**The UK construction cost rose by 15% in 2022** and due to the rising inflation rate of 6.7%, it is at almost 20% in 2023 (BuildPartner, 2023). This rising cost demands efficiency in predicting construction costs to avoid cost overruns, rework, improved safety, errors, and increased productivity. Predicting construction costs from the beginning of a project has been a daunting challenge for construction cost planners. The inept approach of limited AI application in cost estimation has led to **over 69% of construction projects** experiencing more than 10% cost overruns (KPMG, 2020). The increase in construction material prices has drastically impacted UK construction companies' revenue growth and productivity. Generative Artificial Intelligence (GAI) presents an opportunity to spur the uptake of Building Information Modelling (BIM) in construction cost management. The application of BIM to ensure productivity in the construction sector has predominately focused on other aspects such as design, energy efficiency and scheduling (Nikologianni et al., 2022). Although construction cost management through 5D BIM has been in recent demand, the diffusion of associated software applications and processes amongst older professionals has been challenging. This is predicated on the requirement for yearly skill updates to meet the recent technological changes. GAI through large language models (LLM) and prompting has the potential to ease the cost of producing BIM cost-related Master Information Delivery Plan (MIDP) and Task Information Delivery (TIDP) documents. Furthermore, recent feasibility studies revealed that cost prediction of construction projects is feasible through GAI through historical and live data. T**he fusion of GAI and BIM** can potentially reduce the challenge of upskilling new construction professionals, reducing man-hours required to produce construction cost documents, ensuring accuracy and enhancing the competitive advantage of SMEs and large companies. This proposed study will apply the feasibility study to develop the BIM-GAIcost web application to produce construction cost estimation documents through LLM of cost data and support the automation of BIM cost-related documents with accuracy indications to predict and avoid cost overruns in the planning phase. The UK BIM framework and standards would align with the document's early cost advice documentation using the Building Execution Plan (BEP) structure. The web-based GAI for BIMcost application (BIM-GAIcost) would produce a user interface (UI) with prompt option parameters for BIM cost planning. A commercial interface to user experience (UX) to ensure tailored accessibility for different organisation scales and continuous improvement would be developed to ensure productivity in construction cost forecasting.

The application of Generative Artificial Intelligence (GAI) can enhance productivity in cost management in construction SMEs by automating estimating cost plans, measured quantities, and predicting valuation and cashflows outcomes based on the designs, specifications, site information, and other non-graphical input variables. Currently, the UK BIM framework and 2011 Mandate have been ineffective in supporting SMEs' productivity using BIM with only 57% uptake (UKBIMA, 2021). Thus, evidencing a shortfall in the productivity of UK construction SMEs. BIM update has been limited in UK construction SMEs mainly because of cost and ease of use. Recent AI and machine learning developments present new opportunities to make BIM easier to apply in UK construction SMEs. GAI algorithms can potentially reduce errors and rework synonymous with a cost overrun in construction estimating and budgeting processes through deep learning-based generative models such as rule-based generative models within BIM through automation. The time required in producing bills of quantities, cost plans, valuation, final accounts and associated textual documents can be reduced significantly with a full version of BIM-enabled GAI. Construction cost management processes in the UK have been critiqued for errors in estimation and budgeting. Large-scale cost overruns in infrastructure projects have stifled the growth of SME construction companies in the UK. Cost management errors have led to rework and claims, impeding organisations' productive outputs. Furthermore, 69% of construction works experience cost overruns in the UK, creating productivity difficulties in construction SMEs (KPMG, 2023). Consequently, GAI has not been implemented in the construction management process to optimise productivity and mitigate incidents of errors and overruns. Hence, construction cost management needs to advance towards the new models of productivity improvement in the planning and construction stages. The application of GAI in the construction cost management process of SME companies in the UK will empower productivity through BIM. This feasibility study will study the pilot integration of BIM and GAI, datasets, ethical considerations, regulations, interfaces, GAI requirements user interface (UI) and user experience (UX). Furthermore, GAI has not been applied in BIM cost management-based tools such as BlueBeam, CostX and PlainSwift. **This feasibility study intends to use existing construction cost datasets from an SME construction company to develop a systems architecture, a Foundation large language learning model (FLM) and a pilot web-based integration of BIM and GAI to automate the cost estimation to engender productivity.**