Generative AI powers the future of sustainable Industry 5.0

Tools such as predictive maintenance and resource optimization algorithms are shown to reduce energy usage and prevent equipment failure, enhancing both environmental and economic sustainability. In contrast to earlier models focused on efficiency, these GAI applications are tailored to Industry 5.0’s inclusive goals, including workforce training, ethical fairness, and community engagement. For example, GAI-powered community engagement platforms integrate societal feedback into product design, preserving cultural values and enhancing social legitimacy.

The old model of industrial growth no longer meets the demands of a world facing social, environmental, and ethical disruption. Industry 5.0 has emerged in response, promising to restore human-centricity and ecological balance to production systems. Now, generative artificial intelligence (GAI) is being hailed as the key technological catalyst to make that vision a reality.

A groundbreaking study "Generative Artificial Intelligence in Adaptive Social Manufacturing: A Pathway to Achieving Industry 5.0 Sustainability Goals" published in the journal Processes lays out a detailed blueprint for how generative artificial intelligence (GAI) can transform manufacturing into a human-centric, sustainable enterprise. Authored by researchers at Tabriz Islamic Art University, the study combines content analysis, expert validation, and system dynamics modeling to map the influence of 17 GAI functions on nine dimensions of Industry 5.0 sustainability, from environmental efficiency to social inclusion and ethical governance.

While Industry 4.0 focused on automation and digitization, Industry 5.0 prioritizes a deeper integration of human values, environmental responsibility, and technological adaptability. This paper positions GAI not merely as a technical innovation, but as a systemic enabler of inclusive, regenerative, and responsive production ecosystems.

What role does GAI play in transitioning to sustainable industrial models?

The study shows that GAI transforms the manufacturing landscape by enabling adaptive social manufacturing - a decentralized, networked system that promotes co-creation between producers, consumers, and intelligent technologies. Through real-time data processing, automated decision-making, and generative design, GAI facilitates resource optimization, energy savings, and circular economy practices.

Tools such as predictive maintenance and resource optimization algorithms are shown to reduce energy usage and prevent equipment failure, enhancing both environmental and economic sustainability. In contrast to earlier models focused on efficiency, these GAI applications are tailored to Industry 5.0’s inclusive goals, including workforce training, ethical fairness, and community engagement. For example, GAI-powered community engagement platforms integrate societal feedback into product design, preserving cultural values and enhancing social legitimacy.

The study identifies 17 distinct GAI functions with high sustainability impact. Among the top performers are Dynamic Supply Chain Modeling (DSCM), Bias Monitoring Tools (BMTs), Ergonomic Solutions (ESs), and Cyber-Physical System Integration (CPSI). These tools not only reduce environmental impact but also increase transparency, human well-being, and organizational resilience. Unlike static automation, GAI fosters continuous adaptation, enabling firms to respond to supply chain shocks, labor shortages, and climate risks.

How were the impacts of GAI on sustainability measured and validated?

Researchers adopted a multi-method research design that included content analysis of 104 peer-reviewed articles, structured focus group discussions with 130 industrial experts, and a series of statistical analyses including MANOVA and the Friedman test. From this rigorous process emerged nine validated sustainability dimensions: environmental, economic, social, ethical, technological, cultural, human, supply chain, and managerial sustainability.

Each of the 17 GAI functions was evaluated for its potential contribution to these domains. For instance, BMTs scored highest in ethical and managerial sustainability, highlighting their importance in ensuring fairness and algorithmic transparency. DSCM ranked highest in supply chain and environmental sustainability, reinforcing its value in maintaining agility and reducing ecological footprint. ESs and WETPs supported human and social sustainability by designing healthier workplaces and tailoring skill development platforms.

The statistical analysis showed strong correlations between GAI functions and sustainability goals. MANOVA results revealed significant differences in how GAI applications influence various dimensions. Functions like ROA and CPSI demonstrated broad utility across managerial, environmental, and economic domains. Meanwhile, more specialized tools like Safety Simulation Systems (SSS) had concentrated impacts on worker safety and accident prevention.

Normality tests further confirmed the robustness of these assessments, and Cohen’s Kappa values ranging from 0.625 to 0.733 validated the consistency of expert consensus. Together, these findings provide a methodologically sound foundation for strategic prioritization of GAI functions in real-world manufacturing contexts.

What systemic framework did the study propose for operationalizing GAI in Industry 5.0?

Beyond statistical rankings, the study introduced a system dynamics-based Causal Loop Diagram (CLD) validated by 48 selected experts with backgrounds in AI, policy, and sustainable manufacturing. The model mapped the reinforcing and balancing loops among the 17 GAI functions and their influence on the sustainability dimensions.

Ten reinforcing loops and five balancing loops emerged, illustrating the systemic interplay between GAI adoption and sustainability outcomes. For instance, DSCM’s contribution to environmental and managerial sustainability intensified when paired with AIDSSs and Transparent Decision Frameworks (TDFs), while BMTs acted as critical nodes in ensuring equity and reducing ethical risks. The model also highlighted how lower-ranked but strategically positioned tools like Innovative Business Models (IBMs) and Customized Product Designs (CPDs) play vital integrative roles within the system.

Temporal dynamics revealed in the CLD offer key insights: some GAI benefits, like predictive maintenance and material optimization, produce short-term gains, while others, such as sustainable materials development and community engagement, generate long-term impact through cumulative feedback loops. This holistic framework guides not only what technologies to adopt, but how and when to implement them for maximum impact.

Moreover, the CLD emphasized relational dependencies. For example, functions like Scenario Planning Tools (SPTs), though low in standalone impact, emerged as strategic enablers when combined with ROAs and AIDSSs, especially under conditions of market volatility or regulatory change.

The framework’s systemic design supports both macro-level strategy and micro-level operations. It addresses adaptive capabilities, workforce integration, ethical governance, and organizational agility, offering a roadmap for managing the transition from traditional production to human-centered, sustainable systems.