This study aimed to understand the sentiments and acceptance of Generative AI (GenAI) for digital cost management in UK construction businesses by investigating the ethical, technical, market entry, and operational requirements of GenAI in this context. Using a qualitative approach that covered various themes, a multiple case study research strategy was employed, involving micro, small, and large organisations. Sentiment analysis, a branch of natural language processing, was utilised to analyse interview findings, providing insights into participants' emotional undertones and opinions. The study involved four case studies with nine participants from micro, small, large, and academic organisations. These participants provided insights into the ethical considerations, regulations, maintenance, and operations of a GenAI platform for digital cost management. The findings were presented descriptively across four themes: ethics, market entry, technical operations, and operations. The study found a need for a balanced approach to ethics, emphasising transparency and regulatory compliance. Market entry, adaptability, regulatory compliance, and affordability were identified as key factors influencing the adoption of GenAI tools. The technical operations theme revealed a positive sentiment towards the operational benefits of GenAI, such as improved efficiency and decision-making, but also emphasised the need for professional oversight. Operational challenges included workforce training and quality assurance. The implications of these findings are significant for the adoption of BIM and GenAI in the construction sector, especially among SMEs. The integration of these technologies promises to revolutionise operations, offering enhanced efficiency and collaboration. However, challenges such as perceived complexity, initial investment, and the need for skilled personnel must be addressed. The study suggests that overcoming these barriers requires a concerted effort from industry stakeholders, policymakers, and academia to ensure the effective adoption and implementation of BIM and GenAI in the construction industry.