Building technology budgets have evolved rapidly in AI over the past two years. A Dodge Construction Network study done in collaboration with CMiC found that 87% of contractors believe AI will significantly transform their business. More than half of the companies surveyed are already exploring AI through pilot programs and preparing staff for AI-related roles.
Production deployment is a different story. According to a RAND Corporation report based on interviews with 65 data scientists and engineers, more than 80% of AI projects do not make it to production. This failure rate is double the rate of IT projects that do not involve AI. And the Dodge/CMiC Research specifically confirms the gap in construction: while 87% of contractors expect a transformation, only 19% have adapted their workflows to an AI environment.
The reason is rarely the model itself.
Build a single source of truth
Construction is executed with transfers. A project moves from estimate to procurement to field execution to invoicing to close and each deliverable typically involves a system change. Many contractors have built their technology stack incrementally, adding point solutions that solve one team’s problem at a time. It works, until the data has to work together.
The challenge is familiar. Field reports and accounting tell different stories. Schedule percent complete and cost percent complete do not always reconcile. Subcontractor payment data and productivity data live in separate systems. When an AI model enters this environment, opportunity is important, but so is reliance on clean, connected inputs.
This is where pilots often reveal something important. They succeed under controlled conditions because the data scientists carefully prepare the inputs. Scale-to-production exposes the real question: How do you maintain data quality across 40 active projects, every day, without manual intervention? The contractors who get the most out of AI are the ones who solved this problem first.
What sets AI up for success in construction
Three conditions separate AI that is dispatched from AI that is stopped.
The first is a single source of project and financial truth. When accounting, project management, and field data are in a database, the model reads a version of the project. Reconciliation is no longer a prerequisite for analysis.
The second is real-time field input. Construction AI that relies on weekly batch loads from the job site always looks at an outdated image. Models that detect drops in productivity, schedule lags, or cost overruns in time to act on them need daily field data flowing from the same system the foreman already uses.
The third is financial integration that AI can rely on. A forecasting model that considers committed costs but does not change the order exposure will miss the real risk. A risk model that looks at scheduled dates but not the hold or cash position will miss the actual constraint. AI in construction wins when it can see the entire project at once.
Where AI delivers when the foundation is right
The Dodge/CMiC studio found that early adopters are already seeing solid results. Contractors using AI-enabled tools reported 92% effectiveness in automated proposal generation and 86% effectiveness in contract risk review compared to previous methods. Across a broader set of project and business management roles, more than 70% of contractors already using AI-enabled tools found them highly effective.
These results point to two areas with the greatest profitability in the short term. The first is the forecast. Forecasting costs to complete has been a manual exercise for decades, with project managers updating estimates monthly using last month’s burn rate as a baseline. AI working with unified project data can generate committed costs, change order exposure, productivity trends, and schedule position in a continuous forecast that updates as the project moves.
The second is labor productivity. Labor is the largest controllable cost of most projects and the most difficult to read in time to act. AI models trained on daily field data, crew composition, weather and task type can flag productivity dips during the week they occur. A superintendent who sees a productivity problem on Tuesday can reassign teams on Wednesday. A superintendent who sees him three weeks later can only document it.
The base comes first
The construction companies that get production value from AI are not the ones with the most sophisticated models. They are the ones whose project and financial data were already unified before AI entered the conversation. The model is the easy part. The basis is the work.
The CMiC platform manages projects, field operations, accounting and finance in a single database. This structure means AI-enabled tools read a version of the project through cost tracking, scheduling and field reporting without manual reconciliation or data mining. It is the same foundation that underpins the forecasting accuracy and labor productivity gains described above. Learn how CMiC’s leading construction ERP supports AI-ready construction operations.
