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AI is beginning to influence how building teams explore design options, review project information and plan work—but it has not yet become routine across the construction sector. In the Royal Institution of Chartered Surveyors’ (RICS) 2025 global survey, about 45% of respondents said their organisations had not implemented AI, while only a small minority reported using it broadly. The clearest near-term opportunity respondents identified was design optioneering: comparing possible approaches early enough to inform planning. That is an expectation, not evidence that AI has already reduced costs, shortened schedules or improved project outcomes.
For owners, designers and construction professionals, the practical question is therefore not whether to add AI everywhere. It is which bounded preconstruction task is suitable for a pilot, whether the project data is ready, and how people will check the output before it informs a decision.
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What does AI in preconstruction mean?
Preconstruction covers work undertaken before building begins, from early design decisions and estimating through scheduling, document review and risk planning. AI may assist with parts of these workflows by helping teams assess information or generate and compare options. The RICS survey identifies several of these functions as areas of current or expected interest, but it does not measure a single, standardized category called “preconstruction AI.” Some functions, such as progress monitoring, can also continue after construction starts.
The distinction matters: an AI system used for one design or document task is not the same as an organization-wide transformation. Nor does a survey of professional views establish that a particular tool can perform a task reliably on a specific project. The RICS report offers a snapshot of reported adoption and expectations, rather than product testing or project-performance evidence. RICS’s 2025 report draws on six questions in its Q1 2025 Global Construction Monitor and includes a separate Q2 2025 skills survey.
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How widely is AI being used in construction?
RICS’s 2025 global survey suggests that construction is still in an early adoption phase: pilots are more common than regular use, and organization-wide deployment is rare. The figures describe respondents’ reports about their organizations, not a census of construction firms or a measure of project results.
| Reported level of AI implementation | Share of respondents | What it indicates |
|---|---|---|
| No AI implementation in the organization | Approximately 45% | AI had not been implemented, according to the respondent. |
| Early pilot phase | 34% | Organizations were experimenting, rather than reporting routine use. |
| Regular use in specific project processes | Just under 12% | Use was established in selected processes, not necessarily across the organization. |
| Use across multiple processes | 1.5% | Respondents reported broader, but not necessarily organization-wide, application. |
| Fully embedded organization-wide use | Less than 1% | Only a very small share reported AI as fully embedded. |
These are adoption categories reported by respondents to RICS’s Q1 2025 Global Construction Monitor, which received more than 2,200 responses. They should be read as a sector-level sentiment and status snapshot: the report does not establish why any organization adopted AI, how consistently it used a system, or what results it achieved. The categories also show why a claim that “AI is transforming construction” needs qualification. There is visible experimentation and interest, but broad operational integration remained uncommon in the survey.
Where do construction professionals expect AI to have the greatest impact?
Design optioneering was the leading expected impact area in the survey: 40% selected it as the area where AI would have the greatest impact over the next five years. That result reflects respondents’ expectations, not a finding that AI-generated designs are better or that teams have already realized benefits.
Design option evaluation
Design optioneering means exploring alternatives and weighing them against project requirements. In a preconstruction setting, teams may need to consider competing design approaches, constraints and priorities before committing to a direction. AI could support this work by helping professionals examine alternatives, but the survey does not specify the software, data or methods respondents had in mind. It also does not report whether AI-assisted options were more buildable, sustainable, affordable or aligned with a client’s needs.
The useful takeaway is that early option evaluation is where respondents see a strong potential role—not that professional design judgment can be delegated. Teams still need to set the criteria, identify constraints, check assumptions and decide whether an option is appropriate for the project.
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Schedules, progress and resources
Project scheduling and progress monitoring each received a 36% selection for high positive significance. Resource optimisation was selected by 30%. These functions are related but not interchangeable: a schedule concerns planned timing and sequence, progress monitoring concerns how work is advancing, and resource planning concerns the people, equipment or other capacity needed to carry it out.
For preconstruction teams, such workflows can be relevant when developing or reviewing a plan. The survey establishes that respondents see potential in them; it does not establish that AI schedules are more accurate, that forecasts prevent delays, or that automated resource recommendations produce better outcomes. A pilot should make clear which task it is testing and which professional remains responsible for the resulting decision.
Documents, estimating and risk
Contract and document review was selected by 30%, while risk management was selected by 29%. RICS also lists cost estimating among the near-term use cases to consider in its recommendations. These are plausible areas for controlled experimentation because early project planning involves substantial information and decisions with consequences for scope, cost and delivery. However, the RICS figures are perceptions about potential significance, not measured rates of error reduction, faster review or more reliable estimates.
In practice, a team assessing a document-review or estimating pilot should distinguish assistance from approval. An AI output can help a professional locate information or flag items for attention, but a person must check the relevant source material and determine what action is warranted. A flagged clause, inferred quantity or estimated cost is not self-validating merely because it was generated by a system.
What is holding adoption back?
The leading barrier in the RICS survey was lack of skilled personnel, cited by 46% of respondents. Integration with existing systems came next at 37%, followed by data quality and availability at 30%. Cost, unclear return on investment and the lack of standards or guidance also featured prominently.
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| Reported implementation barrier | Share of respondents | Why it matters to a project team |
|---|---|---|
| Lack of skilled personnel | 46% | Teams need people able to evaluate outputs, manage implementation and understand the relevant workflow. |
| Integration with existing systems | 37% | A tool may be difficult to use if project information cannot be accessed or exchanged appropriately. |
| Data quality and availability | 30% | Incomplete, inconsistent or inaccessible inputs can undermine the usefulness of analysis. |
| High implementation costs | 29% | Adoption involves more than acquiring a tool; implementation effort also matters. |
| Unclear return on investment | 28% | Organizations may not know whether the effort produces a worthwhile result. |
| Lack of standards or guidance | 25% | Teams may lack a common basis for acceptable use, oversight or evaluation. |
These percentages represent barriers selected by survey respondents; they do not rank every possible obstacle or quantify its cost. Taken together, they point to a readiness problem as much as a technology problem. A pilot can fail to answer whether AI helps if staff lack time to validate it, the necessary project information is unavailable, or the system cannot fit the team’s established process.
How should a building team evaluate an AI pilot?
A useful pilot starts with a defined task and a comparison that can show whether the new approach helps. RICS recommends prioritizing use cases, improving data quality and governance, using due diligence and human oversight, and later evaluating performance against metrics, benchmarks and user feedback.
- Choose one bounded workflow. Define the task—for example, reviewing a specified set of project documents or supporting a defined scheduling activity—rather than setting a vague goal such as “use AI in preconstruction.” RICS identifies scheduling, cost estimating, sustainability and safety among near-term areas to consider.
- Check the inputs and access. Confirm which project and asset data the task requires, whether the team can use it, and whether it is sufficiently complete and consistent for the intended purpose. RICS recommends strengthening data quality and governance, including through common data environments and open data standards.
- Set rules for use and review. Decide who may use the system, what information can be entered, how outputs will be checked and who is accountable for decisions. RICS advises due diligence, ethical safeguards and human oversight as teams experiment.
- Define a baseline before judging results. Agree on the existing process and relevant performance measures before the pilot starts. Later comparison against a baseline or benchmark is more informative than relying on a general impression that the tool feels faster or more capable.
- Collect user feedback and decide whether to continue. Ask the people doing the work what was useful, where review was difficult and whether the process fit their responsibilities. Expand only if measured performance and user feedback justify the added integration and governance effort.
This approach keeps the decision tied to evidence available to the organization. It also makes it easier to distinguish a tool demonstration from a successful workflow change: a demonstration may show that a system can produce an output, while a useful pilot must establish that the output fits the task, can be reviewed and supports a decision under real project conditions.
What should teams check when comparing AI tools?
The RICS report does not compare named products, verify software features or publish current commercial terms. For a team assessing tools, a workflow-and-evidence comparison is more useful than a feature list alone.
| Evaluation area | Questions to ask |
|---|---|
| Workflow | Does the system address the actual task—such as option evaluation, estimating, scheduling, document review or risk support—or only a related activity? |
| Evidence | Is a claimed benefit a vendor description, an internal pilot result or an independently measured project outcome? What task and conditions does the evidence cover? |
| Data and integration | What information, formats and access does the tool need? Can it work with the organization’s systems and project information without creating avoidable gaps? |
| Human accountability | Who checks outputs and retains authority over consequential design, cost, resource, safety or risk decisions? |
| Governance and transparency | Are access controls, acceptable-use rules, oversight, auditability and communication about AI use clear? |
| Business case | What baseline, metrics, implementation effort and user feedback will determine whether the pilot merits expansion? |
This comparison does not assume that one tool is best across all preconstruction work. Requirements differ by task, data and organizational process. Product claims should be validated against the intended workflow, and any claimed project benefit should be separated from what the team has actually measured.
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What does responsible adoption look like?
RICS’s recommendations treat readiness and oversight as prerequisites to scaling. Its immediate actions for 2025–2026 include upskilling teams, assigning cross-functional leadership, prioritizing near-term use cases, strengthening project and asset data, and applying due diligence and human oversight. For medium-term scaling, it points to explainability, fairness, transparency, formal oversight, audit mechanisms, accountability and performance evaluation.
That guidance translates into practical questions before a project team relies on an AI-assisted output: Can the responsible professional understand enough about how it was produced to review it? Can the organization trace what information and process informed a consequential decision? Is there a clear owner for checking the result and addressing errors? Are people told when AI is part of the work? If these questions have no workable answers, adding the system may create uncertainty rather than improve the process.
Skills are central to this model. Upskilling does not mean every construction professional must become an AI developer; it means giving the people who select, use and review systems enough understanding to question outputs, recognize limits and keep responsibility clear. Cross-functional leadership can connect technical, project and professional perspectives so that a pilot is assessed as a change to work, not simply as a software installation.
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What can the evidence say about AI’s effect on projects?
The available RICS evidence supports a measured conclusion: practitioners report limited organizational adoption, identify possible high-impact functions and describe substantial implementation barriers. It does not quantify realized preconstruction time savings, estimate accuracy, design quality, project cost reductions or safety improvements caused by AI.
The main survey received more than 2,200 responses and reported results from the Q1 2025 Global Construction Monitor; a separate Q2 2025 skills survey included more than 2,000 responses. The report says its main-survey regional distribution was 48% UK, 19% Asia Pacific, 14% Middle East and Africa, 10% Americas and 8% Europe. Because the UK was the largest regional share, the global snapshot should not be treated as an evenly weighted census of every construction market. RICS also says the survey design sought to minimize sentiment, social and response bias; that aim does not turn reported attitudes into measured project outcomes.
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Maureen Ehrenberg FRICS CRE, then Acting President-Elect of RICS, described the report as a “global snapshot” of how surveying professionals think about AI, its potential and what holds adoption back. Her framing captures the evidence’s value: it can help teams understand professional expectations and readiness, but it is not a substitute for project-level evaluation.
Frequently Asked Questions
What share of survey respondents came from the UK?
The RICS report says 48% of responses to its Q1 2025 Global Construction Monitor came from the UK. Asia Pacific accounted for 19%, Middle East and Africa 14%, the Americas 10% and Europe 8%.
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In RICS’s separate Q2 2025 skills survey, 69% of project managers and 67% of quantity surveying and construction professionals agreed that AI will help surveyors deliver greater value in the future. Those are views about future value, not measured results.
What is the difference between design optioneering and design automation?
Design optioneering is the evaluation of alternative approaches against project needs; it does not necessarily mean that a system autonomously designs or selects the final solution. The RICS survey identifies optioneering as an expected impact area but does not define a particular automation capability.
Does the RICS survey show AI reduced construction costs?
No. It reports respondents’ views and organizational adoption status, not a causal analysis of project costs. A team would need its own appropriately measured comparison to establish whether a specific use of AI affected cost.
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