Applied Statistics in the Era of Artificial Intelligence: A Review and Vision

ABSTRACT The advent of artificial intelligence (AI) technologies has significantly changed many domains, including applied statistics. This review and vision paper explores the evolving role of applied statistics in the AI era, drawing from our experiences in engineering statistics. We begin by outlining the fundamental concepts and historical developments in applied statistics and tracing the rise of AI technologies. Subsequently, we review traditional areas of applied statistics, using examples from engineering statistics to illustrate key points. We then explore emerging areas in applied statistics, driven by recent technological advancements, highlighting examples from our recent projects. The paper discusses the symbiotic relationship between AI and applied statistics, focusing on how statistical principles can be employed to study the properties of AI models and enhance AI systems. We also examine how AI can advance applied statistics in terms of modeling and analysis. In conclusion, we reflect on the future role of statisticians. Our paper aims to shed light on the transformative impact of AI on applied statistics and inspire further exploration in this dynamic field.

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Publication Details

Journal
Applied Stochastic Models in Business and Industry
Published
2026-02-18
DOI
https://doi.org/10.1002/asmb.70075
Primary Topic
Gaussian Processes and Bayesian Inference
Type
article
Field-Weighted Citation Impact
0.00
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article

Applied Statistics in the Era of Artificial Intelligence: A Review and Vision

Caleb King, XinWei Deng, Jie Min, Yili Hong et al.
Applied Stochastic Models in Business and Industry
Gaussian Processes and Bayesian Inference
article

Applied Statistics in the Era of Artificial Intelligence: A Review and Vision

Caleb King, XinWei Deng, Jie Min, Yili Hong, Simin Zheng, Xinyi Song
article en

Abstract

ABSTRACT The advent of artificial intelligence (AI) technologies has significantly changed many domains, including applied statistics. This review and vision paper explores the evolving role of applied statistics in the AI era, drawing from our experiences in engineering statistics. We begin by outlining the fundamental concepts and historical developments in applied statistics and tracing the rise of AI technologies. Subsequently, we review traditional areas of applied statistics, using examples from engineering statistics to illustrate key points. We then explore emerging areas in applied statistics, driven by recent technological advancements, highlighting examples from our recent projects. The paper discusses the symbiotic relationship between AI and applied statistics, focusing on how statistical principles can be employed to study the properties of AI models and enhance AI systems. We also examine how AI can advance applied statistics in terms of modeling and analysis. In conclusion, we reflect on the future role of statisticians. Our paper aims to shed light on the transformative impact of AI on applied statistics and inspire further exploration in this dynamic field.

Applied Stochastic Models in Business and IndustryVol. 42(2)
SAS Institute (United States) (US), University of South Florida (US), Virginia Tech (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 53%
Gaussian Processes and Bayesian Inference
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Applied Statistics in the Era of Artificial Intelligence: A Review and Vision — Caleb King, XinWei Deng, et al. · Applied Stochastic Models in Business and Industry (2026) | TGRS Research Map | TGRS