AI’s double-edged role in future of manufacturing

The industrial AI revolution is both a solution to sustainability challenges and a potential contributor to them. A new international study explores this complex relationship, examining how automation and artificial intelligence (AI) are transforming energy consumption and sustainability strategies across Industry 4.0 and the emerging Industry 5.0 paradigm.

The study, titled "Automation and Sustainability—The Impact of AI on Energy Consumption and Other Key Features of Industry 4.0/5.0 Technologies," published in the journal Applied Sciences, analyzes global scientific research on AI-driven automation and investigates how emerging digital technologies influence energy efficiency, industrial productivity, and long-term sustainability.

The energy paradox of artificial intelligence in industry

In smart factories, machine learning systems analyze large volumes of operational data to improve productivity, reduce downtime, and optimize resource allocation. Predictive maintenance tools can anticipate equipment failures before they occur, while automated monitoring systems adjust production processes in real time to improve efficiency.

These capabilities allow industrial operations to minimize waste, streamline production cycles, and lower operational costs. AI-driven analytics also support more accurate forecasting of energy demand, allowing manufacturers to regulate energy-intensive activities and better integrate renewable power sources. In this sense, automation plays a crucial role in improving industrial sustainability.

However, the study highlights a growing contradiction. While AI-based automation improves efficiency within industrial systems, it also requires massive computational resources. Data centers, cloud infrastructure, and continuous data processing consume significant amounts of electricity. Training advanced machine learning models and running large-scale industrial analytics systems can increase overall energy demand, sometimes offsetting efficiency gains achieved in production processes.

This tension has led researchers to distinguish between two approaches to artificial intelligence: energy-intensive "Red AI," which focuses primarily on performance improvements, and "Green AI," which prioritizes computational efficiency and sustainability. The study suggests that balancing these approaches will be critical for ensuring that digital transformation does not undermine environmental goals.

Industry 4.0 technologies such as cyber-physical systems, Internet of Things devices, and automated production networks rely heavily on continuous data exchange and real-time analytics. These technologies enable intelligent control systems that monitor equipment performance, regulate energy consumption, and support automated decision-making across entire manufacturing systems. Yet the digital infrastructure required to support these capabilities significantly expands the energy footprint of industrial operations.

The researchers argue that the future of sustainable manufacturing will depend on the development of more energy-efficient AI architectures, improved hardware efficiency, and smarter integration of renewable energy sources into digital infrastructure.

Industry 4.0 to Industry 5.0: A shift toward human-centric sustainability

The study particularly focuses on the transition from Industry 4.0 to Industry 5.0, a shift that represents a broader transformation in how technology is integrated into industrial systems. Industry 4.0 focused primarily on automation, connectivity, and data-driven optimization. The next phase of industrial development expands this approach by emphasizing human-centered design, resilience, and sustainability.

Industry 5.0 technologies seek to combine the analytical power of artificial intelligence with human creativity and decision-making. Rather than replacing human workers entirely, the new paradigm emphasizes collaboration between people and intelligent machines. Collaborative robots, advanced human-machine interfaces, and adaptive automation systems allow workers to interact with AI-driven systems in more flexible and productive ways.

This human-centered approach also plays a role in improving sustainability outcomes. By integrating human expertise with machine intelligence, organizations can better manage complex decision-making processes related to energy efficiency, environmental impact, and resource management. Human oversight helps ensure that automated systems operate within broader sustainability frameworks rather than optimizing only for productivity.

Several emerging technologies are central to this transformation. Digital twins allow companies to simulate entire production systems in virtual environments, enabling engineers to test energy optimization strategies before implementing them in real-world factories. IoT sensors continuously monitor equipment performance, energy consumption, and environmental conditions, generating data that AI algorithms can use to identify inefficiencies and improve operations.

Smart factories increasingly rely on interconnected digital ecosystems where machines, sensors, and software platforms work together to coordinate industrial processes. These systems can automatically adjust production schedules, regulate energy usage, and identify opportunities for resource savings. The result is a more adaptive and responsive industrial infrastructure capable of responding to changing environmental and economic conditions.

The study also highlights the role of intelligent automation in supporting circular economy principles. By extending the lifespan of machinery through predictive maintenance and optimizing material usage during production, AI-driven systems can reduce industrial waste and improve resource efficiency.

Sustainability challenges and the future of intelligent automation

The study identifies several significant challenges associated with AI-based automation. One of the most pressing concerns is the lack of standardized methods for measuring the environmental impact of artificial intelligence systems. Without consistent metrics, it is difficult for organizations and policymakers to evaluate whether AI-driven technologies genuinely contribute to sustainability goals.

Another challenge involves the increasing complexity of digital infrastructures that support intelligent automation. Integrating AI, IoT, robotics, and cloud computing into a unified industrial system requires extensive technical coordination and cybersecurity safeguards. As automation expands, the risk of cyber threats and system vulnerabilities also grows, potentially increasing operational risks and energy costs.

Ethical and regulatory considerations also play an important role. Automated decision-making systems can introduce issues related to transparency, accountability, and data privacy. Many AI algorithms function as complex black-box systems whose internal decision processes are difficult to interpret. This lack of transparency raises questions about fairness, responsibility, and governance within automated industrial environments.

The study also notes disparities in technological adoption between different regions of the world. Industrial firms in technologically advanced economies often have greater access to digital infrastructure and research resources, allowing them to adopt Industry 4.0 and Industry 5.0 technologies more rapidly. Companies in developing economies may face barriers related to cost, technical expertise, and infrastructure limitations.

To address these challenges, the researchers call for interdisciplinary collaboration across engineering, environmental science, economics, and social sciences. Developing sustainable AI-driven industries will require coordinated efforts that combine technological innovation with regulatory frameworks and ethical guidelines.

Future research directions include developing adaptive AI systems capable of dynamically balancing performance with energy efficiency. Researchers are also exploring energy-efficient computing technologies, including edge AI systems that process data locally rather than relying on energy-intensive cloud infrastructure.

Another promising area involves integrating AI with renewable energy management systems. Smart grids and decentralized energy networks could allow factories and industrial systems to automatically optimize energy usage based on real-time availability of renewable power sources.

Looking further ahead, the study suggests that the convergence of AI, automation, and sustainability could pave the way for the emergence of Industry 6.0. This next generation of industrial systems may involve self-adaptive intelligent infrastructures capable of optimizing energy consumption, resource allocation, and environmental performance across entire industrial ecosystems.

Such systems could integrate digital twins, advanced robotics, and AI-driven decision-making into globally interconnected production networks. These networks would enable real-time monitoring of environmental impacts, resource usage, and sustainability metrics across supply chains.