kaz29Вестник Казахстанско-Британского технического университетаHerald of the Kazakh-British Technical University1998-66882959-8109Казахстанско-Британский Технический Университет10.55452/1998-6688-2026-23-3-627-642kaz29-3222Research ArticleЭКОНОМИКА И БИЗНЕСECONOMY AND BUSINESSДВИЖУЩИЕ СИЛЫ И БАРЬЕРЫ ВНЕДРЕНИЯ ПРАКТИК ИСКУССТВЕННОГО ИНТЕЛЛЕКТА НА РАЗВИВАЮЩИХСЯ РЫНКАХDRIVERS AND BARRIERS TO ADOPTING ARTIFICIAL INTELLIGENCE PRACTICES IN MANAGING PROJECTS: A SYSTEMATIC LITERATURE REVIEW THROUGH THE TOE FRAMEWORKhttps://orcid.org/0009-0009-2917-5772ДемеуханА. Б.DemeukhanA. B.

Магистр

Алматы

M.e.s.

Almaty

aididardemeukhan@gmail.comhttps://orcid.org/0009-0007-2312-7775ЖумабаеваА.ZhumabayevaA.

Системный аналитик

Алматы

System Analyst

Almaty

amina.zhumabayeva1@gmail.comКазахстанско-Британский технический университетКазахстанKazakh British Technical UniversityKazakhstanNetcracker TechnologyКазахстанNetcracker TechnologyKazakhstan202628092026233627642Copyright © Демеухан А.Б., Жумабаева А., 20262026Демеухан А.Б., Жумабаева А.Demeukhan A.B., Zhumabayeva A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.kbtu.edu.kz/jour/article/view/3222

Данное исследование рассматривает основные драйверы и барьеры, влияющие на внедрение искусственного интеллекта (ИИ) в управление проектами, с особым акцентом на условия развивающихся рынков. Исследование основано на систематическом обзоре литературы, проведенном в соответствии с руководством PRISMA 2020. Итоговая выборка включала 41 рецензируемую статью, полученную из базы данных Scopus за период 2016–2025 гг. Выявленные факторы внедрения были закодированы и сгруппированы с использованием модели Technology–Organisation–Environment (TOE). В результате была определена 21 категория движущихся сил и 20 категорий барьеров. Результаты исследования показывают, что внедрение ИИ в управление проектами не ограничивается наличием передовых инструментов. Организации часто проявляют интерес к ИИ из-за его потенциала для повышения эффективности проектов, однако практическое внедрение сдерживается слабой инфраструктурой обработки данных, недостаточной технической зрелостью, нехваткой квалифицированных специалистов и неясными нормативными условиями. Этот разрыв особенно заметен на развивающихся рынках, где такие факторы окружающей среды, как цифровая инфраструктура, регулирование, стандарты и институциональная поддержка, часто определяют, возможно ли вообще внедрение на уровне компаний. В исследовании утверждается, что стратегии внедрения ИИ должны быть адаптированы к местным условиям. Для развивающихся рынков это означает, что организационные усилия должны начинаться не только с обучения на уровне фирмы или внедрения программного обеспечения, но и с разработки базовых цифровых, институциональных условий, которые делают возможным устойчивое внедрение ИИ.

This study examines the main drivers and barriers influencing the adoption of artificial intelligence (AI) in project management, with a specific focus on emerging market conditions. The research is based on a systematic literature review conducted in accordance with the PRISMA 2020 guidelines. The final sample included 41 peer-reviewed articles and conference papers retrieved from the Scopus database for the period 2016–2025. The identified adoption factors were coded and grouped using the Technology–Organisation–Environment (TOE) framework. As a result, twenty-one driver categories and twenty barrier categories were identified. The findings show that AI adoption in project management is not limited to the availability of advanced tools. Organisations are often interested in AI because of its potential to improve project performance, yet practical implementation is constrained by weak data infrastructure, limited technical maturity, shortage of qualified specialists, and unclear regulatory conditions. This gap is particularly visible in emerging markets, where environmental factors such as digital infrastructure, regulation, standards, and institutional support often determine whether firm-level adoption is possible at all. The study argues that AI adoption strategies should therefore be adapted to the maturity of the local context. For emerging markets, this means that policy and organisational efforts should not begin only with firm-level training or software implementation, but also with the development of basic digital, regulatory, and institutional conditions that make sustainable AI adoption feasible.

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