August 2025
If you’ve been in construction long enough to remember when artificial intelligence (AI) first arrived on the scene, you might recall the buzz and speculation. Was it just hype? A passing fad? Or something with real staying power?
Naturally, the industry asked some broad theoretical questions: What could AI do? Would it actually fit into the construction process? Could it help solve—or better yet, prevent—real-world problems?
As time went on, the answers started to come into focus.
As AI began gaining a foothold and proving its value, those early hypothetical questions gave way to more practical ones: How are we using AI? How can we use it in the future? That shift was a clear sign the industry’s view of AI was maturing.
Since then, AI has become far more embedded in construction than many expected, streamlining everything from cost management and workflows to risk mitigation and safety.
For instance, project teams are using large language models (LLM) to generate reports and summarize complex data sets. Natural language processing (NLP) is translating project documentation and reports into usable, actionable insights. Computer vision is visually tracking progress and monitoring safety issues in real time.
While adoption may vary by company, across the board, these tools are already making a measurable impact on project speed, precision and project outcomes.
But here’s the thing: AI’s role in construction isn’t just about how we build, it’s starting to influence what we build.
Rising demand for AI-powered technologies is having a profound effect on capital investment, accelerating the scale and urgency of infrastructure megaprojects, which is an unmistakable indication of its staying power. We’re not just talking multimillion-dollar projects anymore. We’re talking multibillion-dollar megaprojects. And they can’t be built fast enough.
So Where Is All that Investment Going?
First, data centers, the foundation of AI-driven workloads. As industries go through the digital transformation process, they need the critical infrastructure to support it: servers, storage and networking power. A recent analysis by McKinsey projects that the worldwide need for data center capacity could grow 19–22% annually through 2030.
In the U.S., data center construction is active in states like Virginia, Nebraska and Wisconsin. Meanwhile, government-funded AI infrastructure projects are planned in places like Washington and New York. But even with these projects underway, there’s cause for concern that data center construction might not keep pace. Why? It’s straining the existing electrical grids that support energy-intensive data centers and the local labor pool to build them, both currently in limited supply.
Second, semiconductor plants where the chips that make AI possible are produced. These chips power the tools construction teams now rely on, like predictive analytics, real-time monitoring and scenario modeling. Generative AI, in particular, is a major driver behind the demand for high-performance, high-efficiency chips that can handle enormous processing loads.
And it’s not just construction. Those chips help meet the soaring demand across industries and among consumers.
So, pressure is mounting to build more chip plants. Millions of square feet in current projects in Arizona and Texas are encouraging. But, like with the data centers, semiconductor plant construction faces the same energy and skilled labor constraints.
How Is AI Shaping the Infrastructure It Depends On?
You could say AI’s relationship with data centers and semiconductor plants is symbiotic, driving demand for the very infrastructure it relies on to function. (It might sound unconventional, but it matters.)
AI’s effectiveness hasn’t evolved on its own. It’s been fueled in no small part by three factors:
- The increasing availability and comprehensiveness of accurate real-time and historical project data
- More efficient software with more capabilities (especially tools designed specifically for construction)
- Rising adoption of digitalized processes across the industry
These factors haven’t just enabled AI—they’ve grown stronger and more relevant because of it. It’s created a kind of feedback loop that’s transforming how construction operates. AI, in turn, is nudging the industry toward more integrated systems and a better understanding of how to interpret and act on data.
How Are We Responding?
To meet the scale and speed of AI-driven infrastructure demands, construction leaders will have to take a more strategic approach to how projects—especially megaprojects—are executed.
What does that look like? It’s investing in the foundational digital support like data storage and management systems, software and the skilled teams to manage them, all so companies can adapt to the rising expectations that AI brings with it.
But it’s not just about optimizing how we work by incorporating AI into business operations and project workflows. It’s about building a system that supports it.
And that brings us to a new mindset around AI in construction. The industry started off by asking, “What could AI be?” Then it was, “How are we using AI?”
Now the question has become, “What opportunities could AI open up?”
It’s a question that will define how the construction industry adapts and innovates going forward.
The construction industry may still be defining what role AI will ultimately play, but one thing is certain: no matter how the technology evolves, projects will succeed or fail based on the strength of their foundational systems including how well teams plan, track and manage costs.
That’s where Contruent comes in. Its advanced lifecycle cost management platform, Contruent Enterprise, helps organizations build the kind of digital backbone that allows them to adapt to rising demands, whether they’re driven by AI, market shifts or the growing complexity of megaprojects. Learn more or request a demo today.