by Lilian Ho, Programme Director of the world’s first MSc in Artificial Intelligence for Architecture and Construction at ZIGURAT Institute of Technology, Women in BIM Regional Lead and Women in BIM AI Ambassador.

There is so much attention on AI in construction at the moment that it is easy to focus on the visible things. The tools. The platforms. The new features. The speed.

But I actually think the more important conversation is about what sits underneath all of that.

In construction, we understand the importance of foundations. You may not see them, but everything depends on them. AI is much the same.

From what I have seen, organisations do not struggle with AI because the technology is not advanced enough. More often, they struggle because the basics are not ready. Information is fragmented. Teams work in different ways. Standards are inconsistent. Data sits across multiple systems and is not always easy to trust.

Then AI is introduced on top of that, with the expectation that it will somehow create clarity. It usually does not.

If the information going into a system is incomplete or unreliable, AI can simply help us reach the wrong answer more quickly.

That is why I do not see BIM, information management, digital strategy and AI as separate subjects. They are connected.

BIM gives us structured information. Information management gives us consistency and control. Digital strategy gives us direction. AI can then help us do more with all of that.

In many ways, AI is making the less glamorous parts of digital construction more important.

Standards, naming conventions, Common Data Environments, ISO 19650, data quality and clear information ownership may not attract the same attention as generative AI, but they are part of what makes AI useful. The same applies to governance.

AI can process large amounts of information very quickly, but that does not mean every answer should be accepted.

If a system recommends a design option, highlights a risk or generates technical content, someone still needs to understand whether that answer makes sense.

Who checks it? Who is accountable? When should a human step in?

Those questions are important in every industry, but particularly in construction, where decisions can affect safety, compliance, cost and asset performance for many years.

I also think this is where the role of the professional becomes more important, not less.

There is a lot of discussion about learning AI tools, and of course that matters. But knowing how to use a tool is only one part of it.

The real skill is knowing when to question it.

Can you recognise when something looks convincing but is wrong? Can you see what information is missing? Can you challenge an output even when it arrives quickly and appears authoritative?

Through my work in education at ZIGURAT and with professionals internationally, I have found that the people who adapt best are not always the most technical. They are usually the ones who ask good questions, understand the wider context and are comfortable using professional judgement.

For me, that is what AI literacy really means. It is not about becoming an AI expert. It is about becoming a better informed professional in a world where AI is increasingly part of everyday work.

Leadership is another part of this that I think is often underestimated.

AI is still too often treated as an IT initiative.

I do not think it is. It is an organisational change.

If a business has unclear processes, disconnected teams and poor information, adding AI will not suddenly make the organisation more intelligent.

Leadership has to decide what problem is actually being solved, where AI adds value and what responsible use looks like.

It also has to create the right environment for people to experiment, learn and challenge.

That last part is important.

We should not create a culture where questioning AI is seen as resistance to innovation. Sometimes questioning the output is exactly what good professional practice looks like.

And then there is trust.

People will only use AI properly if they trust the way it is being introduced.

That trust does not come from impressive demonstrations or faster outputs. It comes from knowing where the information comes from, understanding the limitations and being clear about who remains responsible for the final decision.

This is why I continue to believe strongly in human-centred AI.

The goal should not be to remove people from decision making. It should be to help them make better decisions.

AI can process information, identify patterns and test scenarios at a scale we cannot match. But judgement, context, ethics and responsibility still sit with us.

So while the industry is understandably asking what AI can do next, I think there is another question we need to ask first:

Are the foundations strong enough to support it?

The organisations that get the most value from AI will not necessarily be the ones using the most advanced tools. They will be the ones that have already done the harder, quieter work around information, governance, skills, leadership and trust.

Those things may not be very visible.

But in construction, we already know that the things we cannot see are often the things holding everything else up.