Roope Nyqvist, Antti Peltokorpi, Rita Lavikka, Antti Ainamo
Purpose This research paper aims to understand the development and impact of artificial intelligence (AI)-driven digital platform business models within the construction industry, addressing their strengths, weaknesses and changes from previous models. Design/methodology/approach The research uses a three-phase qualitative multiple-case study approach, analyzing three companies developing AI-driven digital platforms. Data was gathered from multiple sources within cases, including interviews, group meetings, workshops and company documentation. The analysis used thematic interpretation of qualitative data and the application of business model canvases for within-case structuring and cross-case synthesis. Findings Results reveal that AI-driven platforms have potential to enhance collaboration, efficiency and scalability through data-driven, personalized services. Platforms drive new business models and leverage benefits for ecosystem stakeholders. However, adoption faces barriers including high implementation costs, integration complexities, data risks, resistance to change and gaps in stakeholders’ maturity. Overcoming the barriers will require strategic planning, training, data governance frameworks,\ and tailored stakeholder engagement. Research limitations/implications The study acknowledges limitations related to participant bias, partly virtual data collection and regional focus. Future research should expand the sample size and conduct longitudinal studies. Practical implications The research provides guidance for companies, emphasizing the importance of operational redesign, cultural alignment and training, data governance, collaborative development and fostering ecosystem partnerships. Originality/value This study contributes to the limited body of empirical research on AI-driven digital platforms in construction, offering insights for companies seeking to leverage platform-based business models in their business.