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Artificial Intelligence Approaches for Sustainable Digital Innovation

Author(s) Allegra De Filippo
Country Italy
Abstract Artificial intelligence has emerged as a significant technological enabler of sustainable digital innovation by improving the efficiency, adaptability, and intelligence of modern digital systems. Conventional digital innovation approaches often focus primarily on technological advancement without adequately considering resource efficiency, environmental sustainability, social responsibility, and long-term operational resilience. Artificial intelligence can address these limitations through machine learning, predictive analytics, intelligent automation, optimization, and data-driven decision support. This study examines major artificial intelligence approaches that support sustainable digital innovation and evaluates their contribution to resource optimization, operational efficiency, environmental performance, and responsible technological development. A qualitative descriptive research design based on secondary scholarly literature was adopted, and thematic analysis was used to identify recurring technological and sustainability-related themes.
The findings indicate that AI can support sustainable innovation by improving energy management, reducing resource waste, optimizing digital processes, predicting operational requirements, and enabling intelligent decision-making. AI-driven systems can also facilitate smart infrastructure, sustainable manufacturing, intelligent transportation, digital services, and environmentally responsible business processes. However, AI itself can create sustainability challenges because advanced models may require substantial computational resources, energy, data infrastructure, and hardware. Other concerns include data quality, algorithmic bias, transparency, cybersecurity, privacy, and organizational readiness. The study highlights the importance of balancing AI performance with environmental and social considerations. It concludes that sustainable digital innovation requires AI systems to be designed around efficiency, responsible data use, scalability, transparency, and continuous sustainability evaluation.
Keywords Artificial Intelligence, Sustainable Digital Innovation, Machine Learning, Digital Transformation, Sustainability, Predictive Analytics, Intelligent Automation, Green Technology, Resource Optimization, Responsible AI
Field Engineering
Published In Volume 1, Issue 5, September-October 2019
Published On 2019-09-03
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