It’s fair to say that the commercial property world hasn’t been slow to experiment with AI. Last year JLL found that almost 90% of investors, owners and landlords said they were piloting AI uses, alongside the same proportion of occupiers. Not surprising considering the potential for it to process and augment the data that makes our industry tick is vast.
Where it has been slow, however, is in building out these early experiments into usable tech. So much so that we’re not just delaying innovation, we’re missing an important evolution.
Our industry is one of the most conservative and traditional, and it hasn’t historically been known for its speed and digital adoption. It’s long involved manual processes and is very much a world that still favors an in-person approach (no criticism there). This could also be its strength, but the downside is that many firms still operate with a limited digital infrastructure alongside this. Digital isn’t generally the backbone it is in many other industries.
Focusing in on Flex
Flex is the segment of the commercial real estate market that operates at much higher velocity. It’s also the fastest growing. In fact, last year infinitSpace found that landlords reported that by 2030 demand for flexible office space will rise by 54%.
So, it stands to reason that it should be the quickest to adopt the biggest story of our era — AI. However, the landscape suggests a different picture with only 26% experimenting with AI, according to a 2025 OfficeRnD report.
What are the Barriers?
The biggest barriers to AI adoption in commercial property often stem from poor and fragmented data and outdated digital infrastructure which make it difficult for firms to integrate AI effectively.
The real estate industry has traditionally been building-centric and not tenant-centric. A tenant-centric model focuses on the largest stakeholders, namely investors and landlords, who care most about lease expiries, yield and asset valuations for financing and pricing decisions.
A tenant centric view of real estate is a demand lead model that looks more at the needs of occupiers. A tenant first perspective changes how office space is presented to occupiers in almost every way and removes friction to transactions throughout that process.
As we shift to a tenant‑centric approach, it brings with it a shift in mindset and perspective to solve problems that tenants face. Problems such as understanding space utilization, managing surge capacity, optimising collaborative spaces to name but a few.
At the moment, even basic steps like helping tenants find the right space are still hindered by listing platforms that prioritise paid visibility over what’s actually relevant to their requirements.
Context is Everything
Whatever the barriers, the industry as a whole will suffer if only a handful are innovating. We don’t just need to be embracing AI, but should be actively building sector-specific AI models.
These are models trained on industry-relevant data, language, and workflows. They don’t just understand the words, they actually understand what those words mean in context. And context is everything in commercial property, where market dynamics, leasing structures, and spatial data vary dramatically.
In complex environments, like commercial property, specialism is key.
The technology and AI arms race will deliver tools that increase transparency, speed to market and reduce friction in the discovery phase of the sales process, but only if we rethink what’s possible and dare to turn the industry on its head.
