We have said it many times before on our blog: the construction sector, long criticised for productivity challenges, fragmented workflows, and slow tech adoption, may be on the cusp of transformation.

The recent RICS “AI in Construction 2025” report further validates us, revealing a fascinating tension: industry professionals are genuinely excited about AI’s potential however real-world uptake remains slow and nascent.

In this blog, we explore AI-readiness within the construction sector, notably, where AI may make the biggest difference, what’s holding the sector back, and how firms might navigate the road ahead.

Why the excitement about AI?

Why are so many in built-environment roles optimistic about AI? Because the challenges construction faces are precisely those AI is suited to address:

• Data complexity and scale: Project ecosystems generate vast, multifaceted data (designs, schedules, sensor feeds, contracts). AI excels in uncovering patterns and insights from messy data.
  
  
• Design & decision complexity: Every project involves trade-offs (cost, materials, carbon, time). AI can help evaluate many options more rapidly and support better-informed decisions.
  
  
• Predictive needs: Forecasting risks, delays, resource bottlenecks, and safety issues is hard with rules-based approaches. Machine learning and predictive models offer a more adaptable toolset.
  
  

In the RICS survey, around 70% of project managers and quantity surveyors believe AI can help them deliver more value. In particular, 40% expect AI’s most significant near-term impact to lie in design optioneering, that is, letting algorithms generate and compare design alternatives rapidly.

Other high-potential domains include scheduling and project planning, predictive risk management, cost control, and smarter integration with BIM workflows.

‍

Impediments  to AI-readiness within Construction

The enthusiasm is there, but then, why are construction organisations moving slower on AI-adoption? The RICS report surfaces a number of structural and cultural barriers with respect to readiness. 

1. Skills shortages
   Nearly half (46%) of respondents flagged the lack of AI or data science expertise within their organisations. Hiring or developing that talent is a strategic necessity but what is actually challenging is that many firms lack a roadmap of what skills are pertinent and how they will evolve in the future. This kind of shooting in the dark is what impedes the first layer of adoption.
   
   
2. Integration challenges
   Like many other traditional industries, the built environment typically relies on legacy platforms, siloed tools, and fragmented workflows. About 37% cited system integration as a barrier. Even a powerful AI tool is only as useful as its ability to connect to project, schedule, and data systems. This digital transformation is time and capital consuming and it often takes years for organisations to implement the changes.
   
   
3. Poor data quality
   AI depends on consistent, clean, structured data, like we mentioned last week, yet 30% of respondents identified data gaps, inconsistencies, or lack of standards as a blocker.

4. Readiness mismatch
   Many firms want to invest in AI, but lack foundational readiness (governance, change management, data maturity). This is not shocking at all, by the way. As something new and innovative erupts, there is usually a lag to frameworks and guardrails towards governance and change management. We believe that this mismatch will change with time but organisations will need to invest on setting up the foundation.

5. Organisational inertia and quality concerns‍

           Construction processes are time-consuming and require a deep level of attention to detail. New tools must prove that they can match up to             the quality firms expect. Additionally, organisational inertia can come from the lack of leadership champions and a culture hesitant to             experimentation.
‍

Because of these constraints, the survey found that 45% of firms report no AI use, while only 1% have managed to scale AI across processes.

‍

Where AI is already making a difference in real projects, it is often in pilot or narrow-scope deployments, for example, detecting hazards via vision systems, or automating specific repetitive tasks. Over time, those pilots can scale and be integrated with broader project control systems.

Bridging the gap: A Roadmap to Responsible Adoption

Here’s a structured way for firms to move from ambition to execution:

1. Start small : pick high-impact pilot projects
   Choose a use case with good data availability, clear ROI potential, and manageable scope (e.g. design optimisation, risk forecasting, high impact workflows that impact business topline). Create a pilot committee that can use this project as a learning ground. Use benchmarks, feedback loops, and performance metrics to assess pilot success. Learn and refine before scaling.
   
   

2. Upskill and build internal capability
   Invest in training, hiring data / AI talent, or partnerships with tech firms. Create cross-functional teams (IT, operations, project) to bridge domain + tech perspectives.
   
   
3. Build data infrastructure & governance
   Perhaps easier said than done but everything behind forms here! Standardise data capture, structure, and quality across projects. Ensure systems interoperate (APIs, data pipelines). Adopt data governance to manage access, privacy, and consistency. In our blog from last week we talk about this aspect at length.
   
   
4. Scale selectively & embed AI
   Expand successful pilots across projects, phases, or geographical areas. Integrate AI tools into project workflows so they become part of “how we work.” This becomes your new normal.
   
   
5. Embed ethical, transparent, and responsible use
   Use frameworks like RICS’s “Responsible Use of AI in Surveying Practice” to ensure fairness, explainability, accountability and public trust. 
   
   

6. Foster industry collaboration
   Like with most other disruptive technologies, the adoption is strengthened in numbers and within an ecosystem. Shared standards, data-sharing protocols, open benchmarks, and collective ethical guardrails will accelerate adoption across the sector.
   
   

A tipping point or gradual journey?

The RICS “AI in Construction 2025” report paints a sector on the edge. Optimism is high, investments are ramping, however, foundational readiness is uneven. The construction industry is not guaranteed to be transformed overnight, but it seems poised for a step change if the right enablers come into play.

For firms that act strategically,  choosing the right pilots, investing in data and talent, aligning cultural incentives, and embedding ethics . AI may become not just a differentiator, but a necessary tool for resilience, sustainability, and competitiveness.

‍