Artificial Intelligence has swept through commercial real estate at a pace few expected. Over the past year, the number of founders building AI tools for acquisitions, underwriting, property management, leasing, asset management, and analytics has surged. The ecosystem of commercial real estate AI companies has expanded rapidly, and the excitement is real.
But here is the question you are probably asking as a building operator: how much of this is actually working inside real firms like yours?
A recent industry survey of 150 real estate professionals across firm sizes and roles offers clarity. The results show strong enthusiasm, rising budgets, and heavy experimentation. They also reveal hesitation, stalled pilots, and deep trust barriers. If you manage assets, oversee operations, or guide portfolio strategy, these findings matter to you. They highlight where AI in commercial real estate is delivering results and where it is falling short.
What appears to remain true is that the breakthrough comes when AI is embedded into real workflows in a way that removes friction, reduces risk, and creates measurable impact. Let’s unpack what this means for you.
If you feel like everyone is “doing something” with AI but few are fully transformed, the data confirms it.
Forty five percent of firms are actively running AI pilot programs. Another 26 percent are researching or planning AI initiatives. Only 2 percent report no AI activity at all. On the surface, that looks like widespread adoption of commercial real estate AI tools.
But only 9 percent of firms have achieved enterprise wide AI implementation. Another 18 percent have departmental deployments. That leaves 73 percent testing, planning, or stalled.
You are likely living in this middle ground. Maybe you have a lease abstraction tool in trial. Maybe your acquisitions team is experimenting with AI underwriting models. Maybe property management is testing AI work order categorization. Yet scaling beyond a pilot feels hard.
The bottleneck usually comes down to three issues:
First, data infrastructure is messy. AI models rely on structured, clean data. Many commercial real estate firms operate across spreadsheets, PDFs, legacy systems, and siloed teams. When the data foundation is weak, AI initiatives wobble.
Second, success metrics are unclear. If you cannot define what success looks like before the pilot begins, it becomes difficult to justify expansion. Was the goal time saved? Errors reduced? Revenue increased? Without a baseline, AI investment feels abstract.
Third, organizational resistance slows progress. AI pilots often sit in innovation teams or specific departments. Scaling requires workflow redesign, training, and executive alignment. That shift demands attention and leadership.
AI is a workflow decision, not just a technology decision. If AI remains assistive, meaning it offers recommendations but still requires full human attention to act, it hits a ceiling. Your team still carries the cognitive load. The “attention tax” remains.
To move past the pilot stage, you must decide whether AI is simply advisory or whether it has authority within defined guardrails. That decision changes everything about how you design processes.
Nearly half of surveyed professionals report that their firms distrust AI generated financial analysis. Only 11 percent fully trust AI outputs.
If you oversee underwriting, asset management, or portfolio strategy, this makes sense. Financial decisions carry real risk. A flawed comp set or misinterpreted lease clause can distort valuations and impact millions of dollars.
This trust gap places a ceiling on the highest value use cases for commercial real estate AI. You might feel comfortable using AI for drafting emails or summarizing documents. You likely hesitate to rely on it for cap rate sensitivity analysis or deal underwriting.
So how do you build trust?
The survey suggests a path that aligns with how Cove talks about AI. Start narrow. Build specialized tools where accuracy can be measured and verified. Lease abstraction. Certificate of insurance tracking. Comparable property extraction. These tasks have defined inputs and outputs. They are easier to fine tune and evaluate.
Keep professionals in the loop. AI should surface structured outputs with transparency. When your team can see where numbers came from and validate them quickly, confidence grows. Over time, trust compounds.
You do not earn trust in AI by promising disruption. You earn it by proving accuracy in small, repeatable tasks that matter.
One of the most encouraging findings is that 80 percent of respondents believe AI will benefit their business over the next five years. Yet only 18 percent expect AI to reduce team sizes.
Among firms that have deployed AI enterprise wide, none are planning staff cuts. That is a powerful signal.
You are not looking to replace your property managers, asset managers, or leasing teams. You are looking to make them more effective. Commercial real estate is a relationship driven, judgment heavy business. AI enhances pattern recognition, speed, and data processing. It does not replace local market intuition, negotiation skill, or strategic thinking.
More than half of surveyed firms plan to increase AI budgets by more than 20 percent over the next two years. Another 22 percent plan increases between 11 and 20 percent. Only 12 percent anticipate flat or declining budgets.
For a mid size firm with a 2 million dollar technology budget, a 20 percent AI allocation equals 400 thousand dollars annually. That is not incremental spending. That is strategic reallocation.
Where is the money coming from?
Forty three percent of firms expect reductions in outsourcing. Forty one percent are targeting administrative cost reductions.
This aligns with a practical reality. AI in commercial real estate often delivers the strongest early returns by reducing reliance on third party analysts, manual data entry services, and repetitive back office tasks.
If you are increasing AI spend, you must tie it directly to cost offsets or revenue gains. Map each AI initiative to a measurable outcome:
Without a financial model, rising AI budgets can drift into experimentation without impact.
If you want your AI initiatives to move beyond pilots, you need a clear framework. Here is a practical roadmap.
Identify the tasks that consume the most time and add the least strategic value. Lease abstraction. Invoice coding. Work order triage. Insurance tracking. Comp research. These are prime candidates for AI support.
Document the current time and cost associated with each task. You need a baseline before you deploy any commercial real estate AI solution.
Before starting a pilot, write down what success looks like. Is it a 30 percent reduction in processing time? A measurable drop in errors? Faster turnaround on deal memos?
Tie these metrics to financial outcomes. If you cannot quantify impact, it will be difficult to justify scaling.
Broad AI ambitions stall. Focused implementations succeed.
Deploy AI in a tightly scoped use case. Validate accuracy. Refine prompts and workflows. Train your team. Once you achieve consistent performance, expand adjacent functions.
This is consistent with Cove’s philosophy of embedding AI where it simplifies daily operations rather than layering complexity onto them.
Many pilots fail because AI is bolted onto existing processes. Your team still performs every step, plus reviews AI outputs. That increases attention load instead of reducing it.
Look for opportunities to redesign workflows so AI handles defined steps autonomously, with clear review checkpoints. Granting limited authority within guardrails unlocks real efficiency.
AI performance depends on structured data. Standardize naming conventions. Clean historical records. Consolidate systems where possible. Investing in a centralized commercial property management software now will pay dividends later when AI capabilities begin to compound based on the data it has access to.
Data infrastructure may not be glamorous, but it determines whether your AI investment scales or stalls.
Commercial real estate AI is not a passing trend. Budget allocations, industry optimism, and widespread pilots signal a long term shift.
Yet the industry stands at a crossroads. You can remain in experimentation mode, running pilots that feel promising but never transform operations. Or you can commit to thoughtful integration, workflow redesign, and measurable impact.
AI should reduce friction across property management, compliance, and asset operations. It should make your systems smarter and your teams more empowered. It should turn data into decisions without adding complexity.
Your advantage lies in execution. Technology alone does not create returns. Strategy does.
The firms that win with AI driven property management will build trust through transparency, and scale deliberately.
You do not need to chase every new AI platform. You need to solve real problems inside your portfolio.
Start there. Measure relentlessly. Build trust. Redesign workflows. Allocate budget with intention.
Commercial real estate has caught AI fever. The opportunity now is to turn that fever into disciplined, measurable progress that strengthens your assets and your team for the long term.