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Although substantial research has been conducted on Artificial Intelligence (AI), environmental, social, and governance (ESG) investing, and the rise of tokenized assets individually, there remains a gap in literature which analyzes how these topics and trends overlap and are influence each other. Professionals and scholars alike continue to face a shortage of practically oriented books and content with either technical or purely conceptual writings and content dominating market share.
In Artificial Intelligence, ESG Investments and The Future of Tokenized Assets, award winning researcher and academic Sean Stein Smith addresses this gap, offering a rapid, accessible guide to the intersections and implications of these transformative technologies. Covering topics from AI utilization by financial institutions and professionals for both quantity and quality of data reporting, to the implications of these changes for accounting, finance, and economics professionals needing to contend with an AI-driven tokenized financial landscape, Stein Smith dives into the future of transformative technologies, with examples, resources, and places where readers can learn more will be embedded within every chapter.
Academics and practitioners can discover actionable business intelligences to enable both an understanding and implementation of technologies such as AI, and tokenization across business and service lines.
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Although substantial research has been conducted on Artificial Intelligence (AI), environmental, social, and governance (ESG) investing, and the rise of tokenized assets individually, there remains a gap in literature which analyzes how these topics and trends overlap and are influence each other. Professionals and scholars alike continue to face a shortage of practically oriented books and content with either technical or purely conceptual writings and content dominating market share.
In Artificial Intelligence, ESG Investments and The Future of Tokenized Assets, award winning researcher and academic Sean Stein Smith addresses this gap, offering a rapid, accessible guide to the intersections and implications of these transformative technologies. Covering topics from AI utilization by financial institutions and professionals for both quantity and quality of data reporting, to the implications of these changes for accounting, finance, and economics professionals needing to contend with an AI-driven tokenized financial landscape, Stein Smith dives into the future of transformative technologies, with examples, resources, and places where readers can learn more will be embedded within every chapter.
Academics and practitioners can discover actionable business intelligences to enable both an understanding and implementation of technologies such as AI, and tokenization across business and service lines.