When we talk about the physical infrastructure behind artificial intelligence, the conversation tends to go quickly to data centers. And understandably so. AI is driving enormous demand for computing capacity, power, connectivity, and the facilities needed to support all three. But I think there is a much bigger real estate conversation starting to take shape.
As companies work AI into more parts of their businesses, the infrastructure needed to support it will extend well beyond the data center. It will reach offices, corporate campuses, manufacturing facilities, and other workplaces. At the same time, AI could change who is working in those spaces, what kind of work they are doing, and ultimately how much and what type of real estate organizations need.
For corporate real estate (CRE) leaders, those changes are going to be difficult to separate. AI may be a technology strategy, but it is increasingly a real estate issue, too.
AI is complicating long-term portfolio planning
Real estate has always required organizations to make decisions well ahead of certainty. Buildings are long-term assets, leases can span years, and major capital projects take time to plan and execute.
AI adds another layer of uncertainty because some of the assumptions behind those decisions are now changing very quickly.
CoreNet Global and Colliers’ 2026 CRE at an Inflection Point research found that 51% of corporate real estate professionals globally identify AI and automation as the most significant force shaping CRE. The industry clearly recognizes that something significant is happening. The harder question is what to do about it.
A 2026 JLL survey of more than 2,200 C-suite executives and CRE leaders found that 78% expect AI to significantly affect their portfolio strategies and CRE functions over the next three to five years. Yet only 15% have moved beyond exploration and initial deployment to actively optimizing AI within their real estate operations.
That leaves CRE teams in a difficult position. They are making decisions about buildings and portfolios that may be in place for a decade or longer while the technology influencing their workforce and operations is changing by the month.
No one has a clear enough view of where AI is taking the workforce to design a portfolio around one predicted outcome. The more practical response is to build in enough flexibility to adjust as those changes become clearer.
That might mean more adaptable space, shorter planning horizons in certain parts of the portfolio, better data about how buildings are actually being used, or capital strategies that leave room for changing technology requirements. The right answer will look different for every organization, but flexibility itself is becoming more valuable.
What does an AI-ready workplace actually need?
For years, connectivity and technology infrastructure have been part of workplace planning. AI is making them more consequential.
As employees rely on more AI-enabled applications and organizations connect more workplace and building systems, the line between digital infrastructure and physical infrastructure gets harder to draw.
Network capacity, cybersecurity, data architecture, reliable power, sensors, building management systems, and workplace technologies all come into the picture. So does the less glamorous work of making sure those systems can communicate with one another and produce information that is actually useful.
This changes some of the questions CRE teams need to ask when evaluating a building.
Location, cost, amenities, and the physical space itself obviously still matter. But organizations also need to understand whether a building can support where the business is headed technologically. Power capacity or connectivity may not have historically carried the same weight as rent or location in every real estate decision. That calculation could begin to change.
There is also the question of what happens several years down the road. A building may meet an organization’s needs today but require significant investment if technology demands increase. Understanding that risk earlier can lead to very different decisions about leases, acquisitions, renovations, and capital spending.
The harder problem may be the buildings we already have
It is relatively easy to talk about designing the workplace of the future from scratch, but most CRE leaders do not have that luxury.
They are managing portfolios full of existing buildings, each with its own infrastructure, limitations, lease terms, and investment history. Making those portfolios ready for a more technology-intensive future is a different challenge entirely.
Some buildings will be relatively straightforward to upgrade. Others may require substantial investment in power, connectivity, building systems, or data infrastructure. In some cases, the economics of making those improvements may eventually factor into whether an organization stays in a building at all.
I don’t expect older buildings to suddenly become obsolete because of AI. But technology requirements could create another meaningful point of differentiation between assets.
A building can still be perfectly functional in the traditional sense and yet become more difficult or expensive for an occupier to adapt to new ways of working. CRE teams that understand those vulnerabilities now will be in a much better position when they are making future lease, capital, consolidation, and site-selection decisions.
There is a human side to this infrastructure conversation
One thing that can get lost when we talk about AI infrastructure is the employee. AI is not only changing the tools people use. It is likely to change the work itself.
If more routine tasks become automated, some jobs may put greater emphasis on judgment, collaboration, and problem-solving. We don’t yet know exactly how far that shift will go, but it could have important implications for the role of the workplace. This is why I would be cautious about defining an “AI-ready office” as one filled with more technology.
Technology should make the workplace work better. AI-enabled workplace platforms, occupancy data, and smart building systems can give organizations a much better understanding of how people are using space and how buildings are performing. But installing those systems is not the end goal.
The useful outcomes are much more practical: helping employees find the right space, making collaboration easier, operating buildings more efficiently, identifying underused space, and giving leaders better information before they make portfolio decisions.
Sometimes technology will be the answer, and sometimes the data may tell a CRE team that the workplace itself needs to change.
CRE needs to be part of the AI conversation earlier
AI strategy is typically led by technology teams and business leadership, so corporate real estate may not be among the first functions brought into those discussions. There is a case for changing that.
A decision that starts as a technology or workforce decision can eventually become a real estate decision. Changes in headcount affect space requirements. Technology investments affect building infrastructure. New working patterns influence workplace design. Increased digital demands can shape operating costs, capital plans, and even where a company chooses to locate.
CRE leaders don’t need to become AI experts or engineers, but they do need enough visibility into what their organizations are planning to anticipate the physical consequences.
That means working more closely with IT, HR, finance, operations, and business leaders, and asking questions that may not historically have sat within the real estate function. How might AI change the workforce? Which teams or locations could be most affected? What will employees need from their workplaces that they don’t need today? Which buildings could require significant investment to keep pace?
Waiting until those answers become real estate problems is likely to be much more expensive than joining the conversation early.
This conversation is getting much broader
I’ll be hearing many of these questions firsthand when corporate real estate leaders gather at CoreNet Global’s EMEA Summit in Munich this September. What’s interesting is that AI isn’t sitting neatly in one technology conversation. It is showing up in discussions about workplace strategy, portfolios, resilience, leadership, infrastructure, and the future of work. That is probably a better reflection of what companies themselves are experiencing.
The data center boom remains the most visible real estate story associated with AI, and it will continue to be a significant one. But it shouldn’t distract us from what is beginning to happen across the rest of the corporate portfolio.
An AI infrastructure strategy doesn’t mean trying to predict the next breakthrough or upgrading every building in anticipation of needs that may never materialize. It means understanding where the organization’s technology strategy intersects with its buildings, people, and portfolio—and making sure CRE is part of that conversation early enough to do something useful with the information.
We don’t know exactly what an AI-enabled workplace will look like five or ten years from now, and that’s exactly why the real estate decisions being made today need to leave some room for what comes next.
