Chris M.17 August 2026

Construction does not have an AI adoption problem. It has a depth problem

Almost every UK contractor of any size has now tried AI. Almost none have put it anywhere near a decision that matters. That distinction, not the adoption rate, is the thing worth measuring.

What were the answers to the previous five questions?

1. What proportion of UK construction businesses use any AI at all, and how does that compare with the economy as a whole?

Around 13 per cent of construction businesses with ten or more employees, against around 35 per cent across all UK businesses of that size and 58 per cent in information and communication. Construction is under a quarter as likely to use AI as the leading sector.

2. What is the difference between the breadth of AI adoption and its depth, and which one is construction short of?

Breadth is how many firms have touched it. Depth is how much of the work actually runs through it. Across UK businesses breadth has roughly tripled since 2023, and ONS publishes no construction time series, so the 13 per cent is a single point estimate rather than a trend. Depth has barely moved: under 1 per cent of construction organisations report AI embedded organisation wide. Construction is short of depth.

3. Which three barriers to adoption do construction professionals themselves rank highest?

Shortage of skilled personnel at 46 per cent, systems integration at 37 per cent, and data quality or availability at 30 per cent. Notably, none of the three is solved by buying software.

4. Why is enthusiasm among professionals a poor predictor of whether a firm will get value from AI?

Because enthusiasm sits with individuals and value requires organisational plumbing: a controlled knowledge source, a defined reviewer, a delegated authority table, and a record. Two thirds of RICS members surveyed expect AI to help surveyors deliver greater value. Separately, and among construction professionals specifically, almost none work somewhere that has built any of that.

5. If a firm wanted one honest measure of how far it has actually got, what would it be?

Count the live business decisions that currently depend on an AI output, and name the accountable role for each, with the person currently holding it. Zero means the firm is experimenting, whatever its policy says. More than zero with no names is worse than zero, because the exposure exists and the accountability does not.

What do the adoption numbers actually show?

They show an industry that has tried AI widely and deployed it almost nowhere. The Office for National Statistics puts construction at 13 per cent, well behind the 35 per cent average for UK businesses with ten or more employees (ONS, 2026).

Share of UK businesses with ten or more employees using at least one AI technology, June 2026. Construction is under a quarter as likely to use AI as the leading sector, and well under half the all-business average.

That figure alone invites the usual conclusion, which is that construction is slow. The RICS data, drawn from the profession rather than from businesses generally, suggests something more specific and more useful.

Depth of adoption reported to RICS across more than 2,200 construction professionals, 48 per cent of them in the UK. The drop between piloting and production is where the industry is actually stuck.

Read down that funnel. Forty five per cent report no implementation at all. Thirty four per cent are piloting. Just under twelve per cent use AI regularly in one specific process. One and a half per cent use it across more than one. Under one per cent have it embedded organisation wide (RICS, 2025).

The interesting cliff is between 34 and 12. A third of the industry is piloting, and the group using AI routinely in even one process is barely a third of that size. These are snapshot categories rather than a cohort tracked over time, so nothing here shows a particular pilot failing. The shape is still unambiguous: the piloting population is far larger than the producing one, and pilots stall for reasons that have nothing to do with the model.

Why do the pilots stall?

Because the barriers professionals report are structural, and a pilot is specifically designed not to encounter them.

Barrier	Share rating it a barrier	Why a pilot does not hit it
Shortage of skilled personnel	46%	A pilot runs on one enthusiast; production needs a hundred people to be competent
System integration	37%	A pilot uses exported files; production needs live contract, programme and cost data
Data quality and availability	30%	A pilot picks a clean dataset; production gets whatever the CDE actually holds
Implementation cost	29%	A pilot is a licence; production is a knowledge layer somebody has to maintain
Unclear return	28%	A pilot is judged on whether it was impressive, not on a baseline
Regulatory uncertainty	11%	A pilot does not hit it because almost nothing does. It ranks last of the nine

The ranking matters. Regulatory uncertainty comes last, at 11 per cent. The three that lead are all about the state of the organisation rather than the state of the technology, which means the constraint is internal and therefore fixable without waiting for anyone.

What is the cost of standing still in this particular industry?

Higher than in most, because the underlying margins are thinner and the failure modes are expensive.

Construction recorded 3,805 company insolvencies in England and Wales in the twelve months to June 2026, the highest of any industry (Insolvency Service). The Get It Right Initiative puts directly recorded rework at around 5 per cent of project value, and total cost of error, once latent defects and process waste are counted, at 10 to 25 per cent (GIRI). CITB forecasts a need for roughly 41,200 additional workers a year to 2030 (CITB).

Those three facts describe a sector with no spare margin, a large avoidable error bill, and no prospect of hiring its way out. That is the case for doing this properly, and it is a stronger case than any efficiency claim. It is also why the real cost of doing nothing is rarely on anyone's line item, and why the admin tax on small contractors is paid without ever being costed.

One further number explains why the argument stalls. RICS found that roughly one in five UK construction firms never measure productivity at all, and that UK respondents were the most sceptical of five regions about automation, with 17 per cent rating it a high impact intervention (RICS, 2026). A sector that does not measure has no way to be persuaded by evidence, which explains a good deal about how these conversations go.

How do you place your own firm honestly?

Answer four questions in writing, today, without consulting the policy document.

Which live decisions currently depend on an AI output? Not which tools are licensed. Which decisions.
For each of those, which role is accountable, who currently holds it, and do they know?
Where does the AI get its information: the actual contract and current revisions, or general model knowledge?
If a client asked tomorrow how you govern AI, what document would you send?

Most firms find the first question harder than expected, because the honest answer includes things nobody procured. That is not a failure of discipline. part 2, the AI in your business that nobody procured is about that gap, and it is the widest one between what a construction business believes about its AI use and what is actually happening inside it.

What five questions should you be able to answer now?

Attempt these before tomorrow. Each has a defensible answer, and each is answered at the top of the next part.

Your firm has no AI policy and no procurement record for any AI tool. Does that mean AI is not being used?
Why is enterprise grade data security an incomplete answer to the question "is our AI use safe"?
A project manager asks a model to make a delay email "more professional and contractual". What has changed about that email that matters?
What is the difference between an AI system making a decision and an AI system influencing one, and which is more common?
Why is the first governance exercise a discovery exercise rather than writing a policy?

Which sources is this part built on?

Every figure quoted above resolves to one of these. Each was checked before publication.