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| Format: | Preprint |
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2025
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| Online-Zugang: | https://arxiv.org/abs/2503.05770 |
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| _version_ | 1866915187451232256 |
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| author | Storey, Veda C. Yue, Wei Thoo Zhao, J. Leon Lukyanenko, Roman |
| author_facet | Storey, Veda C. Yue, Wei Thoo Zhao, J. Leon Lukyanenko, Roman |
| contents | The continuing, explosive developments in generative artificial intelligence (GenAI), built on large language models and related algorithms, has led to much excitement and speculation about the potential impact of this new technology. Claims include AI being poised to revolutionize business and society and dramatically change personal life. However, it remains unclear exactly how this technology, with its significantly distinct features from past AI technologies, has transformative potential. Nor is it clear how researchers in information systems (IS) should respond. In this paper, we consider the evolving and emerging trends of AI in order to examine its present and predict its future impacts. Many existing papers on GenAI are either too technical for most IS researchers or lack the depth needed to appreciate the potential impacts of GenAI. We, therefore, attempt to bridge the technical and organizational communities of GenAI from a system-oriented sociotechnical perspective. Specifically, we explore the unique features of GenAI, which are rooted in the continued change from symbolism to connectionism, and the deep systemic and inherent properties of human-AI ecosystems. We retrace the evolution of AI that proceeded the level of adoption, adaption, and use found today, in order to propose future research on various impacts of GenAI in both business and society within the context of information systems research. Our efforts are intended to contribute to the creation of a well-structured research agenda in the IS community to support innovative strategies and operations enabled by this new wave of AI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_05770 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative Artificial Intelligence: Evolving Technology, Growing Societal Impact, and Opportunities for Information Systems Research Storey, Veda C. Yue, Wei Thoo Zhao, J. Leon Lukyanenko, Roman Computers and Society Artificial Intelligence Machine Learning The continuing, explosive developments in generative artificial intelligence (GenAI), built on large language models and related algorithms, has led to much excitement and speculation about the potential impact of this new technology. Claims include AI being poised to revolutionize business and society and dramatically change personal life. However, it remains unclear exactly how this technology, with its significantly distinct features from past AI technologies, has transformative potential. Nor is it clear how researchers in information systems (IS) should respond. In this paper, we consider the evolving and emerging trends of AI in order to examine its present and predict its future impacts. Many existing papers on GenAI are either too technical for most IS researchers or lack the depth needed to appreciate the potential impacts of GenAI. We, therefore, attempt to bridge the technical and organizational communities of GenAI from a system-oriented sociotechnical perspective. Specifically, we explore the unique features of GenAI, which are rooted in the continued change from symbolism to connectionism, and the deep systemic and inherent properties of human-AI ecosystems. We retrace the evolution of AI that proceeded the level of adoption, adaption, and use found today, in order to propose future research on various impacts of GenAI in both business and society within the context of information systems research. Our efforts are intended to contribute to the creation of a well-structured research agenda in the IS community to support innovative strategies and operations enabled by this new wave of AI. |
| title | Generative Artificial Intelligence: Evolving Technology, Growing Societal Impact, and Opportunities for Information Systems Research |
| topic | Computers and Society Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2503.05770 |