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AI can help construction project managers monitor progress, review project documents, analyse schedules, and inform resource and risk decisions—but it is not yet widely embedded across the industry. The strongest case is for carefully supervised help with data-heavy, repeatable workflows, not handing project accountability to a model.
How widely is AI used in construction project management?
Adoption remains limited, according to the Royal Institution of Chartered Surveyors (RICS) Artificial intelligence in construction report 2025. The report draws on more than 2,200 responses to the Q1 2025 Global Construction Monitor. In those survey responses, 45% said their organisation had no AI implementation, while 34% said it was in an early pilot phase. Just under 12% reported regular use in specific processes, 1.5% use across multiple processes, and less than 1% said AI was embedded organisation-wide.
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These are respondents’ reports, not an audited census of construction firms. They indicate that pilots and limited applications are more common than broad deployment; they do not establish how many projects use AI on site or how well any particular system performs.
Which project-management workflows can AI support?
RICS respondents rated several data-heavy functions as areas where AI could have high positive significance. The figures below are perceptions of potential, not measured improvements in productivity, cost, safety, or schedule performance.
#1 Best Overall
| Workflow | RICS respondents rating it as high-potential | Possible project-management assistance |
|---|---|---|
| Progress monitoring | 36% | Help compare project updates with plans or surface changes that warrant a manager’s review. |
| Project scheduling | 36% | Help analyse schedule information and flag patterns or tasks for follow-up. |
| Resource optimisation | 30% | Inform consideration of resource needs when records are sufficiently complete and consistent. |
| Contract and project-document review | 30% | Support search, classification, or summarisation across project records. |
| Risk management | 29% | Help surface information that a project professional can assess against known risks. |
All percentages are from RICS’s 2025 report and represent respondents’ ratings of potential significance. They are not estimates of the share of firms using these tools or proof of outcomes on a live project.
Schedules and progress updates
AI-assisted analysis may help teams work through recurring updates, compare reported progress with planned work, or identify discrepancies to investigate. A flagged variance is a prompt to check the underlying records and site conditions, not proof that the schedule is wrong or that a task will miss its target.
Contracts and project records
Search and summarisation can make large document sets easier to navigate. The project manager still needs to verify any extracted term or summary against the governing document, including its current version and relevant context. A summary should not be treated as the contract itself or as a substitute for appropriate professional review.
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Analytical tools may help inform forecasts or resource decisions when project inputs are usable. Incomplete, inconsistent, or disconnected records can make an apparently precise output misleading. Keep it within the normal commercial review process rather than treating a model’s estimate as an approved cost or resource decision.
Risk, safety, and coordination
Risk management is among the potential uses identified by RICS, but a risk flag is not a safety determination. Safety-related decisions need accountable human review against applicable site procedures and project information. A 2025 conference-paper overview also discusses construction applications involving building information modelling (BIM), including visualisation, clash detection, and coordination. That overview illustrates possible applications; it does not independently validate the accuracy or performance of particular products.
Can AI keep a construction project on schedule or budget?
It may support work that contributes to schedule and cost control, but the available survey findings do not prove that it will keep a project on time or within budget. RICS respondents rated progress monitoring and scheduling among the most promising functions, but those ratings measure perceived potential rather than observed project results.
A separate Project Management Institute (PMI) research summary, published in 2024, describes 500 project professionals around the world who were already using generative AI in project work. It reports that high adopters compared with low adopters more often reported improvements in scheduling (85% versus 46%), cost management (85% versus 42%), and quality management (91% versus 40%). These are self-reported comparisons within an existing group of GenAI users. They do not show that AI caused the improvements, and they are not construction-specific evidence.
In practice, an AI-assisted forecast is only as dependable as its inputs, assumptions, and review. A project team should assess it against current project records and established controls before acting on it.
What makes adoption difficult?
Adoption and organisational readiness are separate challenges. In the RICS 2025 report, 45% of respondents said their organisation had limited AI capability and was exploring the technology; another 29% reported no capability or plans. RICS also identifies skills gaps, fragmented or inconsistent data, integration challenges, implementation costs, uncertain returns, and limited standards as obstacles.
Workforce attitudes show both interest and unease. In the RICS Q2 2025 skills survey, as reported in the 2025 report, 69% of project managers and 67% of quantity surveying and construction professionals agreed that AI would help surveyors deliver greater value in the future. At the same time, 44% and 38%, respectively, expressed concern about AI’s impact on their own role, while 48% and 41% felt overwhelmed by the pace of technological change. These are survey responses, not predictions of job losses or proof that specific roles will change in a particular way.
Before a tool can help, teams need to know which project records it can access, whether those records are current and consistent, and how its outputs fit into existing systems and approval paths. Integration and data preparation are part of the implementation, not housekeeping that can safely be ignored.
How should a construction team evaluate an AI pilot?
A narrow pilot makes it easier to tell whether a tool helps with a real project task. RICS recommends demonstration projects and clearer benchmarks. Use that principle to define the test before introducing AI into a live workflow.
Best Value
- Choose one repeatable task. Define a specific workflow, such as finding information in a controlled set of project documents or reviewing routine progress updates. Avoid a broad objective like “use AI to improve the project.”
- Set a baseline and a measurable criterion. Record how the task is handled now and decide what result would count as useful. Select a measure suited to the task, such as review time or the number of outputs requiring correction; do not assume an improvement before measuring it.
- Check data access and quality. Confirm that the inputs are current, sufficiently complete, and consistent, and that the tool is permitted to access them. Consider how the workflow will cope with missing, conflicting, or outdated records.
- Name an accountable reviewer. Assign a qualified person to check outputs against project records and decide whether to use them. Keep safety-critical and contractually binding decisions with the appropriate professionals and approval processes.
- Document the process and assess the result. Make staff guidance clear about when AI is being used, what must be reviewed, and how corrections are handled. Compare pilot results with the baseline before deciding whether to continue or expand the use.
For any candidate tool, assess its fit with existing project records and systems, data requirements, auditability, approval controls, security and permissions, and implementation effort. Evidence of value on a comparable workflow matters more than a general claim that a system uses AI.
What governance should be in place?
RICS recommends clear internal policies, practical staff guidance or standards for responsible and transparent use, and targeted demonstrations with clearer benchmarks. For a project team, those principles mean making AI use visible and defining where human review is required.
Quick Recap
- Specify which workflows may use AI and what information staff may provide to a tool.
- Require checks against authoritative project records, contract versions, and relevant procedures before an output informs action.
- Keep accountable project professionals responsible for decisions; an AI-generated answer does not transfer that responsibility.
- Record enough about the use and review of outputs to support the project’s normal oversight and approval processes.
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