralized authorities. For example, smart contracts facilitate payments and information exchanges among manufacturers, suppliers, and clients and promote horizontal integration across the value chain [111]. On the other hand, in IR5.0, blockchain’s role extends beyond transparency to enable human-centric connections and ethical operations. For instance, blockchain-powered Unified Namespace systems securely integrate data from IoT devices and sensors [112]; it creates vertical connections between humans, machines, and systems and ensures traceable and trusted communications across different levels of the chain. Aligned with the benefits of edge computing, blockchain also promotes sustainability and resilience by reducing inefficiencies and disruption cases from various stakeholders [113].

6.2. Human–Machine Collaboration

Human–machine collaboration is at the heart of IR 5.0. Today’s industry seeks seamless collaboration between workers and advanced systems, shifting from task-based automation to a more holistic approach that prioritizes ergonomics, safety, and user. A future is emerging in which repetitive, high-risk, and data-intensive tasks can be delegated to robots, enabling humans to concentrate on strategic thinking, innovation, and problem-solving. Additionally, instead of merely replacing human tasks, the goal is to foster greater trust in AI-driven processes while addressing user needs for enhanced satisfaction and well-being. Co-bots, humanoids, wearable technologies, and immersive AR, VR, and XR technologies are a few of the tools guiding us in that direction.

• Collaborative Robots (Co-bots) and Humanoids

Co-bots are one important shift toward human–machine synergy, and they enable workers and robots to share tasks in proximity without extensive safety barriers. In contrast to traditional “caged” industrial robots, co-bots integrate advanced sensors, force-limiting joints, and intuitive programming interfaces. From the industrial example, it is quite evident that this type of robotic system is boosting the emergence of IR5.0 through a more human-centric workspace. Nowadays, co-bots work side by side with humans in industrial settings. For example, in BMW’s Spartanburg plant, co-bots assist in door assembly by precise rolling and insulation onto car doors, which is an ergonomically demanding process for human workers [114]. This benefits the workers by preventing them from performing repetitive handling processes and reducing their skeletal strain with ensured consistent assembly quality. It is also an example of effective human–robot teaming in which human workers perform the decision-making tasks, and the robots conduct the tasks that require consistency, force, and precision. Ford introduced “Robbie the Co-bot”, specifically designed to help an employee with wrist and shoulder issues in attaching covers to engine blocks [115]. By taking over the force-intensive part of the process, the co-bot significantly reduces physical strain while allowing the operator to maintain oversight and control. The recent AI boom has also witnessed significant advancements in humanoid robot research. Unlike co-bots, which are designed for collaboration with humans, humanoid robots closely resemble humans and utilize advanced AI capabilities. They are primarily trained using reinforcement learning to adapt to human actions and are being studied for complex task scenarios. Some researchers are exploring teleoperating humanoids, where a human operator controls a robot from a distance. The primary focus here is on executing tasks that demand high precision at a remote site [116]. This type of research and application will make the future of industry and workplaces more inclusive, ensure broader workforce participation, and reduce the risk of injuries.

6.3. Wearable and Immersive Technologies

• Wearable Devices

Wearable devices such as virtual gloves, head-mounted displays, exoskeletons, smart safety vests, and brain–computer interfaces (BCIs) play a crucial role in the IR5.0 transition by enhancing operator safety, ergonomics, and adaptability. Exoskeletons, such as Ekso Bionics’ Ekso EVO, help redistribute load during repetitive overhead tasks, mitigating injury risks and enabling workers with physical limitations to remain active [117]. Sanofi, for instance, employed co-bots fitted with wearable sensors for product packaging, illustrating how integrated solutions reduce strain and boost productivity [118].

Similarly, BCIs continuously capture an operator’s cognitive and emotional states from neural activity monitoring. The primary purpose of BCIs is to interact with the environment using brain signals without any motor activity, which in its finest form can be called telepathy. Apart from various invasive and non-invasive methods, the basic method includes collecting neural signals and translating them into commands using complex machine learning (as shown in Figure 5) [119]. This technology has immense potential in the IR5.0 perspective within the fields of communication, control, healthcare, and gaming. Researchers around the world are making significant progress with the advent of AI proliferation and algorithmic advancements in this area. Recently, Neuralink [120] and BCI Neural Electronic Opportunity (NEO) [121] achieved some significant breakthroughs by completing the first wireless and implantable BCI clinical human trials independently. The role of active BCI applications goes beyond just monitoring, as they enable intuitively control machines and robots. Because of this control advantage, BCIs can enable a collaborative environment where operators can manage devices through their thoughts. This development promises to improve operational efficiency and make industrial processes more accessible to those with physical limitations [122].

On the other hand, in contrast to active BCI, passive BCI interprets brain activity without the user’s conscious effort to control it, essentially “listening” to the brain’s neural signals to understand cognitive states such as emotions or attention levels. By monitoring workload, stress, or diminished vigilance, this type of BCI enables real-time interventions, such as shifting tasks, suggesting breaks, or adapting training difficulty. They also open new possibilities for hands-free robot control and more accessible work environments [123]. Passive BCIs can monitor an operator’s mental states, such as fatigue or cognitive overload, allowing for real-time interventions to prevent errors and improve overall workplace safety [124]. Additionally, BCIs contribute to personalized training and skill development in industrial settings. By adapting training programs based on real-time cognitive assessments, these technologies can optimize learning processes and prevent frustration among workers [123]. This personalized approach aligns with the core principles of IR5.0, which values adaptability and human well-being over rigid automation protocols. Their integration into industrial environments represents a significant shift from the technology-driven focus of IR4.0 to a more inclusive and adaptive paradigm that prioritizes human factors in manufacturing processes [125].

• Immersive Technologies

From the early command line interface to graphical user interfaces, to smartphones, and now to modern immersive technologies, the ways in which humans interact with technology have been continuously redefined throughout history [126] (see Figure 6). The latest generation of user interfaces, driven by advancements in spatial computing, is now commercialized through AR, VR, and MR products, enabling more seamless and intuitive interactions between humans and machines [127]. Spatial computing bridges the digital and physical worlds, integrating VR, AR, or even AI-powered MR to create real-time, adaptive environments. With capabilities such as spatial mapping, sensory integration, and computer vision, immersive technologies are evolving beyond static visualization tools into interactive, human-centric systems to facilitate collaboration, cognition, and decision-making. During IR4.0, the primary focus of VR and AR systems was on visualization and efficiency in gaming and training. However, as IR5.0 shifts toward deeper human–machine collaboration, technologies embedding affective computing to interpret cognitive and emotional states emerge. Immersive tools are no longer just static interfaces but adaptive instruments that respond dynamically to user engagement and environmental factors, offering multifaceted use cases [128]. This transition is particularly evident in industrial and manufacturing applications. Modern AR and VR systems are now used not only for design, training, and maintenance but also for real-time decision support. Such solutions expedite “virtual commissioning”, allowing companies to simulate and optimize factory layouts before physical implementation. As IR5.0 unfolds, XR is now integrated into live operations; it not only supports training and remote assistance but also collaborative design sessions, and it empowers front-line operators with context-specific insights [129]. Going beyond, AR headsets bring context-specific, key data into the operator’s field of view. In DHL warehouses, these wearables have reportedly improved picking processes by 25% [130]. Likewise, smart safety vests Elokon offers use real-time tracking to slow or halt machinery when workers enter hazardous zones [117]. Additionally, XR solutions now offer real-time visual cues, interactive work instructions, and remote expert guidance, ensuring that technology adapts to humans rather than the other way around. The shift from passive visualization to dynamic human–machine collaboration marks a defining characteristic of the IR5.0 revolution.

6.4. Generative AI

Recent advancements in AI, particularly in the form of generative AI and autonomous agents, are accelerating the transition to Industry 5.0, where human-centric and intelligent automation plays a pivotal role. Unlike traditional AI systems, which primarily relied on deterministic rule-based logic or statistical learning for automation, modern generative AI models leverage deep learning architectures, particularly transformer networks and generative adversarial networks (GANs), to generate novel solutions beyond simple pattern recognition. While earlier AI generations were designed mainly to automate large-scale, repetitive, and predefined tasks, generative AI introduces creative and adaptive capabilities such as autonomous design, advanced process optimization, and real-time decision-making support. These advancements enable a new paradigm of AI-human collaboration, where AI augments human expertise rather than only executing predefined rules [131]. This transformation holds great promise for industries; in adaptive manufacturing as an example, generative AI autonomously designs innovative materials with enhanced properties, shortening development cycles and boosting product innovation. In addition, generative AI is also influencing healthcare by accelerating drug discovery [132], predicting complex protein structures [133], and facilitating the development of novel therapeutics with unprecedented precision. This transition not only enhances efficiency but also fosters deeper synergy between human and intelligent systems, which essentially embodies IR5.0’s core principle of augmenting human creativity and decision-making rather than just automating or replacing human roles [134]. Regarding collaboration and adaptability aspects, generative AI promotes real-time collaboration and adaptability by using agentic AI to handle complex tasks with minimal oversight. Paired with co-bots, these AI-driven systems streamline production, respond dynamically to changing needs, and provide on-the-fly digital instructions. In terms of workforce empowerment in the industry, generative AI-based tools are also in use to evaluate employee skill sets, monitor personalized cognitive load, and offer customized training. This turns rigid workflows into flexible, people-focused systems that blend human expertise with AI insights [131,135].

6.5. Advanced Wireless Network

Efficient wireless networks serve as a backbone for interconnected factories. The 5G network, characterized by low latency and high connection density, enables technologies such as massive IoT, AI-driven automation, and advanced AR/VR applications for training and remote maintenance [136]. While 5G currently leads industrial connectivity, 6G, quantum, and bio-inspired networks will push the limits of human-AI collaboration in the near future. To keep pace with advancements in AI algorithms, faster data transfer, reduced latency, and AI-native networks are required [137,138]. The integration of 6G with AI and blockchain will result in self-optimizing, intelligent, industry-wide ecosystems capable of making autonomous decisions without human input. Holographic communication and digital twins powered by 6G will be significant breakthroughs, enabling engineers and operators to engage with immersive, real-time virtual environments to test, optimize, and implement manufacturing solutions before physical deployment. Some researchers also highlighted the potential of 6G to integrate AI-driven “semantic communications”, which will ensure that only contextually meaningful information is transmitted [139]. This approach will reduce bandwidth usage and support emerging concepts like “Goal-Oriented” networking and benefit use cases in energy grid management, autonomous systems, and immersive telepresence [140,141].

7. Applications and Opportunities

IR5.0 advancements are notably evident in various industry sectors, such as manufacturing and production, consumer and retail, biotechnology and healthcare, and service industry and infrastructure. In manufacturing, smart factories and future maintenance improve operational efficiency and product quality, especially in automobile, electronics, and semiconductor industries. Retailers take advantage of AI and large data to provide personalized customer experience and embrace permanent practices. Biotech industries benefit from the discovery of AI-powered drug and sustainable biomanufacturing, while healthcare sees an improvement in telemedicine and patient care. The service industries adopt IoT and knowledge-based systems for individual and efficient operations. This section focuses on how these innovations are re-designed to meet the challenges and social demands that develop industries.

7.1. Manufacturing and Production

The ongoing IR5.0 transition is significantly transforming the manufacturing and production sectors, particularly in the automobile, electronics, and semiconductor industries. Digital transformation is becoming increasingly evident, as manufacturers optimize production processes through real-time data analytics and automation [142,143]. Additionally, the transition to smart factories, where machines interact and work together with humans, results in more efficient production lines and lower operational costs [144,145]. The use of robotics and automation in electronics manufacturing has been shown to increase precision and speed, which are critical in a sector that demands high levels of accuracy due to the complexity of electronic components [146]. Additionally, the application of big data analytics in understanding consumer behavior and market trends is helping manufacturers to tailor their products more effectively [147]. Similarly, the semiconductor industry is also experiencing transformative changes due to this transition. The demand for higher efficiency and lower energy consumption in semiconductor manufacturing has led to the adoption of advanced manufacturing technologies such as AI [148]. For example, the use of AI and machine learning algorithms in design and production processes allows for the optimization of chip performance while minimizing resource usage [149].

7.2. Consumer and Retail

The personalization of consumer experiences is a hallmark of Industry 5.0. AI and big data analytics enable retailers to analyze consumer behavior and preferences in real-time, allowing for tailored marketing strategies and product recommendations [150]. This capability not only enhances customer satisfaction but also fosters brand loyalty as consumers are more likely to engage with brands that understand their individual needs [151]. Furthermore, the use of AI-driven chatbots and virtual assistants in retail settings facilitates immediate customer service and improves the overall shopping experience [152]. Moreover, the shift towards omnichannel retailing allows consumers to interact with brands across multiple platforms seamlessly. This integration of online and offline channels provides consumers with greater flexibility and convenience in their shopping experiences. For instance, consumers can research products online, check availability in physical stores, and make purchases through various digital platforms [153]. This omnichannel approach has been particularly accelerated by the COVID-19 pandemic, which has significantly changed consumer shopping behaviors and resulted in increased reliance on online shopping and contactless payment methods [154,155].

Sustainability is another essential aspect influenced by Industry 4.0 and 5.0. Consumers are increasingly aware of environmental issues and are seeking sustainable products and practices from retailers. The integration of digital services in retail allows for better traceability, transparency in supply chains, and helps consumers make informed choices about the products they purchase [151]. Retailers respond to this demand by adopting sustainable practices, such as reducing waste and utilizing eco-friendly materials, which not only appeal to environmentally conscious consumers but also enhance brand reputation [156]. Furthermore, the COVID-19 pandemic has accelerated the adoption of digital technologies in retail and caused significant shifts in consumer behavior. Many consumers have developed new shopping habits, such as increased online purchasing and a preference for local products, which have been influenced by the need for safety and convenience during the pandemic [157]. Retailers are adapting to these changes by enhancing their digital presence and offering innovative solutions, such as virtual shopping experiences and enhanced delivery services, to meet evolving consumer expectations [158].

7.3. Biotech and Healthcare

The application of AI and machine learning in biotechnology is accelerating drug discovery and enhancing precision medicine [159]. By analyzing vast datasets, AI algorithms help researchers identify potential drug candidates more efficiently, reducing both time and cost [160,161]. For instance, the application of AI in genomics allows for the rapid analysis of genetic data, facilitating personalized medicine approaches that tailor treatments to individual patients based on their genetic profiles. The use of microfluidic technologies in biotechnology helps us to have more efficient and accurate experimental methods. Microfluidics enables the manipulation of small volumes of fluids, allowing for high-throughput screening of biological samples [162]. This technology is particularly beneficial in drug development and diagnostics, where it can significantly reduce the number of reagents needed and improve the speed of experiments. As a result, researchers can now conduct more experiments in less time and make faster discoveries in biotechnological applications.

In healthcare delivery, the implementation of telemedicine and remote monitoring systems is enhancing patient care. These technologies allow healthcare providers to monitor remote patients’ health in real-time, improving the management of chronic diseases and enabling timely interventions [163]. The integration of IoT devices in healthcare settings facilitates the collection of patient data, which can be analyzed to provide insights into health trends and outcomes, ultimately leading to improved patient management strategies. Furthermore, the use of wearable health technologies allows patients to take an active role in managing their health [164]. The focus on sustainability and ethical considerations in biotechnology is also gaining momentum with the advent of IR5.0 transition. This new paradigm emphasizes the importance of human-centric approaches and environmental sustainability in biotechnological innovations. For instance, the development of bio-based products and sustainable bio-manufacturing processes is becoming increasingly relevant as industries seek to reduce their environmental footprint [165]. The integration of biotechnological solutions in healthcare, such as the use of biodegradable materials for medical devices, aligns with the growing demand for sustainable practices in the sector [166]. Also, the economic potential of biotechnology is being realized through the commercialization of innovative products and services. As biotech industries continue to grow, opportunities arise for startups and established companies to develop novel solutions that address pressing health challenges [167,168]. The collaboration between academia and industry is crucial in this regard, as it helps the translation of research findings into practical applications [169].

7.4. Service Industry

Integrating the IoT, cloud computing, and smart sensing and analytics technologies has revolutionized traditional service models. For instance, in finance, the adoption of automated systems allows for real-time data analysis and decision-making, which improves service and customer satisfaction [170]. Similarly, in education, smart technologies facilitate personalized learning experiences and meet diverse student needs more effectively [171]. Furthermore, the human-centric approach of IR 5.0 is gaining more traction in the hospitality sector, where automation tools are increasingly utilized to enhance guest experiences while maintaining a human touch. For instance, AI-driven chatbots and virtual assistants can manage routine inquiries, allowing staff to engage in more meaningful interactions with guests [172]. In addition, VR-based room demos allow guests to have a high-fidelity experience of the space even before their arrival booking.

7.5. Infrastructure and Utilities

IR5.0 transition is changing the infrastructure and utilities landscape by enhancing efficiency, promoting sustainability, and fostering resilience. Infrastructure management requires real-time monitoring and optimization of energy consumption to reduce operational costs [173]. Today, people are becoming increasingly familiar with smart meters and sensors that enable energy companies to collect data on energy usage patterns. These data can then be analyzed to optimize energy distribution and reduce waste [174]. Furthermore, the emphasis on sustainability in energy production drives the adoption of renewable sources. Similarly, the transportation sector is also experiencing significant changes due to IR5.0. The advent of autonomous vehicles, smart traffic management systems [175], and connected infrastructure is transforming how goods and people are transported. These innovations lead to reduced congestion, lower emissions, and enhanced safety [176]. For example, the implementation of AI-driven traffic management systems can optimize traffic flow and reduce travel times, thereby improving the overall efficiency of urban transportation networks [177]. Also, integrating data analytics in transportation planning enables better resource allocation and infrastructure development to meet future demands [178].

In the construction industry, tools and technologies such as Building Information Modeling, drones, and 3D printing are improving the efficiency and accuracy of construction processes [179]. These technologies enable real-time collaboration among stakeholders, reducing delays and costs associated with traditional construction methods [180]. Additionally, the focus on sustainable construction practices is leading to the development of eco-friendly materials and methods, which are essential for minimizing the environmental impact of construction activities [181]. In utilities like water management, integrating smart technologies into water supply and waste management systems enables more efficient resource management and enhances service delivery [182]. For instance, smart water meters can detect leaks and monitor consumption patterns and thus inform service providers to respond proactively to issues and optimize their operations [183]

8. Challenges and Future Directions

Industry 4.0 has introduced numerous opportunities, including enhanced efficiency, innovation, and sustainability. For instance, cutting-edge automation and AI-based predictive maintenance significantly reduce operational costs by minimizing idle time. By integrating cyber-physical systems, IoT, AI, and big data analytics, Industry 4.0 has is changing global manufacturing and supply chain management. Change is never without challenges. As progress is made, obstacles are encountered, failures occur, and valuable lessons are learned from experience. Understanding potential problems in advance can help us navigate difficult times more effectively. One of the most critical obstacles is the high cost of digital transformation. Industries must invest heavily in advanced technologies, infrastructure, and workforce upskilling. Additionally, as digitalized connectivity expands, the risk of cybersecurity threats intensifies, exposing industries to cyber-attacks and critical data leakages. Also, industries should secure interoperability among various systems, platforms, and instruments. Resistance to change among traditional industries and an uncertain regulatory environment also pose barriers to advancement. Industry 5.0 builds upon the technological advancement of Industry 4.0 and shifts to a human-centric, sustainable, and resilient industry paradigm. Industry 4.0 advances automation and digitalization by integrating AI, IoT, and cyber-physical systems. Industry 5.0 emphasizes cooperation between humans and smart technologies, fostering human–machine collaboration. Yet, ensuring its ethical and sustainable implementation requires a strong focus on workforce reskilling, enhanced cybersecurity, and robust data protection strategies. By addressing these challenges, a well-structured plan can be developed to ensure a resilient future.

One of the primary challenges in cybersecurity is the increasing complexity and interconnectivity of today’s systems. Integrating IoT devices into industrial environments creates numerous entry points for cyberattacks, making it difficult to secure networks effectively [184]. The rapid adoption of these technologies has led to new vulnerabilities that organizations must address, particularly where interconnected systems are prevalent. Moreover, the reliance on cloud computing and data sharing further complicates security measures, as organizations must ensure that data are protected across various platforms and environments [185]. Another significant challenge is the skills gap in the cybersecurity workforce. The demand for qualified cybersecurity professionals has surged due to the increasing frequency and sophistication of cyber threats [186]. Developing comprehensive cybersecurity frameworks that include training and certification programs is crucial for addressing this skills gap and ensuring that organizations are adequately prepared to face emerging threats. Furthermore, cybercriminals are becoming more adept at exploiting vulnerabilities in new technologies, which necessitates a proactive approach to cybersecurity [187]. The renewable energy sector, for example, faces unique cybersecurity challenges due to its increasing reliance on digital technologies [188]. Organizations must adopt innovative cybersecurity measures, such as AI-driven threat detection and response systems. Additionally, investing in research and development of advanced cybersecurity technologies, such as machine learning and blockchain, can provide organizations with the tools necessary to detect and mitigate threats more effectively [189]. Moreover, encouraging a culture of cybersecurity awareness within organizations is essential. This includes regular training for employees at all levels to recognize potential threats and understand their role in maintaining security [190].

Managing the vast and diverse data generated by interconnected devices and systems is another significant challenge. The complexity of managing diverse data types, from structured to unstructured data, requires robust data governance frameworks to ensure that data are accurate, accessible, and secure [191]. Furthermore, the rapid pace of technological change necessitates continuous updates to data management practices to keep pace with evolving data landscapes [192]. It is also necessary to ensure data privacy and compliance with regulations such as the General Data Protection Regulation (GDPR). Organizations must navigate complex legal landscapes while implementing data management strategies that protect user privacy and maintain compliance [193]. This is particularly critical in sectors such as healthcare and finance, where sensitive personal information is handled. It is also necessary to exercise caution in the management processes for advanced analytics and machine learning data, as they require a substantial investment in infrastructure and expertise. Organizations may face challenges in finding skilled personnel who can effectively utilize these technologies due to increased demand [194]. In parallel, the adoption of decentralized data management solutions, such as blockchain technology, can enhance data security and integrity. AI and machine learning should also be leveraged for automated data management processes, enhancing efficiency and minimizing the risk of human error in routine data handling [195].

The ethical use of data, particularly concerning privacy and consent, is another challenge in IR5.0. As organizations collect vast amounts of data from various sources, including IoT devices and customer interactions, the potential for the misuse of such data increases. The challenge lies in establishing transparent data governance frameworks prioritizing ethical considerations while leveraging data for business insights. Another significant challenge is the impact of automation and AI on employment and society. The integration of these technologies raises concerns about job displacement and the need for reskilling the workforce. The rapid development of AI technologies necessitates a reevaluation of the ethical implications surrounding labor markets and the potential for increased inequality [196]. Also, the ethical implications of AI in decision-making processes present a complex challenge. As AI systems become more prevalent in various industries, the potential for algorithmic bias and lack of accountability in automated decisions raises significant ethical concerns. Therefore, a thorough guideline on the ethical considerations associated with commercialized AI, emphasizing the importance of transparency and fairness in algorithmic processes, is required in every industry. Additionally, fostering collaboration between industry stakeholders, policymakers, and academia is essential for addressing ethical challenges. This collaborative initiative can facilitate knowledge sharing and the development of best practices that promote ethical behavior across industries and society. In addition, as the workforce adapts to new technologies, equipping employees with the skills to navigate ethical dilemmas will be crucial. Therefore, organizations should invest in education and training programs that emphasize ethical decision-making and social responsibility.

Similarly, the economic implications of transitioning to sustainable practices can pose challenges for organizations, particularly in emerging economies. The costs associated with implementing sustainable solutions can deter companies from challenging the status quo, especially when immediate economic benefits are not apparent [197]. This reluctance can hinder progress toward sustainability goals, as organizations may prioritize short-term profitability over long-term sustainability objectives. To address that, organizations should prioritize the development of sustainable business models that integrate environmental, social, and economic considerations into their core strategies. By aligning business objectives with sustainability goals, organizations can foster a culture of responsibility and accountability. Industries should find their own playing field, which will improve efficiency with sustainable growth. By utilizing data analytics, IoT, and AI, organizations can optimize their operations, monitor environmental impacts, and make informed decisions that support sustainability objectives. In addition, integrating circular economy principles into production processes can help organizations minimize waste and maximize resource utilization. Finally, collaboration among stakeholders is necessary for advancing sustainability in the IR 5.0 transition, as integrating sustainable development strategies requires a collective effort from industry leaders, policymakers, and academia to create a unified vision for sustainability. Collaborative initiatives can facilitate knowledge sharing, best practices, and innovation, ultimately driving progress toward sustainability goals.