What is agentic AI and why it's changing the game in real estate

Classic artificial intelligence, as we know it from ChatGPT or Gemini, excels at answering questions and generating text. Agentic AI goes a step further — these are systems composed of several collaborating agents that pass data between themselves, evaluate results, and independently decide on the next course of action. Instead of using AI for a single isolated task ("read this lease agreement"), an agentic system handles the entire chain: it reads the contract, extracts key parameters, compares them with market data, prepares a summary for the investment team, and updates internal systems.

According to McKinsey partner Ankit Kapoor, it is precisely this transformation of entire domains — "when you deploy a series of coordinated agents that acquire data, prepare materials, summarize findings, and update systems, that's what it means to transform a domain." McKinsey estimates that AI can create value in the real estate sector ranging from $430 to $550 billion.

Three key areas where agentic AI generates value

McKinsey's analysis identifies three main domains where agentic AI is making the biggest impact:

1. Front-end: leasing and business activities

The first area covers everything related to leasing and revenue generation. The traditional "9-to-5" model is shifting to 24/7 engagement — agentic systems can continuously communicate with potential tenants, qualify leads, and prevent their loss. AI agents automatically respond to inquiries, schedule viewings, and prepare personalized offers based on historical data about the given location.

2. Property operations

The second domain is property management — the management of buildings and complexes. Agentic systems here automatically sort maintenance requests ("triage tickets"), assign technicians based on their specialization and location, and optimize the entire service process. The result is faster response to tenant issues and lower operational costs.

3. Back-office: investment operations and reporting

The third area covers financial reporting, valuation, and investment analysis. Here McKinsey provides concrete numbers: deploying agentic AI can reduce the time spent on these processes by 60 to 80 percent. Instead of manually collecting data from various systems, consolidating it, and creating reports, all of this is handled by coordinated agents. The human worker then only checks and approves the results.

Domain approach: from isolated tools to entire workflows

A key concept that McKinsey advocates is the so-called "domain approach". Most companies today use AI tools in isolation — one for reading contracts, another for market analysis, a third for client communication. These tools don't talk to each other and don't create synergy. The result is minimal financial impact and low return on investment.

The domain approach, on the other hand, means that a company redesigns the entire work process from start to finish so that it can be executed by a coordinated network of AI agents. Humans enter the process at key moments — when checking outputs, making strategic decisions, and building relationships. McKinsey reports that organizations adopting this approach observe improvements in net operating profit and cycle time reductions of 10 to 30 percent.

What does this mean for people in the industry?

McKinsey emphasizes that the question isn't "which positions will disappear," but "how will existing roles transform." If two-thirds of the activity is handled by an automated tool, the human role shifts toward evaluation, accuracy checking, and building trust.

McKinsey partner Vaibhav Gujral illustrates this with investment professionals: "Instead of creating standardized reports and models for signal detection, their attention will shift to judgment-based decision-making, capital allocation, and maintaining relationships." In crisis situations — for example, during a building emergency — tenants still need empathy and responsiveness from a living person, not an automated system.

Barriers: data, corporate culture, and trust

Despite the enormous potential, significant barriers exist. Ankit Kapoor points out that without a clean and well-managed data infrastructure, agentic AI has no chance: "If an AI agent works with faulty data, it produces faulty results. There is no substitute for a quality data foundation."

Gujral adds three more risks: organizational inertia (companies resist changing established processes), treating AI as an IT project rather than a change to the entire company's operating model, and concerns about trustworthiness and security. Alex Wolkomir of McKinsey also warns against a "race to the bottom" — if all companies deploy the same technology in the same way, they lose their ability to differentiate.

Czech and European context

For the Czech real estate market, McKinsey's report is relevant for several reasons. Global consulting firms such as JLL, CBRE, Cushman & Wakefield, or Knight Frank operate in the Czech Republic and are already experimenting with AI at a global level, with their know-how gradually reaching the local market.

At the same time, Czech real estate agencies and developers face the same problems as their foreign counterparts: administrative burden, slow processing of inquiries, and inconsistent reporting. Agentic AI could particularly help medium-sized firms that don't have extensive back-office teams.

In the European context, it's necessary to mention the EU AI Act, which comes into full effect from August 2026. Real estate companies wanting to deploy agentic AI must ensure transparency of agent decision-making, protection of personal data (GDPR), and clear delineation of responsibility for automated decisions. This can be more complex in practice than it seems — especially if agents work with sensitive data about tenants or investors.

The good news is that agentic AI isn't just for giants. Platforms such as Microsoft Copilot, AWS AI Agents, or open-source frameworks are emerging on the market, enabling even smaller companies to build agentic workflows. Prices range from free tiers to hundreds of dollars per month for enterprise deployments. For a Czech company, the investment in initial agentic pilots can be in the range of tens of thousands of crowns per month.