Artificial Intelligence and Business Sustainability: Data-Driven Approaches for Resilient Organizations
AI-Enabled Sustainability - Integrating Data Analytics, Engineering, and Business Innovation
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Beschreibung
Organizations worldwide struggle to translate AI ambition into measurable sustainability outcomes — this volume provides the evidence and frameworks to close that gap. Thirty-six chapters across seven thematic parts connect AI and data-driven methods directly to organizational performance: banking stability, investment efficiency, insurance service quality, logistics optimization, and cybersecurity governance. The approach is distinctive in integrating empirical research with dedicated governance and ethics chapters — responsible AI, environmental footprint, and circular-economy systems — within a single coherent framework. Geographic and sectoral breadth is a further differentiator: contributors draw on evidence from Jordan, Oman, the Gulf, Malaysia, Iraq, and Lebanon, spanning banking, insurance, public administration, engineering, and water utilities. Doctoral students and researchers gain replicable methodologies; executives and policymakers gain practical benchmarks for AI adoption decisions.
Organizations worldwide struggle to translate AI ambition into measurable sustainability outcomes — this volume provides the evidence and frameworks to close that gap. Thirty-six chapters across seven thematic parts connect AI and data-driven methods directly to organizational performance: banking stability, investment efficiency, insurance service quality, logistics optimization, and cybersecurity governance. The approach is distinctive in integrating empirical research with dedicated governance and ethics chapters — responsible AI, environmental footprint, and circular-economy systems — within a single coherent framework. Geographic and sectoral breadth is a further differentiator: contributors draw on evidence from Jordan, Oman, the Gulf, Malaysia, Iraq, and Lebanon, spanning banking, insurance, public administration, engineering, and water utilities. Doctoral students and researchers gain replicable methodologies; executives and policymakers gain practical benchmarks for AI adoption decisions.
Provides frameworks for integrating artificial intelligence and data analytics into business sustainability practices Addresses the critical gap between sustainability aspirations and measurable operational implementation Shows how AI and machine learning transform sustainability from theoretical concept to quantifiable business value
Autor*in
Abdul Razzak Alshehadeh
Themen in »Artificial Intelligence and Business Sustainability: Data-Driven Approaches for Resilient Organizations«
Artificial intelligence for business sustainability Data analytics for ESG performance measurement Machine learning for circular economy Sustainable business model innovation AI-driven environmental optimization Smart cities and IoT sustainability Big data for corporate social responsibility Digital transformation for sustainable development Data-driven sustainability reporting Climate risk financial modeling Sustainable supply chain analytics Green engineering lifecycle assessment Ethical AI governance frameworks Workforce sustainability analytics Predictive models for resource efficiency