Under Construction: A Week in Tech & Innovation
In construction simplicity is a survival trait.
What I’m thinking
Here’s something to consider:
The more complex the solution, the more likely it is to fail.
With BIM, it’s easy to spiral through fractal after fractal into a cacophony of complexity. You might even land on the right answer — but only for that exact moment in time.
Here’s the issue: construction projects aren’t perfect. They’re not static. They’re dynamic beasts — shifting, squirming, and transforming over time.
They’re complex systems-of-systems and networks-of-networks. And complexity doesn’t solve for complexity.
What we end up with is fragility — driven by static solutions that demand mountains of time and effort just to “keep updated.”
Instead, we should ground ourselves in a few simple truths — ones most of us can agree on:
It’s good to agree on what we call things, so we can find them again though multiple paths.
It’s good to establish relationships between data, information, and entities.
It’s good to coordinate our work to see, avoid, and remedy issues before construction begins.
It’s good to understand what information needs to be developed — both to satisfy the end-user and to support design and construction.
It’s good to know, from the outset, the skill and effort required to deliver that information — so we can resource and price it effectively.
Simplicity isn’t naive. In construction, it’s a survival trait.
What I’m consuming
I’m constantly scanning the news, social media, and industry for case studies, innovations, and generally interesting construction related content. In these very brief articles my goal is to share these with you.
Replacing 1200 SaaS apps with AI and Knowledge Graphs
Data fragmentation is a problem that all large projects and enterprises face — and it’s often a hidden danger. With so many different point solutions handling specific problems, there’s a non-zero risk of data falling between the gaps or losing context across silos.
That’s where knowledge graphs (KGs) come in. By managing relationships between fragmented elements, they help navigate these hidden dangers. Not only that, but through GraphRAG, KGs can provide LLMs with the structure needed to avoid confusion and hallucinations.
Fintech company Klarna discovered its fragmented silos, and after embracing AI, began deprecating existing platforms — 1,200 of them, including Salesforce — thanks to the solutions found when combining KGs and LLMs.
This offers an amazing insight into what the future may hold for data and solution architecture in the construction industry.
“Build it, don’t buy it” AI and data
From the New York Build conference, this article discusses the challenges the construction industry faces in using the vast amounts of data it generates, and how AI can help. According to industry experts at the conference, much of the data is disorganised and not standardised, leading to inefficiencies. This is the struggle, and why simply buying off the shelf tools is not the solutions to bring AI capability to construction organisations and projects. The companies best placed for the future are those who are building in-house capability and expertise.
Panelists suggested that construction firms can use AI to validate data, automate workflows, and improve decision-making. The advancement of robotics and reality capture tools were also highlights as improving efficiency and data capture on sire. While these tools offer solutions, mass adoption faces hurdles as it requires commitment from all stakeholders to implement and maintain them.
Cove: the first AI-powered architecture firm
The emergence of Cove Architecture, touting itself as the first AI-powered full-service architecture firm, could be a significant moment in the evolution of the construction and design industries. The promise is compelling - to fuse architectural artistry with the precision and efficiency of artificial intelligence.
Cove's foundation rests on its proprietary AI framework, Vitras.ai and ARK_BIM, a decade-long, $25 million investment. The company's claims of substantial improvements in design efficiency, cost estimation accuracy, and reduced design iteration expenses are certainly eye-catching. The reported success of their initial Atlanta project lends credence to these assertions.
However, several points warrant closer examination:
The Reality of AI Integration:
While AI's potential in streamlining workflows and optimising design is understood, the architectural process is inherently complex and nuanced. True integration requires more than just algorithmic precision; it demands an understanding of the human element, including client needs, aesthetic considerations, and the often-unpredictable nature of construction.
It will be very important to see how well the AI deals with the unexpected, and the very human need to make changes during the building process.
Data Dependency:
AI's effectiveness is contingent upon the quality and quantity of data it processes. The construction industry, as noted above, often grapples with data fragmentation and inconsistencies. Cove's success will hinge on its ability to effectively manage and leverage data.
The Human Element:
The article highlights AI's role in augmenting, rather than replacing, human expertise. This distinction is crucial. Architects bring creativity, intuition, and contextual understanding that AI cannot (yet) replicate. The ideal scenario is a synergistic partnership, where AI empowers architects to make more informed and efficient decisions.
Industry Transformation:
The wider architectural community should be watching to see how Cove affects the industry. If the claims hold true, this kind of AI implementation could cause a large disruption in how buildings are designed.
Cove Architecture represents a bold experiment, one that could potentially reshape the future of architectural practice and construction design. Whether it fulfills its promise remains to be seen. However, its emergence underscores the growing influence of AI in traditionally human-centric fields.