Industrial automation has traditionally been driven by performance optimization, cost reduction, and productivity gains. However, the growing emphasis on sustainability, regulatory compliance, and equitable practices has highlighted the limitations of conventional automation strategies that often prioritize efficiency at the expense of transparency and fairness. The rise of lightweight artificial intelligence (AI) models provides an opportunity to design sustainable automation pipelines that not only maintain high levels of operational efficiency but also integrate ethical and regulatory considerations into decision-making processes. This study examines the role of lightweight AI in powering automation pipelines capable of adapting to diverse industrial contexts without imposing excessive computational or energy burdens. By reducing model complexity and resource consumption, lightweight AI supports sustainable operations while enabling real-time analytics and decision-making in resource-constrained environments. The proposed pipelines embed transparency mechanisms such as explainable AI, which allow stakeholders to interpret model outputs, ensuring accountability across the value chain. Furthermore, by integrating compliance protocols and ethical safeguards, these systems address legal requirements and societal expectations for fairness, particularly in labor management, environmental impact, and equitable stakeholder outcomes. Case examples from manufacturing, logistics, and energy sectors illustrate how lightweight AI–driven automation can balance efficiency with social and regulatory responsibility. The study concludes that embedding sustainability, transparency, compliance, and equity into automation design is critical for advancing next-generation industrial ecosystems. Such approaches not only optimize operational performance but also enhance trust, resilience, and legitimacy in increasingly data-driven industries.