AI in construction: Why the industry needs to stop treating it as a standalone tool

Artificial intelligence is rapidly becoming part of everyday business operations across the construction sector and organisations are increasingly experimenting with AI-powered solutions. From advanced use cases like predictive maintenance and contract analysis, to those still grappling with where it fits into their operations, the industry is entering a phase of adoption. But as time goes on, the industry may be approaching AI the wrong way if it continues to be treated as a standalone capability, rather than a structural part of how modern construction businesses operate.

Our chief technology officer, Dave Stott shares his perspective.

For the construction sector, a recent survey from the Association for Project Management revealed that AI use in project management nearly doubled in just two years. What is becoming clearer is that the challenge isn’t access to AI technology, but rather how to approach implementation.

The real opportunity lies not in deploying AI to solve isolated problems such as automating reports, meeting notes, or improving scheduling for example. These applications can deliver measurable benefits, but focusing solely on isolated tools risks overlooking AI’s much bigger potential.

Moving beyond the “tool” mindset

One of the biggest wrong turns a business can take at the start of its journey is to treat AI as a single-purpose tool. A more accurate comparison is to the internet itself. It’s an enabling layer that, when properly embedded, quietly enhances productivity across everything – from the administrative uses already mentioned through to technical and design functions and project delivery.

When companies treat AI in silo, organisations only really scratch the surface of its potential. Sometimes this comes with dedicated teams, ringfenced budgets, and sometimes even Chief AI Officers. On the surface, this may seem like a logical step, but in practice it keeps AI isolated from the very processes it can improve.

Drawing on the parallel to the internet, it would seem laughable today to appoint a Chief Internet Officer and treat it like its own department. The internet is a vital piece of infrastructure that allows businesses to function, just like electricity. The same should ultimately be true of AI, taking it out of silo and into the core business structure.

The construction industry already faces significant challenges around fragmented processes, disconnected data and complex stakeholder relationships. Creating another silo risks adding further complexity. The shift of perspective to moving away from this isolated approach and rather embedding AI into the fabric of operations is critical.

From a novelty to a core business capability

One of the most common patterns across industries is the tendency to treat AI as an innovation and novelty project. Businesses launch proof-of-concepts and test new applications and early results are often promising. However, AI’s greatest value is rarely realised through short-term implementation. It’s something that compounds value over time, so having AI specialists within a business can be powerful, but they should be working to nurture this growth business wide, not manage something in a vacuum.

This means that AI should become part of the organisation’s long-term strategy, integrated into their structure, enabling it to align with everyday operations and wider business objectives.

That doesn’t, however mean replacing people – especially in the construction sector that is so heavily built on relationships. What AI can do is create the efficiencies that allow people to focus more of their time on high-value activities that drive outcomes for clients, communities and projects.

It’s a well-known saying from Bill Gates that applying automation to an efficient operation will magnify the efficiency, but the opposite is also true. Humans have been at the centre of established processes, which now need to shift towards something that’s now designed with ‘machines’ in mind. The capabilities are similar, but different, so embedding AI into existing workflows will probably require redesigned thinking.

The data challenge facing construction

Data remains one of the biggest barriers to effective AI implementation within the construction sector. Most organisations have significant volumes of information, but it is often spread across different systems. Without reliable and accessible data, even the most sophisticated AI tools will struggle to generate valuable insights.

Addressing this challenge requires investment beyond technology itself. Businesses need stronger data governance, greater standardisation and improved collaboration across supply chains.

There are huge efficiencies to be found in strong, industry-wide data warehouses. For example, across the Pagabo ecosystem, an abundance of data on projects procured through the frameworks we manage for contracting authorities can be found. This allows for lessons learned that are invaluable when it comes to public sector spend, impact on local economies and outcomes for communities. Imagine the benefits that could be unlocked in project delivery and social value generation by learning from the whole industry, not just siloed experiences

Building trust and top-down leadership

A cultural shift is also needed. Teams need to have confidence in AI-driven outputs and using them in day-to-day decision making. For many organisations, the challenge is less about technical capability and more about usability and training.

And it is leadership that will play a crucial role in making this happen. Transitioning AI from experiment to infrastructure needs a long-term strategy that looks beyond quick wins.

The process will not be simple, but the potential benefits to be uncovered are huge. AI offers an opportunity not just to work faster, but to completely change how a business operates.  And for an industry facing ongoing pressures – many outside of its own control – these gains aren’t just desirable, they are essential to futureproofing.

A useful measure of success may be when businesses stop talking about AI altogether and it eventually become so embedded in daily life that it becomes ‘unremarkable’. Few people think about the complex engineering behind the water that flows from our taps or compliments a sat nav for getting us to our destination, they simply expect them to work.

The same applies here. When AI is implemented right, we’ll simply be able to rely on it operating in the background, enabling better outcomes without requiring constant attention.

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