The Tool Desk
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AI can help construction project managers find patterns in project data, track progress, review documents and support planning. It is not yet widely embedded across the sector, and survey findings about potential benefits are not proof that a particular tool will improve a project. Treat AI as decision support: its usefulness depends on reliable, permissioned information and accountable human review.
How widely is AI being used in construction?
Adoption remains limited, according to the Royal Institution of Chartered Surveyors (RICS) Artificial intelligence in construction report 2025. Its findings draw on more than 2,200 responses to the Q1 2025 Global Construction Monitor; they describe survey respondents, not an audited census of construction firms.
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| Reported level of use | RICS survey result |
|---|---|
| No AI implementation in the organisation | 45% of respondents (RICS, 2025) |
| Early pilot phase | 34% of respondents (RICS, 2025) |
| Regular use in specific processes | Just under 12% of respondents (RICS, 2025) |
| Use across multiple processes | 1.5% of respondents (RICS, 2025) |
| Embedded organisation-wide use | Less than 1% of respondents (RICS, 2025) |
So AI is appearing in pilots and selected workflows, but widespread integration is not the norm in the RICS findings. The report also identifies a readiness gap: 45% of respondents said their organisations had limited capability and were exploring AI, while 29% reported no capability or plans (RICS, 2025).
Which project-management workflows could AI support?
RICS asked respondents where AI could have high positive significance. The percentages below are perceptions of potential, not measured improvements in project outcomes.
#1 Best Overall
| Workflow | Respondents rating it as high potential |
|---|---|
| Progress monitoring | 36% (RICS, 2025) |
| Project scheduling | 36% (RICS, 2025) |
| Resource optimisation | 30% (RICS, 2025) |
| Reviewing contracts and project documents | 30% (RICS, 2025) |
| Risk management | 29% (RICS, 2025) |
Schedule and progress
AI-assisted analysis may help teams compare planned and reported progress, identify changes in updates, or flag tasks that need a closer look. The project manager still needs to check source records, confirm whether the information is current, and decide what action to take. RICS respondents rated scheduling and progress monitoring among the most promising uses; that rating does not establish the accuracy of any system on a specific project.
Project documents
Search, classification and summarisation can help teams navigate large sets of contracts and project records. A generated summary is a starting point, not a substitute for the governing document: check the relevant clause, version and context before relying on it, especially where an interpretation could affect scope, payment or obligations.
Costs and resources
Analytical tools may assist with forecasts and resource decisions when project records are sufficiently complete and consistent. Use their outputs to inform a manager’s review, not to bypass commercial controls, approvals or the underlying cost records. RICS identifies fragmented data and implementation costs among the barriers that can limit this work.
Risk, safety and coordination
Risk management is another area respondents identified as having potential. A system might help surface patterns or items for review, but the evidence cited here does not establish that AI prevents incidents. Keep safety decisions under accountable professional oversight and apply the project’s safety rules and procedures.
A 2025 conference paper from the International Symposium on Automation and Robotics in Construction (ISARC) discusses construction applications including BIM-related visualisation, clash detection and coordination. It is an overview of applications, not independent validation of the performance of current products.
Can AI keep a construction project on schedule and budget?
It may help teams notice information relevant to schedule and cost management, but the available figures do not prove that AI causes projects to finish on time or within budget. RICS’s construction-sector results measure respondents’ expectations of potential, not observed delivery improvements.
Project Management Institute (PMI) research published in 2024 offers a separate, cross-sector comparison. It surveyed 500 project professionals who were already using generative AI in project work. Among these respondents, high adopters versus low adopters reported scheduling improvements at 85% versus 46%, cost-management improvements at 85% versus 42%, and quality-management improvements at 91% versus 40%. These are self-reported comparisons, not causal estimates, and the sample is not construction-specific. They should not be read as a forecast for a construction project or as proof that adopting a tool will produce those results.
What makes implementation difficult?
Adopting AI involves more than choosing a model or software feature. RICS identifies constraints that affect whether a tool can be integrated and used responsibly:
Best Value
- Data quality and access: fragmented or inconsistent records can undermine analysis; inputs also need to be current and permissioned.
- Integration: tools must work with the project’s existing records and systems rather than creating an unreliable parallel source of information.
- Skills and support: staff need practical guidance on what a tool can and cannot do, along with time and organisational support to use it appropriately.
- Cost and uncertain return: implementation requires effort, and expected value may not be established in advance.
- Standards and accountability: unclear expectations make it harder to assess outputs, document use and determine who is responsible for decisions.
Workforce views also show both interest and unease. In the RICS Q2 2025 skills survey, reported in its 2025 AI 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. Concern about AI’s impact on their own role was reported by 44% and 38%, respectively; 48% and 41%, respectively, said they felt overwhelmed by the pace of technological change.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should a construction team pilot AI?
RICS recommends clear internal policies, practical staff guidance for responsible and transparent use, and targeted demonstrations with clearer benchmarks. A disciplined pilot turns those principles into a test the team can evaluate.
- Choose one repeated workflow. Define a narrow task, such as identifying progress updates that need review or locating relevant project-document passages. Avoid starting with a broad promise to automate project management.
- Set a baseline and evaluation criterion. Record how the task is handled now and decide in advance what result would count as useful. Evaluate the selected workflow rather than relying on general claims about AI.
- Check the inputs. Confirm that records are current, sufficiently complete, consistent, and accessible to the people and tool involved. Resolve permissions and integration questions before putting sensitive project information into a system.
- Name the accountable reviewer. Specify who checks each output against project records, contract requirements and applicable safety rules, and who has authority to act on it.
- Document use and limits. Give staff practical guidance on acceptable uses, required checks and decisions that must remain with qualified project professionals. Keep an auditable record where the workflow requires one.
- Review the result before expanding. Compare the pilot with its baseline and criterion. If the output is unreliable, difficult to verify or not worth the implementation effort, revise the workflow or stop rather than expanding on assumption.
What should remain a human decision?
AI can organize information and suggest items for attention; it does not take responsibility for the project. Keep professional judgement and established approval processes in charge of decisions that are safety-critical, contractually binding, or commercially consequential. Verify material outputs against authoritative project records, and make clear to staff when and how AI is being used.
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