According to JLL's 2025 Global Real Estate Technology Survey of more than 1,500 senior CRE decision-makers, 88% of investors, owners, and landlords have started piloting AI. Yet more than 60% of investors remain unprepared strategically, organizationally, and technically.
JLL's research shows most firms are still experimenting with AI rather than scaling it. The usual explanations for that gap sound familiar — the model wasn't good enough, the use case was too ambitious, the team ran out of time. But the harder question is organizational: has the firm decided that AI is a resourced initiative, with a budget, an owner, and a plan for what happens six months from now? Or is it still what someone built when they had the bandwidth?
That missing decision is the gap. And it's why "ask your analyst to build it" keeps showing up as the default — not because it's a strategy, but because it lets firms skip the harder question of whether AI is worth resourcing with purpose.
Handing AI to Whoever's Closest
When leadership isn't sure what AI can actually do, the instinct is to hand the problem to whoever seems closest to it. Often that's an analyst, because analysts are close to the workflows and increasingly comfortable experimenting with new tools. Just as often it's the IT team, on the theory that AI is software and therefore belongs with IT.
Both are reasonable instincts, and both produce real, if narrow, results:
- An analyst automates lease data extraction.
- A transactions team uses AI to summarize offering memoranda.
- An asset and portfolio management team experiments with automated variance reporting.
Each of those can save meaningful time, and none necessarily needs much institutional investment to get started.
The problem is that each of those people already has a day job. Building the prototype was a side project they took on because they had the curiosity and the bandwidth. Maintaining it, scaling it, and making it reliable enough for the rest of the firm to depend on is a different role. The trouble starts the moment someone asks whether one of those prototypes should become something the rest of the firm depends on, and the person who built it is already back to their actual workload.
Why the Builder Was Never the Real Problem
Here's what that next step tends to look like in practice: the analyst who built the prototype, the IT team asked to support it, and the asset managers who'd actually have to trust it end up in a room together. Then another room, then another. A year later, there's still no product, not because anyone did bad work, but because nobody was ever assigned to make the call on data, ownership, or what happens when something breaks.
The analyst was never really the issue. Swap the analyst for a senior developer, the IT team, or an outside contractor, and the same organizational questions remain: who owns the data, who owns the system, and who decides what happens when something breaks?
What It Takes to Own an AI Initiative
JLL's research points in the same direction: The firms making progress are taking a systematic approach to AI by building technology roadmaps, investing in data and infrastructure, and embedding AI into core workflows. For a CRE firm moving beyond experimentation, that means making three commitments.
1. Reliable data
Lease abstracts, ARGUS assumptions, and property-level records vary enough between analysts, and even between properties, that AI will inherit those inconsistencies rather than fix them. Whoever owns this has to fix the data foundation first, not after.
2. A problem worth funding
A prototype answers "Can AI do this?" Scaling one requires a different question: "Is this important enough to fund?" The answer should show up in measurable business value—hours saved, deals moved faster, or decisions made with better information. The value worth measuring is whether the team you already have can take on a larger portfolio, and whether decision-makers actually trust what's in front of them.
3. Clear governance
Someone has to own the tool: who reviews its outputs, what happens when the underlying data changes, who maintains it once the original builder moves to a different desk or a different firm, and who has authority to change or shut it down.
None of these are technology questions. They're organizational commitments, and they're the same three regardless of whether an analyst, IT, or an outside vendor wrote the code.
What a Partner Can Fix, and What It Can't
A specialized partner can help address the first two commitments: establishing reliable data and translating a clearly defined business problem into a CRE-specific workflow. Governance still requires an owner inside the firm.
That requires understanding both the technology and the workflow. An internal IT team may know how to build reliable software without knowing why an asset manager might distrust an automated output. A generalist contractor may have the technical skills without understanding the nuances of CRE data and processes.
Truss brings those capabilities together, reconciling CRE data across systems like ARGUS, Yardi, and MRI and applying that data to workflows the industry already understands. The highest and best use of that partnership is automating the repeatable, manual processes a firm already knows are slowing its teams down.
The firm still owns the decision to deploy and govern the system. A partner can build it, but the business has to decide how it gets used.
The Decision That Actually Separates Firms
None of this is an argument against letting people experiment. Analysts poking at AI tools on their own time is often where the best ideas come from, and the people closest to a workflow are usually the ones who see where AI could help most.
The mistake is treating that experimentation as the strategy itself. The firms hitting their AI goals are the ones that look at a useful prototype and make a deliberate call: this is worth resourcing, here's who owns it, and here's the budget for what happens next.
"Ask your analyst to build it" is a fine way to find out what's possible. It isn't, by itself, an AI strategy. For firms ready to turn a promising prototype into a funded, owned initiative, that's a conversation worth having with Truss.