Everyone's started with AI. Almost no one's finished.

The exploring majority of 2025 is the active majority of 2026.

Active AI use among commercial landlords went from 32% to 57% globally in twelve months. In the United States it stands at 64%. The wait-and-see group did not grow. It halved.

The United Kingdom moved 43 points in a single year, from 22% to 65%. Only one respondent in eight has no plans at all.

It starts in the lease document. Every market, no exceptions.

Lease administration and abstraction came straight in at the top: 60% globally, 66% in the United States, more than twenty points clear of any other use case.

It is easy to see why. This is the highest-volume, highest-friction document task in commercial property: turning leases that run to hundreds of pages, in no common format, into structured, queryable data the rest of the business can act on.

An email arrives. The triage is already done.

Tenant communication is the second most common AI use case globally, cited by 39% of active users, with US landlords leading at 52%, double the UK's 26%. The scenario plays out dozens of times a day in every portfolio: an email arrives from a tenant reporting a maintenance issue.

Before AI, a property manager reads it, manually identifies the property, pulls up the tenancy, decides on a course of action and writes a response. Each step is small. Across a portfolio, the cumulative time is significant.

With AI reading incoming email, the system identifies the intent, links it to the right tenant, property and lease, and surfaces the context alongside a suggested next action. The property manager reviews and approves what goes out.

Check 5% of your data once a year, or 100% of it every day.

Auditing lease data by hand has always meant sampling. Even large portfolios check a fraction of records once a year and accept that as good enough, because reading every lease, contract and insurance document against every field was never realistic.

AI checks all of it, continuously, against the source document. Discrepancies surface as they appear, not at audit time.

Move from doing to driving.

Commercial real estate has always described itself as a human business. Yet operationally, its people have been spending the majority of their time on the administrative infrastructure of doing that work: parsing documents, drafting communications, entering data, running audits.

Neither is about replacing people. The choice isn't AI or judgement, it's AI freeing up the judgement that was always there, just buried under data entry. Whichever path a business takes, the person making the call is still a person.

61% see measurable productivity results.

In commercial property, general-purpose AI runs out of road quickly.

90% of CRE investors and owners have piloted AI. 5% have achieved all of their program goals, and more than 60% of real estate companies remain strategically, organisationally and technically unprepared to scale it. The gap between having started and having made it work is the defining story of this stage.

The barrier landlords name most is not money. It is in-house technical expertise, cited by 24% globally and first in three of four markets. Cost came fifth. Which makes the choice of vendor the decision that determines whether any of this lands.

Commercial property is complex enough that this matters more than people think. A break clause isn't a rent review. A general model can read that text and still not know a tenant can't be charged twice. That's why the real difference isn't what AI can do, it's what it can safely be allowed to do on its own.

No hallucinated rent figures, no invented lease terms. Every answer is cited back to the source record and the exact clause, so you verify before you act. A general model has no way to do that. It has never seen your leases.

Each use case frees up budget and builds proof for the next. Invoice processing extends into investment documents, valuations and asset management. What starts as one efficiency gain becomes the case for the next, and that's how a pilot turns into a transformation.

The engine behind the examples.

Re-Leased AI is built directly into the platform. No separate integration, no technical team, no implementation project, which is precisely the barrier landlords name most. It works on your live leases, tenants, financials and compliance history.

leases supported across the Re-Leased platform

thousands of property firms, across four markets

the Re-Leased AI average one property manager can carry

Two reports behind this page.

One measures the market. One documents what is actually happening inside it.