Real estate is the world’s oldest and largest asset class. Yet, the sector has a heavy tech debt. Agents still process documents manually, schedule viewings via calls or texts, and rely on spreadsheets or outdated CRMs to manage critical operations. While other industries are being completely disrupted by AI, many real estate businesses are still patching over inefficiencies with incomplete solutions.
Part of the problem is structural. The industry operates largely with fragmented legacy systems, and this complexity makes it difficult to implement change without risk. The perceived burden of going through an automation rollout is enough to deter many business owners from wanting anything to do with technology. It’s no surprise that many firms stick to what’s “worked” — even if it’s inefficient.
But there’s a deeper issue. Even in those cases where technology is integrated, for most companies, “digital transformation” means adding tools to improve existing processes — not redesigning the processes themselves. That mindset limits what AI can do. You can’t use AI to reduce contract errors if the contract workflow itself is broken. You can’t optimize decision-making if critical data is buried in PDFs or emails.
AI adoption in real estate won’t accelerate until the industry shifts its goal: from automation for speed to automation for structural reliability and risk reduction. What we need is not a system that adapts to existing operational processes, but that entirely changes and optimizes them.
The current state of AI in real estate
AI is being adopted, but its usage is still narrow and tactical. Most solutions on the market address one sliver of the process: chatbots for customer service, smart pricing tools, document scanners, or AI-powered viewing tools.
These innovations provide value, but their scope is limited. In rental agencies, for example, AI might help automate viewing reminders — but tenant screening, ID verification, and compliance are still handled manually or via third-party providers with limited integration. This approach slows down the overall experience and increases the chance of human error.
There’s a significant opportunity to reduce that risk — if we let AI handle more than surface-level tasks. McKinsey found that only 8% of companies use AI for risk reduction, even though it’s one of the areas where the technology consistently outperforms humans. In real estate, this translates into missed verifications, invalid compliance documents, or contracts sent with wrong details — all of which can cost deals, clients, or licenses.
In contrast, sectors like finance and logistics are already using AI to predict and prevent errors at scale. MasterCard uses AI to detect fraudulent transactions in real-time. Tesla predicts maintenance needs before a breakdown. Walmart uses AI to forecast inventory needs down to the shelf level. These cases show it is possible to use AI to both maximize output, boost quality, and minimize errors.
There is no reason why the real estate sector can’t be at the same technological level. However, this requires it to integrate technology across its entire workflow.
Real estate and AI: What innovation looks like
Some companies are beginning to move past the incremental mindset.
Let’s look at property compliance. It is traditionally a manual process involving emails, scheduling, PDF certificates, and multiple platforms. However, newer systems now automate compliance checks using a combination of OCR, structured workflows, and voice interfaces.
For example, AI can read a Gas Safety Certificate, extract the renewal date, trigger a follow-up task, notify stakeholders, and update the property record, all without human input. This reduces both workload and legal risk.
Document verification — such as Right-to-Rent checks in the UK — is another area of transformation. Instead of agents manually checking IDs or uploading them to a third-party portal, AI-powered systems now handle these in real time using government-compliant verification engines. This eliminates delays, errors, and repeat requests from tenants.
Other areas of tenant screening are being rebuilt as well. Rather than relying on static credit reports or reference calls, predictive models assess the likelihood of a tenant defaulting based on multiple data points — income consistency, job stability, prior rent behavior, and so on. These evaluations translate into better outcomes, such as higher-quality tenants, fewer arrears, and faster time to rent.
There’s also value in internal operations. AI can flag inconsistent rent inputs, missing fields in contract drafts, or improperly tagged properties in CRM systems. It acts as a safety net for busy teams — and ensures processes are followed regardless of who’s working that day.
Very importantly, these innovations don’t require building proprietary AI models. What matters is how existing tools — OCR, LLMs, workflow engines, analytics platforms — are layered and sequenced into coherent systems. Real value emerges not from single tools, but from orchestration and fully capitalizing on the tools that are already available.
Final thoughts
The biggest barrier to AI in real estate is no longer cost or availability. To fully harness its potential, the sector needs to move beyond thinking of AI as a time-saver or productivity booster, and understand its real power lies in risk reduction, quality control, and complete process automation.
Done right, AI redefines the job of an agent. Instead of manually verifying documents, chasing certificates, or cross-checking data, agents can focus on what matters: advising clients, closing deals, and solving problems. Meanwhile, the system handles the rest — consistently and without burnout.
To reach that level, real estate companies need to rethink how they approach integration. What’s needed is not bolting AI onto broken systems, but rebuilding key parts of their workflow with automation as the foundation that powers them.
There is a growing body of evidence — across industries — that AI excels in environments where there are repeatable processes and structured data. Real estate fits that profile. It’s time the industry takes full advantage of what’s already possible and overcomes its tech debt once and for all.
