72% of Construction Firms Want AI. Under 23% Have a Plan.
Construction productivity has gone backwards since 1990, costing Australia $62 billion a year. Most firms want AI — fewer than one in four have a plan.
Building less per hour than in 1990
Construction workers in Australia build less per hour today than they did in 1990. That is not a rhetorical flourish — it is the central finding of Oxford Economics' Construction Productivity Challenge report, published in August 2025. Over a 35-year window where the broader market sector achieved 64 per cent productivity growth, construction managed 17 per cent.
The annual cost of that stagnation, per Oxford Economics: $62 billion in lost output.
That number lands in your business as the quote that took two days instead of two hours. The report written after hours on unpaid overtime. The second truck dispatched because materials were not staged correctly for the first.
Productivity growth over 30 years
Construction
17%
Near-stagnant since 1990
Market average
64%
Almost 4× higher
You cannot hire your way out
The traditional fix for a construction productivity problem has been headcount. That lever is now broken.
Infrastructure Australia’s national outlook projects a shortfall of 300,000 skilled construction workers by mid-2027. Trades workers and labourers account for 126,000 of that gap. The pipeline driving the demand — $242 billion in infrastructure work across FY2025 to FY2029 — is not easing. And the Housing Industry Association estimates the country needs 83,000 additional tradies just to meet the National Housing Accord target of 1.2 million new homes by 2029.
Over a quarter of the current construction workforce is aged 55 or older. You cannot recruit your way to higher output per hour when the people are not there to recruit.
72 per cent want AI. Fewer than 23 per cent know how.
The Deloitte and Autodesk State of Digital Adoption 2025 report surveyed the Australian construction sector and found a telling split. Technology adoption is not the problem — 49 per cent already use construction management software, data analytics sits at 48 per cent, and average technology investment has risen from 19 per cent of total expenditure in 2023 to 25 per cent in 2025. Over 80 per cent of firms using digital tools report positive return on investment.
And 72 per cent of construction firms say they plan to implement AI and machine learning.
But fewer than 23 per cent have a clear technology strategy or roadmap for getting there.
We wrote recently about trade contractors reporting the same pattern: most say AI matters, but only 12 per cent have embedded it into operations. The broader construction sector is repeating the cycle — spending more on technology each year, getting value from most of it, but approaching the biggest lever without a plan.
The AI execution gap in construction
Plan to adopt AI
72%
Of construction firms
Have a tech strategy
<23%
A 49-point gap
Where the hours actually go
The productivity problem in construction lives outside the billable work. It sits in scheduling, reporting, quoting, compliance documentation, and material coordination — tasks that eat hours from every crew member every day.
Microsoft ANZ reported in June 2026 on field workers using AI-assisted dictation and automated form completion to cut reporting time from hours to minutes. Scale that across a team: if AI reporting recovers an hour a day per technician on a ten-person crew, across 230 working days, that is 2,300 hours returned to billable work annually. At a loaded rate of $80 per hour, the value is roughly $184,000 — without a single new hire.
This is an operations throughput problem. The technology exists today. The bottleneck is not capability. It is the gap between buying a tool and building a process around it.
Audit before you automate
Before committing to any AI platform, run a structured one-week time audit. Have each crew member log non-billable hours: travel, quoting, reporting, compliance, rework, material coordination. Compare the actual split against what you assumed.
The gap between perception and reality is the size of the opportunity — and it tells you whether AI-assisted scheduling, automated reporting, or smart quoting should be your first move.
The businesses closing the productivity gap are not the ones with the most subscriptions. They are the ones that measured the problem before they tried to solve it.
Key takeaways
Sources
Oxford Economics — The Construction Productivity Challenge in Australia (August 2025)
Deloitte and Autodesk — State of Digital Adoption in Construction 2025
Microsoft Source Asia — Australia’s next AI frontier is on the job site (June 2026)
▶Assumptions & methodology
- The $62 billion annual figure and the finding that construction output per hour has declined since 1990 are from Oxford Economics’ ‘The Construction Productivity Challenge in Australia’ (August 2025). The 17% vs 64% productivity growth comparison covers approximately 30 years; the exact baseline years differ between ABS data series used in various sources.
- The 300,000 worker shortfall and 126,000 trades worker subset are from Infrastructure Australia’s annual national outlook. The $242 billion infrastructure pipeline covers FY2025–FY2029. The 83,000 tradie figure is from the Housing Industry Association’s assessment of National Housing Accord requirements.
- The $184,000 recovered capacity estimate is illustrative: 10 technicians × 1 hour saved/day × 230 working days × $80/hour loaded rate. The one-hour daily saving is a conservative interpretation of Microsoft ANZ’s June 2026 case study, which describes reporting time dropping from ‘hours’ to ‘minutes.’ The $80/hour loaded rate is a mid-range estimate for Australian trades (wages, super, vehicle, fuel, insurance).
Next
AI-Ready Australian Firms Save 4 Hours Per Employee Per Week
Field Notes are general commentary on AI trends for Australian businesses. They don’t constitute professional advice. Talk to your accountant, lawyer, or IT adviser before acting on anything specific to your situation — or talk to us if you want help working out where AI fits.
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