Most CRE firms spent 2025 experimenting. They ran pilots, evaluated platforms, and built internal task forces. Some tested AI for market analysis. Others explored it for lease abstraction or portfolio reporting. A few launched tools and quietly sunset them six months later.
That period of low-stakes exploration is over.
In 2026, the question facing leasing teams isn’t whether to invest in AI; it’s whether they’ve moved past experimentation into something that actually performs. And for a significant portion of the industry, the honest answer is no.
The Pilot-to-Production Gap Is Real
According to McKinsey, only one-third of companies have scaled AI enterprise-wide — two-thirds remain stuck in experimentation or pilot phases. In CRE specifically, 70% of occupiers still lack an AI change management plan, and half lack the internal digital skills to execute one. Those aren’t technology problems. They’re organizational ones.
This gap matters because pilots and production are fundamentally different things. A pilot answers the question: Can this work? Production answers: Are we actually using it to make better decisions? Most CRE teams have answered the first question. Very few have answered the second.
The reasons are familiar. Data lives in disconnected systems, leasing activity, broker conversations, market signals, and portfolio performance rarely communicate with one another. Teams that made real progress in 2025 were the ones that addressed this fragmentation directly, not just the ones that licensed another platform.
What Separates Teams That Scale from Teams That Stall
The difference between firms that are compounding AI advantages and firms that are stuck in pilot mode usually comes down to three things.
Clear use cases tied to leasing outcomes. The firms making progress aren’t using AI broadly; they’re using it to answer specific questions that affect deal velocity: Where is demand moving? Which brokers are actively engaged? What does the pipeline look like six months from now? When the use case is specific, adoption follows naturally because the value is visible.
Organizational buy-in beyond the technology team. AI initiatives that live inside IT or are driven by a single champion tend to stall when that champion moves on or priorities shift. The firms scaling successfully have embedded AI into how leasing, asset management, and brokerage teams actually work — not as a separate tool, but as part of the workflow.
A willingness to buy rather than build. MIT research published in late 2025 found that companies purchasing AI tools from specialized vendors succeed roughly twice as often as those attempting internal builds. In CRE, where leasing data is highly specific, and market dynamics vary by submarket, domain expertise built into the platform matters. Generic AI tools often require too much customization to deliver consistent results.
The Cost of Staying in Pilot Mode
Stalling isn’t neutral. AI systems improve with time and organizational use. The longer a team waits to fully deploy, the more ground they cede to competitors who are compounding learning and efficiency gains month over month.
This is particularly acute in leasing. Demand signals are time-sensitive. Broker relationships require consistent, well-timed engagement. Occupancy outcomes are determined in windows—not over indefinite timelines. Teams operating on manual processes or fragmented data are making decisions more slowly and with less context than competitors who’ve moved past the pilot phase.
The firms that piloted AI in 2025 and treated it as a proof-of-concept had the right instinct. The ones that will gain a durable advantage in 2026 are the ones that treat it as infrastructure, something the entire leasing operation runs on, not something the strategy team evaluates. That transition is what this year demands.
