From Industry 4.0 to 5.0: Why AI must become human-centric to drive sustainability

While Industry 4.0 focuses on automation and cyber-physical systems, Industry 5.0 introduces a new framework based on resilience, sustainability, and human well-being. The authors find that although Industry 5.0 remains underrepresented in the literature, interest is growing rapidly as policymakers and researchers confront the social and environmental costs of unchecked automation.

New research suggests that the real impact of artificial intelligence (AI) depends less on technical sophistication and more on how industrial systems evolve alongside it. 

A new study, Artificial Intelligence and Sustainability in Industry 4.0 and 5.0: Trends, Networks of Leading Countries and Evolution of the Research Focus, published in the journal Sustainability, offers a decade-long systematic review of how AI is reshaping responsible production and consumption under the United Nations Sustainable Development Goal 12, while exposing gaps that could undermine long-term environmental gains.

From efficiency to sustainability, how AI reshaped industrial priorities

The study traces the evolution of AI applications in industry from 2015 to 2025, a period marked by rapid automation, data-driven manufacturing, and global supply chain expansion. Early research overwhelmingly focused on Industry 4.0, where AI technologies were deployed to increase productivity, reduce costs, and optimize logistics. Machine learning, robotics, and predictive analytics became standard tools for streamlining production lines and minimizing operational downtime.

However, the review shows that sustainability entered the conversation gradually rather than by design. Initially, environmental benefits such as reduced waste or energy savings were framed as secondary outcomes of efficiency improvements. Over time, rising regulatory pressure, climate targets, and supply chain disruptions pushed sustainability from a peripheral benefit to a central research theme.

The authors identify waste reduction and energy efficiency as the most heavily studied sustainability outcomes. AI-enabled demand forecasting, smart inventory management, and predictive maintenance emerged as key mechanisms for cutting material waste and lowering energy consumption. These applications helped manufacturers reduce overproduction, extend equipment lifespan, and improve resource allocation across supply networks.

Yet the study also reveals an imbalance. While waste and energy receive substantial attention, other critical sustainability dimensions remain underexplored. Water usage, air pollution, emissions monitoring, and broader ecological impacts appear far less frequently in the literature. This uneven focus suggests that AI-driven sustainability research has concentrated on areas with immediate economic returns, leaving more complex environmental challenges insufficiently addressed.

The review further notes that many AI applications operate within existing linear production models. Even when efficiency gains are achieved, they often reinforce consumption patterns that drive resource extraction and environmental strain. Without structural change, the authors warn, AI risks becoming a tool that accelerates production rather than one that enables genuine sustainability.

Industry 5.0 signals a shift toward human-centered sustainability

While Industry 4.0 focuses on automation and cyber-physical systems, Industry 5.0 introduces a new framework based on resilience, sustainability, and human well-being. The authors find that although Industry 5.0 remains underrepresented in the literature, interest is growing rapidly as policymakers and researchers confront the social and environmental costs of unchecked automation.

Industry 5.0 reframes AI not as a replacement for human labor, but as a collaborative tool designed to support workers, communities, and ecosystems. The study highlights a shift toward research that integrates ethical considerations, circular economy principles, and social responsibility into AI-driven industrial systems. This transition reflects a broader recognition that sustainability cannot be achieved through technical optimization alone.

The review shows that Industry 5.0 research increasingly addresses the need for resilient supply chains capable of adapting to shocks such as pandemics, geopolitical instability, and climate-related disruptions. AI plays a role in scenario modeling, risk assessment, and adaptive planning, helping industries respond more effectively to uncertainty. Unlike earlier models, resilience is treated as a sustainability outcome in its own right rather than a byproduct of efficiency.

Human-centric design also emerges as a defining feature of Industry 5.0. The authors observe growing attention to worker safety, skill development, and meaningful human–machine collaboration. AI systems are increasingly evaluated not only for performance, but for their impact on employment quality and organizational culture. This marks a departure from earlier research that largely sidelined social consequences.

Despite these advances, the study underscores that Industry 5.0 remains more aspirational than operational. Much of the research is conceptual, with limited empirical validation or real-world implementation. The authors argue that translating Industry 5.0 principles into practice will require coordinated policy frameworks, interdisciplinary research, and stronger alignment between technological development and sustainability governance.

Global research networks reveal gaps and future risks

The study also maps global research networks to identify which countries are shaping the AI–sustainability agenda. Large economies such as China, India, the United States, the United Kingdom, and France dominate publication volume and international collaboration. These countries act as hubs for research activity, reflecting their industrial capacity and investment in digital transformation.

However, the authors note that research influence does not always correlate with output volume. Smaller countries often achieve higher average citation impact, suggesting that quality and specialization play a significant role in shaping the field. This finding challenges the assumption that sustainability leadership is confined to industrial powerhouses.

Keyword trend analysis reveals a clear evolution in research focus over time. Early studies emphasized supply chain optimization and operational efficiency. More recent work increasingly incorporates concepts such as circular economy, digital sustainability, and ethical AI. The growing prominence of Industry 5.0-related terms signals a shift toward more holistic approaches that integrate environmental, social, and economic dimensions.

The study also identifies several unresolved risks. AI systems themselves consume significant energy, particularly when deployed at scale. Data centers, training processes, and real-time analytics contribute to carbon emissions that may offset efficiency gains elsewhere. The authors caution that sustainability assessments must account for AI’s full lifecycle impact rather than focusing solely on downstream benefits.

Rebound effects also pose a challenge. Efficiency improvements can lower production costs, encouraging higher consumption and negating environmental savings. Without regulatory safeguards and behavioral change, AI-driven efficiency may accelerate resource depletion rather than curb it.

Ethical concerns add another layer of complexity. The study highlights issues related to transparency, accountability, and social equity. AI systems that optimize supply chains or production decisions can obscure responsibility for environmental harm and exacerbate inequalities between regions and labor groups. Addressing these risks requires governance structures that extend beyond technical design.