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Construction companies are spending heavily on artificial intelligence, yet the early effect is organizational change rather than widespread job cuts. Berkeley Research Group’s report AI in Construction: Measurable Results, Unclear Benchmarks, published October 9, 2026, finds that adoption is concentrated in information-heavy work, while firms still lack a consistent way to prove financial return.
What BRG’s survey actually measured
BRG surveyed 109 executives and other leaders involved in AI implementation in July 2026. The sample included 58 people from construction and project delivery and 51 from development and ownership. Sixty-five respondents were in North America, 22 in Europe and 22 in APAC. BRG says its analysis prioritizes North American findings unless a regional comparison is specified.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11The results are anonymous and self-reported. They show how firms describe investment, adoption and outcomes, not a controlled estimate that AI caused a particular saving or staffing change. Percentages can differ slightly from 100% because of rounding.
Investment is substantial, but enterprise integration is rare
| Measure | Finding | Qualification |
|---|---|---|
| At least $250,000 invested | 68% | North American respondents; spending during the previous 24 months |
| $1 million or more invested | 37% | North American respondents; previous 24 months |
| AI fully integrated across projects and operations | 15% | North American respondents |
This gap matters. Buying software, funding pilots and hiring implementation help does not mean that models are connected to estimating, project controls, field reporting, document systems and enterprise governance. A company can spend seven figures while AI remains limited to individual teams or isolated workflows.
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AI is changing construction jobs more than eliminating them
The survey does not show broad headcount replacement. Eighteen percent of respondents said AI had led to additional headcount in some areas, while 11% reported reductions in some areas. The more common responses were reskilling or retraining existing staff (38%), redeploying staff (31%) and creating new types of roles (29%).
That pattern fits the nature of the work being automated. BRG managing director John Fletcher said: “AI is not replacing the materials, equipment, and people that physically build a project. Its most immediate impact is on the information-intensive work around construction: design, drawings, scheduling, and project management.”
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Where construction companies are using AI first
Information-dense work leads adoption because it produces large volumes of drawings, specifications, schedules, contracts, cost data and field records that software can search or analyze.
| Project or business area | Reported implementation | What the work can involve |
|---|---|---|
| Pre-construction | 46% | Feasibility, conceptual estimating, options analysis and early risk review |
| Construction | 49% | Scheduling, drawing review, safety monitoring, procurement and project controls |
| Post-construction | 40% | Claims, closeout analysis, handover records and performance review |
| Operations | 43% | Asset monitoring, maintenance information, contract management and compliance |
Planning and feasibility was the leading individual use, reported by 58% overall and by 80% of builders. Dara Hendrix, a BRG managing director, described it as a natural starting point because project risk is concentrated there and early analysis often generates little direct revenue. AI can make more detailed options and risk reviews practical before a project is committed.
Hendrix also cautioned that the benefit is not simply completing an existing review faster: “AI can make it practical to perform analyses that teams previously might have skipped because they were too time and detail intensive. The tradeoff is that the results still require independent verification.”
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Reported savings do not create a standard ROI benchmark
BRG found that firms use different definitions of return. That makes one company’s “AI savings” difficult to compare with another’s result.
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| Primary ROI approach | Share of respondents | What it captures |
|---|---|---|
| Financial metrics | 37% | Direct cost, revenue or margin measures |
| Operational metrics | 29% | Cycle time, throughput, quality or delivery performance |
| Usage or adoption metrics | 23% | How often people use an AI tool or workflow |
| Qualitative assessment | 5% | Judgment-based evaluation rather than a quantified measure |
| No ROI evaluation | 6% | No formal assessment reported |
Double-digit cost savings were reported by 67% of respondents for construction, 62% for business operations, and 58% for both pre-construction and post-construction. Those figures are reported outcomes, not an independently audited industry benchmark. They also do not show whether the savings came from labor reduction, avoided rework, fewer delays, better procurement or another mechanism.
Effectiveness can be more important than speed. Better design quality, earlier risk detection or a more complete claims file may improve the project without producing a clean labor-cost line item. Travis Olson, a BRG director, summarized the difficulty: “The hardest ROI questions arise when AI makes something possible that was not realistically on the table before.”
For construction, contingency can provide a more meaningful measure than hours saved. Ben Nolan, a BRG managing director, said that when AI identifies risks earlier, the return may appear as uncommitted contingency preserved at successive project stages, through fewer unexpected change orders and delays.
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| Region | At least $250,000 invested | Full integration | Notable adoption emphasis |
|---|---|---|---|
| North America | 68% | 15% | Pre-construction 46%; construction 49% |
| APAC | 41% | 18% | Business operations 50% |
| Europe | 18% | 9% | Post-construction 41% |
The investment and integration figures are regional survey responses, not market-size estimates. Different project mixes, regulatory environments, labor markets and technology strategies may explain why priorities vary.
Why the business case remains difficult to prove
- Data quality: AI cannot reliably analyze incomplete, inconsistent or poorly classified project records.
- Integration: A pilot that cannot exchange data with estimating, scheduling, document control or field systems may create another information silo.
- Verification: Generated quantities, interpretations and predictions require review by a qualified person before they influence cost, safety, design or contractual decisions.
- Accountability: Firms must identify who approves AI-assisted work and who is responsible when an output is wrong.
- New capability: A useful analysis that was previously too expensive or time-consuming may produce value without a direct before-and-after labor comparison.
- Contract treatment: Existing agreements may not specify permitted AI use, validation duties, records, disclosure or liability.
Olson said the industry should ask whether contracts still create the right incentives as AI becomes part of project delivery. That is a commercial question as well as a technology question: parties need to know what was generated, what was checked, what records must be retained and how responsibility is allocated.
How a contractor can measure an AI initiative
- Define one decision or bottleneck. Choose a specific problem, such as drawing-review turnaround, procurement exceptions, schedule-risk detection or claims-document retrieval.
- Record a baseline. Capture the current cycle time, error or rework rate, staffing effort, delay exposure, contingency position and quality outcome before deployment.
- Set an outcome hierarchy. Track direct financial impact where it exists, but also operational performance, quality, risk avoided and new analyses made possible.
- Measure adoption separately. Login counts, prompts or completed AI workflows show use; they do not prove better project outcomes.
- Use a controlled review process. Require independent verification for outputs that affect safety, design, payment, schedule commitments, compliance or claims.
- Review the result at project gates. Compare the baseline at estimating, award, major design releases, construction milestones and closeout rather than waiting for a single end-of-project number.
- Document who owns the result. Keep an auditable record of data sources, model or tool version, human reviewer, decision taken and any correction.
BRG’s implementation practices for construction leaders
- Focus each implementation on a clearly defined problem instead of deploying AI as a general productivity slogan.
- Invest alongside the software in talent, reliable data and system integration.
- Set explicit accountability for AI-assisted work and require independent verification where the consequences of error are material.
- Do not reduce every initiative to hours saved or immediate cost reduction; effectiveness and previously impractical analysis can be the real value.
- Update contracts and internal policies to address permitted AI use, validation responsibility, records, disclosure and liability.
The evidence points to a transition phase: construction firms are funding AI and changing how teams work, but most have not integrated it across the enterprise or agreed on a common definition of return. Headcount reduction is not the dominant early outcome, and reported savings should be treated as company-specific results that require clear baselines and verification.
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