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Against the backdrop of deepening digital transformation, the integration of artificial intelligence (AI) technology with enterprise operations is becoming increasingly close. However, the mechanism through which AI impacts enterprise performance still lacks systematic quantitative analysis. Based on the logical framework of “Technology Input - Scenario Application - Performance Output”, this paper systematically sorts out the action paths of AI technology in three core dimensions of enterprises: cost control, revenue growth, and efficiency improvement. It innovatively constructs a simple mathematical model to quantify the correlation between AI input and performance improvement, and verifies the model’s effectiveness using empirical data from 10 enterprises across three industries (retail, manufacturing, and finance). The study finds that a reasonable AI investment structure — with technology procurement accounting for 40%–50% and talent development accounting for 25%–30% — can boost enterprises’ comprehensive performance by 15%–25%. Additionally, the impact coefficient of scenario adaptability on performance output reaches 0.72, providing quantitative references for enterprises to formulate AI investment strategies.

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Mechanism and Quantitative Study on Artificial Intelligence Technology Empowering Enterprise Performance—Empirical Analysis Based on the “Input-Scenario-Output” Model

  • 会议论文

  • 发表日期：

    2026年

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  • Depeng Pan,
  • Shaozheng Guo,
  • Qian Xiong,
  • Xianpeng Wang,
  • Tatul Manaseryan

摘要

Against the backdrop of deepening digital transformation, the integration of artificial intelligence (AI) technology with enterprise operations is becoming increasingly close. However, the mechanism through which AI impacts enterprise performance still lacks systematic quantitative analysis. Based on the logical framework of “Technology Input - Scenario Application - Performance Output”, this paper systematically sorts out the action paths of AI technology in three core dimensions of enterprises: cost control, revenue growth, and efficiency improvement. It innovatively constructs a simple mathematical model to quantify the correlation between AI input and performance improvement, and verifies the model’s effectiveness using empirical data from 10 enterprises across three industries (retail, manufacturing, and finance). The study finds that a reasonable AI investment structure — with technology procurement accounting for 40%–50% and talent development accounting for 25%–30% — can boost enterprises’ comprehensive performance by 15%–25%. Additionally, the impact coefficient of scenario adaptability on performance output reaches 0.72, providing quantitative references for enterprises to formulate AI investment strategies.