Robotics and autonomy are entering construction sites — but slowly

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In an era increasingly dominated by AI and automation, it’s still incredible just how much construction work remains manual. The contrast is most stark in data centers – facilities built to power cutting-edge technology, but whose delivery is generally still slowed down by major fragmentation and time-consuming manual tasks.

One of the industry’s biggest challenges seems to be capturing and understanding what’s actually happening on a project. Project managers can spend countless hours walking sites, checking completed work and coordinating contractors operating to different schedules. When documentation falls behind, small issues can go unnoticed and eventually develop into costly delays.

While the industry is finally starting to get to grips with this technology, construction is presenting own on challenges. Autonomy works best within fixed parameters and with a limited number of variables, but live sites offer the opposite – changing plans, moving materials, new structures being built and multiple trades working alongside each other.

Construction is where automation fails – is now the turning point?

These are the exact conditions that have made automation ineffective in construction. But automation isn’t impractical or impossible, it just means vendors will need to focus on the tasks where machines can deliver the best results.

Progress capturing, side documentation and routine inspections are some of the areas where automation could work best, and better still, companies like OpenSpace argue much of this work can actually be done outside of normal operating hours to both reduce disruption, and to reduce exposure to a live and dynamic environment.

Data centers could be a good proving ground for this, with large floor plans, repeatable layouts and intense schedule pressures. With up-to-date data from overnight checks, for example, leaders could assess progress and identify areas that need extra attention.

But collecting that data is only the first step, because the AI running behind the scenes need to be able to interpret site imagery to cross-reference it with site plans, drawings, schedules and other information held across other systems.

In this Q&A, OpenSpace CEO Jeevan Kalanithi explains why construction automation is starting to gain momentum, why ‘good enough’ may indeed be good enough without needing immediate perfection, and how automated monitoring could actually help soften the blow of ongoing labor shortages.

Construction has traditionally been one of the least automated industries. Why is that beginning to change now?

I actually think the idea that construction is slow to adopt technology is a bit of a misconception. Builders adopt tools that genuinely make their jobs easier – they've just been waiting for technology that understands how they actually work.

Construction is fundamentally different from industries like manufacturing. Every project is unique. Jobsites change every day. Teams are working in environments that are constantly evolving, with dozens of trades operating simultaneously. That's a much harder environment to automate than a factory or warehouse where conditions are highly controlled.

For a long time, most construction software focused on documents, schedules and reports because that's what computers could understand. But construction isn't really a document problem – it's a physical-world problem. The most important information lives on the jobsite itself: what's been built, what's changed, where work is progressing and where risks are emerging.

What's changing now is that AI is becoming capable of understanding the physical world. Instead of asking people to manually document what's happening, AI can interpret images and other real-world data to understand the state of a project. That makes robotics and automation much more practical because they fit naturally into how builders already work instead of forcing them to adopt entirely new processes.

Robotics in construction has been discussed for years but has seen relatively limited adoption. What factors are contributing to the increased interest in autonomous and semi-autonomous systems on jobsites today?

The conversation has become much more practical.

Ten years ago, people asked whether robots would replace construction workers or build entire buildings autonomously. Today the question is much simpler: How can robots help experienced builders work more efficiently? That's an important shift because construction has always adopted tools that solve real problems.

The biggest opportunities today are around repetitive, time-consuming tasks like documenting progress, capturing site conditions or performing routine inspections. Those activities are incredibly valuable, but they're not necessarily the best use of a superintendent's or project engineer's time.

I think of robotics a bit like the introduction of nail guns. Nail guns didn't replace carpenters – they helped carpenters work faster and more consistently. Robotics is following a similar path. The goal isn't to automate construction. It's to automate specific tasks that allow skilled professionals to spend more time coordinating work, solving problems and making decisions.

We're already seeing that with the robotics companies that we integrate with, each approaching different aspects of autonomy. Rather than trying to solve every problem, they're proving that robots can reliably perform specific tasks that create immediate value on today's jobsites.

Data centers and other large-scale projects have been cited as promising early use cases for construction robotics. What characteristics make those environments more suitable for autonomous technologies?

Data centers are actually a great example of where robotics and AI can demonstrate value today because they combine three characteristics that work well for autonomous systems.

First, they're relatively structured environments. Compared to a renovation project or an occupied hospital, data centers typically have large floor plates, repeatable layouts and fewer unexpected obstacles, making them easier for robots to navigate.

Second, they're incredibly schedule-sensitive. AI infrastructure is expanding at an unprecedented pace, and every day matters. Owners and contractors need continuous visibility into progress because hundreds of activities are happening simultaneously.

Finally, they're highly repetitive. The same systems and construction sequences occur over and over, which allows robotics to operate more consistently and makes it easier to measure progress over time.

Autonomous data capture is particularly valuable in these environments because it creates a consistent visual record without disrupting work during the day. That gives project teams objective information about what's actually happening on site, helping them identify issues earlier, coordinate more effectively and keep stakeholders aligned.

Construction sites are highly dynamic environments. What are some of the biggest technical and operational challenges robots face when deployed on active projects?

Construction sites are probably one of the hardest environments you could ask an autonomous system to operate in.

Unlike a warehouse, where everything is designed to be predictable, construction sites change constantly. Materials move. Equipment gets relocated. Walls appear. Doors that were open yesterday might be closed today. You also have dozens or sometimes hundreds of people working alongside autonomous systems, so safety has to remain the highest priority.

There are practical challenges as well. Connectivity isn't always reliable. GPS often doesn't work indoors. Navigation has to account for changing layouts and temporary obstacles that don't exist in more controlled environments.

That's why I think we'll continue seeing supervised autonomy for quite some time. Humans are still remarkably good at adapting to unexpected situations, and construction has plenty of them.

One lesson we've learned is that "good enough" often beats "technically perfect." Builders don't need the most sophisticated robot, they need one that's reliable enough to show up every day, operate safely and consistently, and fit into the way projects already run.

As robotics adoption grows, what role does visual intelligence play in helping machines understand and navigate the physical world?

Whether imagery comes from a person carrying a 360° camera, a robot, a drone or another autonomous system, collecting the data is really only the first step. The real challenge is understanding what that data means.

AI has become remarkably good at understanding language, but if it's going to operate in the real-world economy, it also needs to understand physical places, physical assets and physical work.

Construction has always had information about what was supposed to happen – drawings, BIM models, schedules and specifications. What's historically been much harder is understanding what actually happened in the field.

That's where visual intelligence comes in. AI can compare what's been built against what was intended to be built, measure progress over time, identify potential issues and surface insights that help project teams make better decisions.

Our philosophy is simple: we don't really care how the data gets collected. Whether it comes from a phone, a 360° camera, a drone, a laser scanner or a robotics platform from one of our robotics partners, our goal is to bring that information together into a common understanding of the jobsite.

Today, our customers have captured imagery across more than 70 billion square feet of construction. That scale creates an opportunity not only to help builders today, but also to help train and validate the next generation of AI systems that need to understand the physical world.

There's often concern that robotics could replace workers. How are builders currently thinking about the relationship between automation and the existing construction workforce?

I think that's a very understandable concern, but it doesn't reflect what we're seeing on jobsites.

The builders we work with aren't trying to replace experienced people. If anything, they're trying to figure out how to help those people do more.

Construction has faced labor shortages for decades, and demand continues to outpace the available workforce. There simply aren't enough skilled people entering the trades to meet the amount of work that needs to get done. That's especially true as we build more data centers, manufacturing facilities and infrastructure.

Robotics and AI help address that challenge by taking on repetitive work like routine documentation, progress capture or inspections, allowing experienced professionals to spend more time coordinating work, solving problems and applying their expertise.

The best technology doesn't replace people – it amplifies what people are already good at. That's how I think construction will continue to adopt AI-powered tools and robotics. They become another set of tools that help experienced teams make better decisions, work more efficiently and get more done with the resources they already have.

The concept of "human-in-the-loop" robotics is gaining attention across industries. How does that approach apply within construction environments?

I think human-in-the-loop is going to be the dominant model for construction for quite some time.

Construction is simply too dynamic to expect fully autonomous systems to handle every situation. Something unexpected happens every day – a blocked corridor, a relocated piece of equipment, a new safety barrier, an area that's suddenly inaccessible. Humans are still exceptionally good at recognizing those situations and adapting in real time.

What we're seeing today is a very practical division of responsibilities. Robots handle routine, repetitive tasks consistently, while people provide judgment, context and intervention whenever it's needed.

Some of our robotics partners are already operating this way, where autonomous systems perform most of the work but remain remotely supervised so a person can step in if something unexpected occurs.

I don't think that's a compromise. I think it's actually the right model. The goal isn't autonomy for its own sake. The goal is giving project teams better information while maintaining the flexibility and judgment that complex construction projects require.

Some contractors are experimenting with autonomous data capture during off-hours, such as overnight or before crews arrive. What lessons are emerging from those early deployments?

The biggest lesson has been consistency.

One of the challenges with manual documentation is that it depends on people having the time to do it. On a busy project, it's easy for documentation to become a lower priority because everyone's focused on solving immediate problems.

Autonomous capture changes that. Robots can document the site on a predictable schedule, often overnight or before crews arrive, creating a consistent visual record every day without interrupting construction activities.

That consistency gives teams a much clearer understanding of how a project is progressing over time. It also makes documentation much more resilient. If a superintendent is tied up or a project engineer is out that day, the visual record doesn't stop. Everyone still has access to current information about what's happening on site.

Capturing a visual record has always been a natural byproduct of walking a site with a 360° camera. It's also a very natural task to hand to a robot. That allows experienced people to spend more of their time interpreting information and making decisions instead of simply collecting data.

Looking ahead, what indicators should the industry watch to better understand whether construction robotics is moving beyond pilot programs and toward broader adoption?

I don't think the biggest indicator will be the number of robots on jobsites. It will be whether contractors keep using them after the pilot is over.

Construction is a very practical industry. Builders don't adopt technology because it's exciting, they adopt it because it saves time, reduces risk or helps projects run more smoothly. If a technology creates more work than it eliminates, it won't last.

We'll also know the industry has reached the next stage when robotics becomes just another way of collecting project information. Builders shouldn't have to think about whether data came from a person, a robot, a drone or another autonomous system. They should simply have access to an accurate, up-to-date understanding of what's happening on their projects.

Ultimately, I think the story is bigger than robotics. The real-world economy doesn't run on documents alone. It runs on physical places, physical assets and physical work. If AI is going to create value there, it has to understand reality, not just language.

That's why we say agents need eyes. When AI can reliably see, understand and reason about what's happening in the physical world, robotics becomes much more than automation. It becomes a new way for people to interact with the built environment and make better decisions. I think that's the transition we'll be talking about over the next decade.