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AI is beginning to change preconstruction by making large sets of design, cost, schedule and document data easier to analyse, but it has not yet become standard practice. In the Royal Institution of Chartered Surveyors (RICS) Artificial intelligence in construction report 2025, about 45% of respondents said their organisation had no AI implementation, while only a small minority reported regular or organisation-wide use. Practitioners see the greatest near-term potential in comparing design options, followed by scheduling, progress monitoring, estimating, document review and resource planning.
That distinction matters: the RICS figures describe reported adoption and expectations, not proven reductions in cost, programme duration or defects. A responsible preconstruction strategy treats AI as decision support. Professionals still set requirements, validate assumptions, approve designs and remain accountable for safety, cost and compliance.
What does AI do in the preconstruction phase?
AI in preconstruction means using machine-learning, generative-AI or rules-based automation to interpret project information before physical work starts. It can search drawings and specifications, compare design alternatives, extract quantities, identify schedule conflicts, classify risks and produce draft analyses for professional review.
Preconstruction is broader than design alone. It can include feasibility, concept and detailed design, estimating, procurement planning, programming, permitting, constructability review and risk preparation. Some functions, such as progress monitoring, continue after mobilisation, so sector surveys should not be read as measuring preconstruction-only adoption.
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What AI changes first
- Speed of analysis: software can screen many documents or design scenarios faster than a person working manually.
- Consistency: a configured workflow can apply the same checks to every revision, provided the underlying data is reliable.
- Traceability: a governed system can retain prompts, source documents, assumptions and approvals for later review.
- Decision breadth: teams can evaluate more options instead of selecting the first workable scheme.
These are workflow capabilities, not guarantees. An AI output is only as dependable as its source data, configuration and human review.
How widely is AI being adopted in construction?
Adoption is still at an experimental stage for most organisations. RICS reported six questions from its Q1 2025 Global Construction Monitor, which received more than 2,200 responses worldwide. The figures below describe respondents’ reported organisational status, not an audit of every construction company.
| Reported status | Share of respondents | What it indicates |
|---|---|---|
| No AI implementation | Approximately 45% | AI had not been implemented in the respondent’s organisation. |
| Early pilot phase | 34% | Experiments or limited trials were under way. |
| Regular use in specific processes | Just under 12% | AI was used repeatedly for selected workflows. |
| Use across multiple processes | 1.5% | Deployment extended beyond one isolated process. |
| Fully embedded organisation-wide use | Less than 1% | AI was integrated broadly across the organisation. |
The main survey’s responses came from several regions: 48% from the UK, 19% from Asia Pacific, 14% from the Middle East and Africa, 10% from the Americas and 8% from Europe. Because the UK was the largest single regional group, the results should be treated as a global sentiment snapshot with an uneven regional mix, not a census.
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Design optioneering is the structured comparison of alternative solutions against constraints such as area, cost, carbon, programme, buildability and client requirements. Forty percent of RICS respondents selected it as the area where AI would have the greatest impact over the next five years, the highest share for any listed function.
An AI-assisted option study might generate or classify alternatives, test them against a parameter set, highlight conflicts and prepare a comparison for the design team. For example, a team could ask a system to organise façade options by embodied-carbon assumptions, floor-area targets and procurement constraints, then have engineers verify the calculations and code compliance.
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The 40% figure is an expectation, not evidence that AI-generated designs are inherently better. A visually attractive option can fail planning rules, structural checks, fire requirements, lifecycle targets or budget limits. Human designers and statutory specialists must approve the criteria and the final choice.
What makes an option study credible?
- A written brief defining performance requirements and non-negotiable constraints.
- Version-controlled drawings, models, specifications and cost assumptions.
- Explicit units, locations, building codes and escalation dates.
- A record of which alternatives were rejected and why.
- Independent checking of calculations and compliance before a decision is issued.
Which other preconstruction workflows can AI support?
RICS respondents identified several project functions as having high positive significance. These results show where practitioners see opportunity; they do not demonstrate realised savings or accuracy improvements.
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|---|---|---|
| Project scheduling | 36% | Drafting activity logic, identifying sequencing conflicts and testing programme scenarios. |
| Progress monitoring | 36% | Comparing reported or captured progress with planned work; this often spans construction as well as planning. |
| Resource optimisation | 30% | Exploring labour, plant, material and site-logistics allocations. |
| Contract and document review | 30% | Finding clauses, obligations, exclusions, missing information and inconsistent terminology. |
| Risk management | 29% | Classifying risk registers, surfacing dependencies and drafting mitigation prompts. |
Estimating and quantity take-off
AI can extract quantities from structured models or drawings, map items to a cost database and flag unusual movements between estimates. It can also draft assumptions and identify missing scope. Estimators must check measurement rules, specification interpretation, location factors, market conditions, supplier quotations, escalation and exclusions. A generated quantity is not a tender-ready measurement until a qualified person validates it.
Scheduling and constructability
Systems can analyse activity relationships, compare a proposed sequence with historic patterns and highlight clashes between trades or access requirements. The useful output is a scenario for the planner to test, not an automatically approved baseline. Site constraints, permits, long-lead items, weather allowances, labour agreements and temporary works still require project-specific judgment.
Document and contract review
Language models can locate clauses across large document sets and produce a first-pass list of obligations, notice periods or inconsistencies. They can miss context, misread defined terms or invent a citation. Keep the source paragraph with every extracted point, restrict access to confidential material and require commercial or legal review before anyone relies on the result.
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Resources, sustainability and safety planning
RICS recommends near-term use cases that include resource planning, cost estimating, sustainability and safety. AI may help compare material scenarios, identify resource bottlenecks or organise hazard information. Those outputs are prompts for competent professionals; they are not a substitute for statutory design duties, risk assessments, method statements or site supervision.
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What is holding organisations back?
The principal obstacles are practical rather than a lack of interesting demonstrations. RICS respondents identified the following implementation barriers:
| Barrier | Share citing it | Why it matters in preconstruction |
|---|---|---|
| Lack of skilled personnel | 46% | Teams may not know how to select, configure, validate or govern AI workflows. |
| Integration with existing systems | 37% | Drawings, models, cost plans and schedules may use incompatible formats or permissions. |
| Data quality and availability | 30% | Missing, inconsistent or outdated project data produces unreliable analysis. |
| High implementation cost | 29% | Configuration, integration, security and training can exceed the software licence. |
| Unclear return on investment | 28% | Time saved in one task may not offset review, correction and governance effort. |
| Lack of standards or guidance | 25% | People may not know what data, audit trail or approval standard is acceptable. |
These percentages are survey responses, so they can overlap: one organisation may face several barriers at once. Poorly structured information is especially consequential because an AI system can process bad data quickly and make the result look authoritative.
How should a construction firm start an AI preconstruction pilot?
Start with one bounded, repeatable task and a measurable baseline. RICS’s recommendations emphasise upskilling, cross-functional leadership, prioritised use cases, better project-data governance, due diligence, ethical safeguards and human oversight.
- Choose a narrow workflow. Select a task such as searching specifications, checking estimate revisions or comparing schedule scenarios. Avoid an initial goal such as “automate design,” which has no clear boundary.
- Define the baseline. Record current cycle time, rework, error findings, review hours and user satisfaction for several comparable tasks. Without a baseline, a faster draft may simply move effort into correction.
- Inventory the data. Identify authoritative drawings, models, specifications, cost codes, schedules and risk registers. Record owners, revision status, formats, retention rules and access permissions.
- Set acceptance tests. Prepare representative examples containing known quantities, clauses, clashes or risks. Specify the minimum recall, precision, completeness and response time required for a useful result.
- Assign accountability. Name the professional who approves each output and define decisions the system is never allowed to make autonomously, particularly those affecting safety, compliance, cost commitments or contractual rights.
- Run a controlled pilot. Use a segregated environment, approved data and a limited user group. Log prompts, source files, model or system version, outputs, corrections and approvals.
- Measure the whole workflow. Include preparation, checking, correction, training, integration and governance time, not just the time taken to generate an answer.
- Decide whether to scale. Expand only when results beat the baseline without unacceptable quality, confidentiality or accountability risks. Document lessons and update the acceptable-use policy.
How can teams evaluate whether an AI pilot is worthwhile?
Use a balanced scorecard rather than a single “accuracy” percentage. Compare AI-assisted work with the existing process on the same type of project and under the same information constraints.
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| Dimension | Questions to measure |
|---|---|
| Workflow | Did cycle time or manual search effort fall, and where did review work increase? |
| Evidence quality | Were outputs checked against authoritative documents, and how often were errors or omissions found? |
| Integration | Can users access current data without duplicate entry or uncontrolled exports? |
| Professional accountability | Is every decision linked to a named reviewer and an approval record? |
| Governance | Are permissions, retention, audit logs, transparency and incident handling working? |
| Business case | Do quantified benefits exceed implementation, review, training and support costs? |
RICS recommends using performance metrics, benchmarks and user feedback as adoption expands. A pilot that improves speed but increases unresolved errors, security exposure or coordination failures is not a successful transformation.
What governance does responsible AI require?
Before connecting project information to an AI service, establish an acceptable-use policy. It should state which data may be uploaded, which systems are approved, how confidential or personal information is handled, how outputs are labelled and when human review is mandatory.
Minimum controls
- Data governance: maintain a common data environment, clear naming and revision rules, and agreed open formats where practical.
- Access control: give users only the project and document permissions they need; review supplier access and deletion terms.
- Explainability: retain the source passages, model assumptions and transformation steps behind material recommendations.
- Fairness and transparency: test whether historical data or automated rankings disadvantage a group, supplier or design option without a defensible reason.
- Auditability: log versions, prompts, outputs, corrections and approvals so a decision can be reconstructed.
- Escalation: define how users report hallucinated content, data leaks, unsafe recommendations or suspected bias.
RICS identifies explainability, fairness, transparency, formal oversight, audit mechanisms, accountability and performance evaluation as medium-term priorities. Governance is therefore part of the system design, not paperwork added after deployment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What can AI not replace in early building decisions?
AI cannot assume the legal duty of a designer, cost consultant, planner, contractor or client representative. It does not automatically know whether a source is current, whether a local authority will accept an interpretation, or whether a proposed sequence is safe on a particular site.
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- Estimators must confirm measurement rules, market evidence, exclusions and commercial assumptions.
- Planners must test logic against real resources, access, permits, procurement and site constraints.
- Contract specialists must interpret obligations and obtain formal advice where rights or liabilities are affected.
- Clients must decide which trade-offs among cost, time, quality, carbon and risk are acceptable.
The safest division of labour is to let AI find patterns, draft alternatives and expose questions while people set the brief, challenge the result and make the decision.
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What does the evidence suggest about the next five years?
RICS’s survey indicates strong interest but a long path from pilots to embedded practice. Forty percent of respondents named design optioneering as the most significant expected impact area. Scheduling and progress monitoring each received 36%; resource optimisation and contract or document review each received 30%; and risk management received 29%.
A separate Q2 2025 RICS skills survey, with more than 2,000 responses, found that 69% of project managers and 67% of quantity-surveying and construction professionals agreed AI will help surveyors deliver greater value in the future. That is a confidence measure, not a forecast of guaranteed productivity.
Maureen Ehrenberg FRICS CRE, Acting President-Elect of RICS, described the report as a global snapshot of how surveying professionals see AI’s potential, obstacles and preparedness, and stressed that use should support trusted and safe practice, real value and the public good. The practical implication is clear: organisations that improve skills, data quality and accountability can test useful applications; organisations that skip those foundations are likely to scale errors and uncertainty.
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What is the practical takeaway for a project team?
Use AI to widen and accelerate early analysis, not to remove professional responsibility. Pick a clearly bounded preconstruction task, supply controlled and current data, define success before deployment, preserve an audit trail and compare results with the existing process. Scale only when measured performance, user feedback and governance controls show that the tool improves the complete decision workflow rather than merely producing faster drafts.
Frequently Asked Questions
Does AI reduce construction costs during preconstruction?
The available RICS evidence does not establish that AI has caused lower project costs. It records perceived opportunities and reported adoption, so any savings claim should be demonstrated through a project-specific baseline and independent measurement.
Which project data should be prepared before introducing AI?
Begin with authoritative, revision-controlled drawings, models, specifications, cost codes, schedules and risk registers. Document ownership, permissions, units, location, dates and retention rules so users can tell which information is current.
Can an AI system approve a design or estimate automatically?
Automatic approval is inappropriate for decisions carrying professional, safety, regulatory or contractual responsibility. AI can prepare comparisons or flag anomalies, but a suitably qualified person must verify the evidence and record the approval.
How is AI adoption different for small construction firms?
Smaller firms can start with a narrow workflow and shared templates rather than attempting enterprise-wide integration. They still need confidentiality rules, a named reviewer, reliable source data and a simple baseline for judging whether the pilot helps.
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