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Building in the digital age
The digital economy is changing what construction must deliver.
Everyday life depends on a physical network: data centres, smart infrastructure, digitally connected workplaces, hospitals, schools and homes.
These assets are becoming more complex. They need more power, cooling, resilience, controls, security and specialist systems. This has implications for cost, programme, procurement and long-term performance.
The 2025 Construction Certainty Index found that organisations using AI were more confident in delivering project goals. Their advantage did not appear to come from technology, but from their mindset and how they brought technology, insight and judgement together.
This year, the industry is moving at different speeds. Uncertainty has accelerated digital adoption for 35% of respondents but slowed it for 40%.
AI is already part of that shift. 55% see it as one of the most effective technologies for mitigating uncertainty. Data analytics follows at 49%.
Technology is changing the assets being built too. 58% say AI and digital integration are already influencing the design and long-term operation of their buildings.
The next step looks practical. Respondents expect AI to be used most for cost estimating and forecasting, design optimisation, project scheduling, supply chain optimisation and risk modelling.
“The digital age is raising expectations of buildings and the teams that deliver them. Data and technology need to work together faster so clients can understand risk and test scenarios. This allows them to compare the best ways to design, build and operate assets before key decisions are locked in.
But the quality of the insight depends on the quality of the data behind it. The true value only comes when better information changes the decision early enough and quickly enough to protect cost, programme and long-term performance.”
— Paul Fitch, Global Tech Sector Lead, Currie & Brown
see AI as one of the most effective technologies for mitigating uncertainty
say AI and digital integration are already influencing their design and long-term operation
Data is the barrier
Poor data is one of the biggest barriers to getting value from AI in construction projects.
39% of respondents struggle to integrate multiple data sources. 37% point to inconsistent or incompatible formats, while the same proportion cite limited access to real-time data.
The challenge grows with scale. Among organisations with construction pipelines of $25bn or more, 53% say integrating multiple data sources is making it hard for them to make full use of AI.
Yet larger, more complex pipelines are often where AI could have the greatest value. It can help teams understand exposure, model scenarios and make better decisions across whole programmes.
Without the right data foundations, AI may produce more outputs, but not necessarily more certainty.
“AI can support construction, from productivity metrics to schedule analysis. But the question is how you capture the data accurately enough. Whatever AI gives you is only as good as the data you put in. There is real opportunity, but the industry is still early in working out how to use it in the way people expect.”
— John Kutcher, Programme Manager, EPC - Data Centres, SLB
Adoption alone will not close the confidence gap
AI cannot fix poor data. Nor can technology make decisions for people. Its value depends on whether teams trust the information, understand what it means and can act on it early enough.
Cost, trust and a lack of internal skills are still holding adoption back. But fragmented, inconsistent or outdated information creates a more basic problem: teams cannot rely on the insight they receive.
The skills challenge is changing as a result. Construction still needs people on site. It increasingly needs people who can manage data, understand digital systems and turn insight into action too.
Human judgement remains central. 78% of respondents say human expertise is important or essential in critical risk decision-making. The opportunity is to bring technology, data and judgement together around the decisions that shape delivery. These range from forecasting cost and securing supply chain capacity to improving asset performance.
Digital is changing both what construction delivers and how it delivers it. Used well, technology can help teams see risk earlier, test options faster and make better-informed decisions. Without the data, skills and judgement behind it, it risks becoming another layer of noise.
Poor data is one of the biggest barriers to getting value from AI.
Barriers to scaling AI adoption for construction projects
“AI has the potential to help clients quantify risk and reduce uncertainty. But the issue the industry still has to crack is the lack of structure and consistency, particularly in programme or schedule data, and at a global level in cost data. The challenge is arguably less about the tools used to interrogate the data as it is about the data itself.
Strengthening that foundation is what will allow AI to make the difference people expect: making emerging risks easier to anticipate, testing assumptions faster and giving teams greater confidence in the decisions they make.”
Nick Gray
Chief Operating Officer, UK and Europe
Currie & Brown
Case study
Turning digital demand into delivery certainty
Data centre clients are under pressure to move fast, but speed creates uncertainty. Costs shift, designs evolve, supply chain capacity changes and global standards must be delivered consistently across different markets.
For a hyperscale data centre client in the US, we helped turn programme data into intelligence for decision-making. Benchmark costs were kept up to date as the market and design changed. Data visualisation allowed the client to compare data across projects, set budgets and agree maximum contract prices with stronger evidence. These tools became key to how the team managed cost while keeping up with rapid growth.
In Malaysia, the challenge was more local. A global technology client needed to deliver a major data centre in Cyberjaya, where tight deadlines, contractor capability and global standards all had to be managed at the same time. We helped translate the client’s requirements, tailored global standards to the Malaysian market, monitored progress closely and flagged risks early so the team had more time to respond.
Together, these examples show how certainty can be built into fast-moving digital infrastructure. Data makes programme-level uncertainty visible. Local insight makes global requirements deliverable. Strong project controls connect the two, helping clients keep pace with growth without losing control of cost, schedule or risk.