When building systems speak the same language

Prof. Dr. Cornelius Preidel
Professor of Digital Construction and Building Informatics
Munich University of Applied Sciences

Construction generates vast amounts of data, but much of it is fragmented and unusable. Prof. Cornelius Preidel argues that progress depends on structuring that data, defining clear use cases, and making it usable across systems.

Cornelius: AI will transform building management and help address the industry’s productivity problem. Construction is highly fragmented, which breaks the flow of data. AI agents can turn unstructured data into structured data that software can understand and act on.

Gavin: Why is it so fragmented?

Cornelius: Construction is project-based, not a continuous production line. Each project is different, whether it’s a bridge, a hospital, or a school. The lifecycle spans design, construction, and operation, with many stakeholders involved. Each uses different systems, formats, and terminology, which further disrupts the flow of data. That’s toxic to the golden thread of digitalization.

This is not about blame. Specialists need different tools and methods. But if we used structured data and a shared language, we could unlock much more automation. AI could act as a translator across these systems, like a “Babel fish” for the construction industry.

[The fictional fish from The Hitchhiker's Guide to the Galaxy that, when placed in the ear, instantly translates any spoken language into the user's native tongue].

Gavin: Which ICT technologies will have the most impact on smarter, more energy-efficient buildings?

Cornelius: We should focus less on specific technologies and more on understanding the building as a digital asset. Most owners don’t have a clear picture of the condition of their buildings. We need structured ways to assess damage and performance so we can make better investment decisions.

The biggest opportunity is in operations, the longest phase of a building’s life. You don’t need to digitalize everything. Predictive maintenance can help you get more value from what already exists. Over time, better data allows you to identify patterns, link types of damage to causes, and plan interventions more effectively.

This is also a cultural challenge. You need to define the purpose and use cases first, then decide what data to capture. That’s a human problem as much as a technical one.

Gavin: Are clients enthusiastic, or cautious?

Cornelius: There is excitement, but also uncertainty, especially in the public sector, where the scale of the problem can feel overwhelming. It’s like that old saying about how to eat an elephant: you have to slice it into thin strips of elephant carpaccio, and eat them one at a time.

A common mistake is to assume AI will solve everything without structured data. It won’t. More data does not automatically mean better decisions. You need to define your objectives, decide what matters, and measure progress. AI can support that process, but humans still need to lead it.

Gavin: What skills should we be teaching?

Cornelius: Everyone involved in construction needs the tools to participate in a digital workflow, not just architects and engineers. Site workers will also need to use digital tools, even if they don’t need to understand the underlying data structures.

That means building digital literacy, including how to use tools, interpret information, and understand the consequences of decisions. These baseline skills are essential.

Gavin: Are you confident that will happen?

Cornelius: There is no single coordinated approach, and education systems vary. But frameworks are emerging. For example, the EU has defined broad digital competencies for citizens and workers, including data literacy, problem solving, and understanding data security and cloud systems.

Gavin: How should technology providers and industry partners contribute?

Cornelius: Collaboration between academia and industry is essential. It ensures research is grounded in real problems and that solutions are usable.

For example, we are working on methods to assess a building’s condition in a way that non-experts can understand. A building owner does not need engineering detail, but they do need to know the condition of the building and when to call in a specialist. The goal is to connect real problems with practical solutions.

Gavin: Are there gaps between research and industry?

Cornelius: Yes. The industry invests relatively little in R&D. It is focused on short-term project delivery, which makes long-term innovation difficult.

Unlike media, retail, or banking, construction has not been fundamentally disrupted. Processes such as procurement and contracting remain largely unchanged. There are some innovative projects, but they are still the exception.

Gavin: What will drive greater investment in technology?

Cornelius: Competition. We are starting to see more standardized and repeatable approaches to building and refurbishment. Companies that adopt these methods can deliver faster, cheaper, and at higher quality.

That creates pressure on others to follow. But the starting point is not technology. It is understanding the problem and defining clear use cases. Only then should you look at automation or AI as tools to address those needs.

Digitalization is a tool you can use once you’ve identified the pain points. That's what will help you digest the elephant.