Opinion piece by Irina Constantin, CEO & Co-founder VAUNT

Everyone is talking about AI in real estate.

According to Morgan Stanley, it could generate $34 billion in efficiency gains by 2030, especially in areas like sales, admin, maintenance, and operations.

The conversation about AI in real estate tends to be divided between excitement about these gains and fear of disruption, job losses or overhyped promises. 

But the more important question is simpler: What are we actually trying to improve?

AI should be a tool that helps create more value, not just automate processes for the sake of it. Not even for the sake of just moving faster. When you automate document generation, for example, and you end up with the same pile of documents, just faster - did that add value? 

The real question about using AI should be how it improves results, what real problems it solves, and whether those results can be measured.

Being more conservative is not a bad thing

Real estate is traditionally more conservative, and rightfully so. Mistakes are expensive, risk tolerance is low, and tech fatigue is real - we switch between too many tools every day.

What we see in practice is that some companies have AI tools but are not using them, while others experiment aggressively. It’s a matter of risk adversity and budgets, but also of having clear evaluation criteria, internal expertise to assess AI tools, and a structured adoption framework.

In many cases, hesitation isn’t resistance. It’s uncertainty, and a lack of trust - will this solution really help me? Will this provider still be on the market one year from now? Real estate players can’t just switch between providers every six months, and they need constant, high-end customer support.

Another major barrier is the lack of educational resources to help people understand how AI can actually support their business. Large corporations can afford experimentation. But what about small and mid-sized operators?

If your business is functioning “well enough,” you may not have the time or resources to audit your processes, test automations, or hire new specialists.

The Productivity trap

Even when companies do adopt AI, there’s another important risk: productivity inflation.

If AI helps you respond faster, generate reports quicker, or automate outreach, what happens next? Is your sales cycle aligned with market reality? Does faster output translate into higher conversion?

If used without structure, AI doesn’t fix weaknesses, it amplifies them. It can simply accelerate a broken strategy. Speed alone does not equal performance.

Where it gets risky

AI is seen to fall into four broad categories: prompt engineering, agentic automation, AI for financial analysis and AI powered content creation. While some of these are already possible and low-risk, some can be more dangerous. 

For example, financial analysis, if not controlled properly, can lead to hallucinated data. One company who used AI in this scope, showed how they presented incorrect data to the leadership, which led to operational errors. In real estate, this kind of mistake can destroy a company. 

We are not at full-replacement level for financial decision-making. Human oversight remains critical.

Will we see AI-specific roles in real estate?

My guess is that yes, possibly, but only if it directly drives revenue. In real estate - and many other industries as well, hiring decisions are pragmatic, especially in the current economical context. If AI increases profit, roles will emerge, if not - they won’t. So it’s more likely to see hybrid roles: 

• Sales leaders who understand automation
• Operators who understand prompt engineering
• Executives with structural AI literacy

Just as knowing Excel became a baseline skill, AI fluency may become a standard requirement, and we’ve already seen this shift. AI is becoming a skill layer.

The BIG question

But beyond adoption and roles, there’s a broader question. 

AI won’t replace us in real estate or in other industries. If automation reduces jobs across industries, who will build and buy the buildings, who will pay rent, who will consume the services? 

Truth is, real estate depends on people and on employment. 

A more practical approach

We’re still in open-ended territory, so our approach is very systematic. We are looking at how we can automate repetitive processes, with measurable results. How can we create more value by providing the industry with more visibility through a performance score, for example? How can we use AI-driven guidance to improve deal performance?

AI won’t magically fix real estate. But it may reveal where the industry has been inefficient all along. And that might be the real disruption.