The Role of AI in the Construction Industry | From Fragmented Delivery to Predictive Construction
Construction is entering a more demanding operating environment, where project scale is expanding faster than the sector’s ability to improve delivery performance. Global construction spending is projected to rise from USD 13 trillion in 2023 to USD 22 trillion by 2040, while global construction productivity grew by only 0.4% annually between 2000 and 2022, compared with around 2% for the total economy.
A major constraint lies in how construction projects are planned, monitored, and controlled. Project teams often rely on information that is inaccurate, incomplete, inaccessible, inconsistent, or delayed, weakening decisions across tendering, procurement, execution, and financial control. Rework can account for 3–11% of total construction project costs, with poor document management and weak quality controls identified among the main causes. However, digitalizing project documentation and quality processes can help reduce these costs by more than 50%2 by improving visibility, consistency, and control across project delivery.
The sector is also under growing environmental pressure. Buildings and construction account for 32% of global energy consumption and 34% of global CO emissions, while cement and steel are responsible for 18% of global emissions. Construction performance is therefore no longer judged only by cost, schedule, and quality, but also by resource efficiency, waste reduction, and lower-carbon delivery.
In this context, AI is becoming increasingly relevant as a decision-support capability across the project lifecycle. Its applications in construction project management cover:
- Cost estimation
- Cost control
- Planning & scheduling
- Delay-risk prediction
- Risk assessment
- Hazard identification
- PPE detection
- Resource allocation
- Safety monitoring
Adoption among construction companies, however, remains uneven. In 2025:
- ~45% reported no AI implementation
- 34% were still in early pilot phases
- Only 1.5% used AI across multiple processes
- Less than 1% had fully embedded AI across the organization
The main barriers are operational rather than conceptual, including shortage of skilled personnel, system integration challenges, and poor data quality.
AI’s role in construction should therefore be understood less as a move toward fully automated
construction and more as a shift toward predictive construction: earlier risk visibility, data-supported pricing, better material planning, and more integrated decisions across cost, schedule, resources, safety, and commercial exposure.
The Predictability Gap in Construction Delivery
A. Inconsistent Measurement Limits Performance Improvement
Across nearly 3,000 construction professionals in the UK, Europe, the Americas, Asia-Pacific, and the Middle East & Africa, construction firms still lack a common way to define and measure productivity.
In practice, this means firms may be tracking different things under the same productivity label: output per worker, output per hour, cost per unit, schedule performance, or progress against plan. No single productivity definition is used by even 30% of firms in any region, limiting the ability to compare performance consistently across projects, markets, or business units.
Benchmarking is also limited. Only 5% of UK and European firms, 9% of Asia-Pacific firms, 14% of Middle East & Africa firms, and 16% of firms in the Americas use benchmarks. The UK shows the weakest measurement discipline, with 22% of firms never measuring productivity, compared with 9% in the Middle East & Africa.
Without a shared measurement basis, firms struggle to identify whether delays come from labor productivity, design changes, procurement gaps, subcontractor performance, equipment utilization, or weak planning.
Improvement then depends on project-by-project experience rather than repeatable management practice.
