Digital construction is not nearly “there” if that means every firm routinely uses connected, measurable digital workflows. BIM is established in many organisations, cloud collaboration is more familiar, and AI experimentation is accelerating. Yet the strongest current evidence still shows a sector where implementation is uneven: many teams are exploring or piloting tools, while skills, data quality, interoperability, change management and productivity measurement lag behind expectations.
The practical question is no longer whether digital tools exist. It is whether a specific workflow—such as progress reporting, document review, handover or quantity take-off—can produce a repeatable improvement across projects without creating new safety, accountability or integration problems.
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What “digital construction” actually includes
Digital construction is an umbrella term, not a single adoption category. It includes building information modelling (BIM), common data environments, cloud collaboration, digital twins, field reporting, product information management, automated design and estimating, connected equipment, data analytics and artificial intelligence.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Those technologies have different maturity levels. A contractor may coordinate models successfully while still relying on disconnected spreadsheets for cost control. A designer may use cloud authoring but have no structured handover data. An organisation may run an AI pilot without having the governed information needed to use it safely at scale.
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That is why one percentage cannot describe “digital construction adoption”. The population surveyed, technology measured, geography, company size and definition of use all change the result.
What the latest evidence says about progress
Three recent sources illustrate the gap between attention and embedded capability. NBS’s Digital Construction Report 2025 surveyed 559 design and construction professionals about AI, BIM, cloud technology, sustainability, product data, regulation and digital ways of working. It is a professional-attitudes snapshot, not a census of construction businesses. Its central message is that confidence is growing, but more than half of respondents worry about being left behind as digital transformation accelerates.
RICS’s 2025 global construction AI survey asked more than 2,200 professionals, with 48% of responses from the UK. It measures reported organisational AI deployment, preparation and sentiment—not the maturity of every digital process. ONS provides a different view through Business Insights and Conditions Survey Wave 159, covering UK construction businesses with 10 or more employees during 5–28 June 2026.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitches| Measure | Result | How to interpret it |
|---|---|---|
| NBS Digital Construction Report 2025 | 559 design and construction professionals surveyed | Professional views and reported practice across several digital topics |
| RICS AI in Construction 2025 | About 45% reported no organisational AI implementation; 34% reported early pilots; just under 12% reported regular use in specific processes; less than 1% reported organisation-wide embedded use | Global professional survey of AI adoption, not all digital construction |
| ONS Business Insights and Conditions Survey, Wave 159 | 13% of UK construction businesses with 10 or more employees reported using AI in June 2026 | UK business estimate for AI only; definitions and population differ from RICS |
| RICS Global Construction Monitor, Q4 2023 | No significant digitalisation-uptake progress compared with 2021 and 2022 | Evidence of an earlier plateau after pandemic-era acceleration |
These figures should not be averaged or ranked as though they were measuring the same thing. The defensible conclusion is narrower: digital interest and experimentation are widespread, but deep, organisation-wide AI use remains uncommon and broader digital maturity varies sharply by firm, project and workflow.
AI is attracting attention faster than it is being embedded
RICS respondents see the strongest potential in progress monitoring and scheduling, each selected by 36% of respondents. Resource optimisation and contract or project-document review were each selected by 30%, while 29% identified risk management. These are judgements about where AI could matter, not measured productivity gains.
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The deployment figures show why enthusiasm should not be confused with transformation. Nearly half of respondents reported no organisational AI implementation, and a further third described early pilots. Regular use in particular processes was reported by just under 12%; fewer than 1% said AI was embedded across the organisation. A pilot that produces a useful summary or detects a scheduling anomaly is valuable, but it is not equivalent to a controlled process used consistently by project teams and partners.
Human accountability also remains essential. AI-generated quantities, classifications, risk flags or document summaries need review by people with the relevant professional competence. Safety decisions, contractual interpretations and design responsibilities cannot be delegated simply because a system produces a confident-looking output.
Why adoption stalls: three connected constraints
Skills and available people
In the RICS 2025 survey, 46% identified a lack of skilled personnel as an AI-adoption barrier. That includes more than software specialists. Teams need people who understand construction practice, information standards, data governance, cyber risk and the limits of automated outputs. Site staff also need time, access and training, not just licences.
Integration effort
Thirty-seven per cent cited integration challenges. A tool that works in isolation may still fail to improve a project if information must be re-entered between design, estimating, procurement, site reporting and handover systems. Integration also creates implementation cost, testing work and responsibility questions: who owns the data, who approves changes and what happens when systems disagree?
Data quality and availability
Thirty per cent cited data quality or availability. Automation cannot compensate for incomplete, inconsistent or inaccessible project information. Poor naming, missing attributes, duplicated records and unclear revision control make search and analysis unreliable. They also weaken the business case because teams spend their time cleaning data instead of improving the workflow.
These barriers reinforce one another. Weak information makes integration harder; integration work increases cost and training needs; skills shortages make it more difficult to establish governance and maintain the new process. A procurement decision made before diagnosing those conditions often creates another disconnected system rather than a capability.
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BIM remains a foundation, not a guarantee
BIM supports shared, model-based information and can provide a practical foundation for coordination, asset information and structured handover. However, a model or software subscription does not prove interoperability. The real test is whether authorised participants can find, exchange and use reliable information through an agreed process.
ISO 19650-based information management, clear responsibility matrices, naming conventions, revision control and a common data environment can matter more than adding another feature. Clients should specify the information they need and the decision it supports; delivery teams should confirm that the requested data can be produced, checked and maintained.
For readers building their technical understanding, Wiley’s BIM Handbook, fourth edition lists Rafael Sacks, Ghang Lee, Luciana Burdi and Marzia Bolpagni as authors. Wiley records first publication on 21 March 2025 and print ISBN 9781394222223. Its coverage includes BIM tools and platforms, implementation, ISO 19650, digital twins and applications for owners, designers, engineers, contractors and facility managers. Edition and ISBN should be checked with the relevant seller before purchase.
The productivity problem has not disappeared
RICS’s Construction Productivity Report 2026 cites McKinsey’s 2024 research estimating average global construction productivity growth at 0.4% a year from 2000 to 2022, compared with about 2% for the total economy. That historical comparison does not prove that digital tools caused, or failed to cause, the difference. It does show why claims about transformation need measurement rather than enthusiasm.
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Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →RICS also describes weak benchmarking. Only 5% of UK and European firms use benchmarks, regional rates elsewhere are higher but still limited, and one in five UK firms never measures productivity. Without a comparable baseline, a team cannot tell whether a new platform reduced reporting time, prevented rework, improved forecast accuracy or merely shifted effort to data administration. Skilled workers are identified as a major productivity driver alongside measurement and benchmarking.
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The most credible next step is a bounded workflow with a named owner, a measurable baseline and a clear reason for change.
- Select one high-value workflow. Choose a costly, repetitive or safety-critical activity such as progress reporting, document review, handover information or quantity take-off. Avoid starting with a vague programme called “digital transformation”.
- Record the current state. Measure cycle time, error rates, rework, approval delays, travel, manual entries or other project-relevant outcomes. State what improvement would justify continuation.
- Audit the information. Check whether the required records are complete, structured, version-controlled, governed and accessible to the people and systems involved. Identify ownership for corrections and approvals.
- Involve the users before selecting a tool. Include site and office teams, project partners, information managers and responsible professionals. Identify training, role changes, device access, connectivity and offline needs.
- Pilot against the baseline. Run the proposed process on a defined project or work package. Record exceptions, integration effort, user time, output quality and occasions when human review changes the result.
- Set controls and accountability. Define who can approve, edit, export and rely on the information. Retain human review where professional judgement, contractual interpretation or safety is involved.
- Scale only when it is repeatable. Expand when the workflow, data structure, responsibilities, support model and measured value work across more than one project or team. Document what must change for different clients, regions or project types.
How to choose between digital tools
There is no evidence here to justify naming a universal best platform. A sensible comparison should examine the following dimensions:
- Workflow coverage: authoring, coordination, field reporting, cost control, handover or operations.
- Interoperability: exchange formats, APIs, export quality and the ability to work with project partners’ systems.
- Information governance: permissions, revision history, retention, auditability and data portability.
- Implementation effort: configuration, migration, integration, support and total cost rather than licence price alone.
- Usability and access: training burden, mobile or site access, connectivity requirements and accessibility for office and field teams.
- Measurement: whether the tool supports a pilot with a credible baseline and produces evidence that can be checked.
- Fit: company size, geography, project type, client requirements and the capability of supply-chain partners.
So, are we nearly there?
Not if “there” means routine, integrated digital construction across the sector. The evidence points to meaningful progress in BIM practice, cloud collaboration and AI experimentation, but also to a large implementation gap. Low embedded AI use, recurring skills and integration barriers, unreliable data and weak productivity measurement all limit the value that technology can deliver.
The next stage will be less about collecting more demonstrations and more about making a small number of workflows dependable. Firms that connect trustworthy information, trained people, interoperable systems and measured outcomes will move further than firms that simply add another digital product to an unstructured process.
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