Abstract
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.