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AI can help construction project teams monitor progress, support scheduling, examine resource options, search project documents and surface possible risks. Its role today is decision support and task automation—not an autonomous project manager. A 2025 RICS survey found that adoption was still early, and its figures describe respondents’ reported use and expectations, not proven savings or performance across the construction industry.
What AI can do in construction project management
Construction projects produce schedules, site updates, cost records, contracts, drawings and other information that teams must interpret and act on. AI tools can help process some of that information: for example, finding patterns, summarising records or generating scenarios. Whether an output is useful depends on the task, the quality and currency of the inputs, and a qualified person checking it before action.
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The RICS 2025 report identifies several potential applications across project planning and control. The percentages below are survey respondents’ ratings of where AI could have high positive significance; they are not measured improvements on completed projects.
| Potential application | RICS respondents rating it highly | What it could support |
|---|---|---|
| Progress monitoring | 36% | Processing project updates and identifying changes or patterns for the team to investigate. |
| Scheduling | 36% | Reviewing schedule information and flagging possible deviations or emerging issues. |
| Resource optimisation | 30% | Examining allocation scenarios using available project information. |
| Project-document review | 30% | Searching, summarising or reviewing project records and documents. |
| Risk management | 29% | Surfacing patterns that may warrant further assessment by the project team. |
These figures come from more than 2,200 responses to questions in the RICS Q1 2025 Global Construction Monitor. They represent perceived potential, not a controlled comparison of projects using AI and projects that do not.
Progress monitoring and scheduling
AI may help teams process frequent updates and draw attention to information that could signal a schedule change. A flagged deviation or forecast is a prompt to review, not proof that a task is late or that a particular intervention is needed. The project team still needs to validate field conditions, check the underlying records and apply the project’s schedule logic.
Resource optimisation
Data-driven tools can help compare resource-allocation scenarios. Those comparisons are only as dependable as their inputs: incomplete or out-of-date information can make a plausible-looking option unsuitable. Treat a generated scenario as material for discussion, not an instruction to reassign people, equipment or other resources.
Project and contract documents
AI can support document search, summarisation and review. For any output that may affect scope, obligations, claims or safety, reviewers should be able to trace it to the relevant source material and check the interpretation themselves. A summary is not a substitute for reading and applying the controlling document.
Risk and cost management
Models may help examine project information for patterns or support forecasts, but the result depends on project-specific data and assumptions. The RICS report does not establish a universal accuracy level or guaranteed reduction in cost overruns. Teams should treat predictions as inputs to a decision process and record the assumptions that matter to their assessment.
Design optioneering
Design option exploration is an emerging strategic application. In the RICS 2025 survey, 40% of respondents selected design optioneering as the area where AI could have the greatest impact over the next five years. That is a forecast of professional expectations, not evidence that the impact has already been achieved across projects.
Safety and sustainability
RICS respondents ranked safety and sustainability among the lower expected improvement areas in that survey. This measures relative expectations in the survey; it does not establish that AI is ineffective in either area or that these concerns matter less to project teams.
How widely is AI being adopted?
In the RICS Q1 2025 Global Construction Monitor questions, respondents described adoption at very different stages. The report had more than 2,200 responses; these results are survey findings, not a census of construction organisations.
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|---|---|
| No AI implementation | Approximately 45% |
| Early pilots | 34% |
| Regular use in specific processes | Just under 12% |
| Use across multiple processes | 1.5% |
| Fully embedded organisation-wide use | Less than 1% |
The pattern suggests a gap between interest in possible applications and broad organisational deployment. It does not show how any one company, region or project is adopting AI, nor does it indicate whether a pilot delivered value.
A separate RICS Q2 2025 skills survey of more than 2,000 professionals found that 69% of project managers and 67% of quantity-surveying and construction professionals agreed AI would help surveyors deliver greater value. This is a response about expected value, not a measurement of realised productivity or project outcomes.
What the reported benefits do—and do not—show
There is also encouraging, but limited, survey evidence from project management more broadly. In 2024, the Project Management Institute (PMI) reported results from 500 project professionals already using generative AI. High adopters more often self-reported improvements than low adopters:
| Area | High adopters reporting improvement | Low adopters reporting improvement |
|---|---|---|
| Productivity | 93% | 58% |
| Scheduling | 85% | 46% |
| Cost | 85% | 42% |
| Quality management | 91% | 40% |
These PMI results cover project professionals across industries who were already using generative AI. They are self-reported associations, not proof that AI caused the improvements, and they are not construction-specific effect estimates. They should not be used to promise that a construction project will achieve the same results.
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The RICS 2025 report points to organisational and technical obstacles. Respondents most often identified a lack of skilled personnel, system integration and poor data quality among the leading barriers.
| Reported barrier | Share of RICS respondents identifying it | Practical implication |
|---|---|---|
| Lack of skilled personnel | 46% | Teams need people who can assess both the construction context and the tool’s output. |
| System integration | 37% | Information may be difficult to use if AI tools do not fit the systems and workflows already in place. |
| Poor data quality | 30% | Missing, inconsistent or stale records can undermine the output. |
The same report also identifies cost, unclear return on investment and limited standards or guidance as concerns. The percentages above are findings from the RICS survey, not universal rates for construction companies.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate an AI pilot
A small, clearly bounded pilot is easier to assess than a broad promise to “use AI.” RICS recommendations support a sequence that puts the project decision, data and human responsibilities first.
- Choose one workflow and decision. Select a concrete task, such as schedule reporting or document search, and specify which human decision the tool is meant to support.
- Check the data and systems. Confirm that relevant project records are accessible, sufficiently complete and governed. If information is fragmented, address the common data environment and data standards rather than assuming a model will fix the problem.
- Assign expertise and responsibility. Involve project, digital and operational staff. Train users, define acceptable use and make clear who reviews outputs and who is accountable for acting on them.
- Set a baseline and measures. Agree how the pilot will be evaluated against the existing workflow and project-specific measures. Do not infer likely return on investment from survey optimism.
- Apply safeguards and retain human oversight. Conduct appropriate due diligence and ethical review. Keep qualified people responsible for consequential project decisions.
When comparing possible workflows or tools, assess the task supported, data requirements, system fit, explainability and audit trail, review responsibilities, training effort, measurable value against a baseline, and privacy, security and contractual suitability. A useful pilot should make these factors visible, rather than treating a plausible output as proof of value.
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Risks to manage before relying on AI
- Unreliable inputs: Missing or inconsistent project records can produce misleading outputs; a model cannot reliably compensate for absent or poor-quality information.
- Integration friction: A tool may not fit existing systems or workflows, adding work instead of reducing it.
- Unclear accountability: Teams should know who checks an output, who can act on it and who remains responsible for the resulting decision.
- Overstated evidence: The headline findings discussed here are survey perceptions or self-reported experiences. They do not establish construction-specific causal gains.
- Unverified product claims: The cited evidence does not establish current pricing, integrations, security terms or performance for named vendor products. Assess those details directly for the specific product and project before adoption.
RICS’s 2025 report and recommendations support responsible adoption built on data quality, organisational readiness, due diligence and oversight. They offer a practical basis for evaluation, not proof that any particular implementation method guarantees success.
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