caerCentral Asian Economic ReviewCentral Asian Economic Review2789-43982789-4401Университет Нархоз10.52821/2789-4401-2025-4-248-263caer-1564Research ArticleЦИФРОВАЯ ЭКОНОМИКАDIGITAL ECONOMYОценка внедрения искусственного интеллекта в промышленных проектах Казахстана: статистический подходEvlaulating AI implementation in Kazakhstan’s industrial projects: a statistical approachhttps://orcid.org/0000-0001-8201-3638АкыбаеваГ. С.AkybayevaG.

Астана

Akybayeva Gulvira – Ph.D in Economics, Associate Professor

Astana

gulvira.akybaeva@astanait.edu.kzhttps://orcid.org/0009-0008-7622-7793МырзагалиевА. О.MyrzagaliyevA.

Астана

Myrzagaliyev Azat – PhD Candidate

Astana

242614@astanait.edu.kzAstana IT UniversityКазахстанAstana IT UniversityKazakhstan20250211202504248263Copyright © Акыбаева Г.С., Мырзагалиев А.О., 20252025Акыбаева Г.С., Мырзагалиев А.О.Akybayeva G., Myrzagaliyev A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://caer.narxoz.kz/jour/article/view/1564

Цель исследования – Исследование изучает влияние искусственного интеллекта (ИИ) на результаты промышленных проектов в Казахстане посредством анализа сокращения затрат и временной эффективности наряду с эффективностью управления рисками.

Методология – На основе данных, собранных из общедоступных баз данных проектов, отчетов компаний, а также экспертных опросов, были применены статистические методы, включая описательную статистику, регрессионный анализ и сравнительные методы, такие как T-тесты или ANOVA, для оценки показателей 40 промышленных проектов в Казахстане, из которых 20 с использованием ИИ и 20 без использования ИИ из секторов энергетики, производства и инфраструктуры.

Оригинальность / ценность исследования – Данное исследование решает проблему недостаточного статистического анализа влияния ИИ на промышленные проекты в Казахстане, предоставляя количественные доказательства его воздействия на ключевые результаты проектов, такие как стоимость, сроки и контроль рисков. Исследование предлагает ценную информацию для руководителей промышленности и государственных лидеров Казахстана относительно преимуществ и эффективности интеграции ИИ в промышленные проекты для повышения производительности и устойчивого экономического развития.

Результаты исследования – Результаты исследования показывают, что внедрение ИИ в промышленных проектах в Казахстане приводит к сокращению затрат на проекты на 10% и ускорению сроков проектов на 15% по сравнению с традиционными проектами без использования ИИ. Кроме того, внедрение ИИ привело к сокращению рисков проектов на 40% и более высокому показателю успешности проектов – 92% для проектов с использованием ИИ против 85% для проектов без использования ИИ.

Purpose – The research examines how artificial intelligence (AI) affects industrial project outcomes in Kazakhstan by studying cost reduction and time efficiency alongside risk management effectiveness.

Methodology – Based on the data collected from publicly accessible project databases, company reports as well as expert surveys, statistical methods including descriptive statistics, regression analysis, and comparative methods such as T-tests or ANOVA, were employed to evaluate the performance of 40 industrial projects in Kazakhstan, comprising 20 AI-enabled and 20 non-AI projects from the energy, manufacturing, and infrastructure sectors.

Originality / value – This research addresses the insufficient statistical analysis of AI’s impact on industrial projects in Kazakhstan, by providing quantitative evidence of its effects on key project outcomes like cost, timeline, and risk control. This study offers valuable insights for industry executives and government leaders in Kazakhstan regarding the benefits and effectiveness of AI integration in industrial projects for enhanced performance and sustainable economic expansion.

Findings – The study’s findings indicate that the adoption of AI in industrial projects in Kazakhstan leads to a reduction in project costs by 10% and an acceleration of project timeframes by 15%, when compared to traditional non-AI project efforts. Furthermore, AI implementation resulted in a 40% reduction in project risks and a higher project success rate of 92% for AI-enabled projects versus 85% for non-AI projects.

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The authors declare that there are no conflicts of interest present.