Agentic AI Use Cases for Today’s Real Estate and Construction Firms

Real estate and construction companies are on the precipice of a dramatic shift. Artificial intelligence (AI), particularly agentic AI, will permanently change how the industry does business, streamlining functions from back-office administration to logistics, data-heavy tasks, and more.

Unlike traditional automation, intelligent agents are purpose-built and trained to fulfill specific roles, enabling them to make decisions independently and navigate complex processes with minimal human intervention. Organizations that successfully integrate these tools stand to benefit from faster decision making, improved project planning, and more competitive pricing.

For all its benefits, autonomous AI represents an intimidating advancement. These systems require robust support infrastructure and bring new risks and challenges. Organizations will need to strengthen their data governance processes, implement cybersecurity best practices, learn how to collaborate with autonomous systems, and account for novel risks like AI bias on an ongoing basis.

These complex dynamics call for thoughtful planning and targeted investments without delay. As first steps to integration, real estate and construction companies should proactively investigate how agentic AI can improve their operations and seek out potential use cases. Organizations that act quickly will unlock a powerful competitive differentiator, while those who wait risk being left behind.

Agentic AI Real Estate and Construction Use Cases

Companies are just beginning to understand the vast potential of AI agents. For real estate and construction leaders seeking an entry point, several use cases stand out as impactful and achievable options, each carrying the potential to increase efficiency and reduce operational overhead.

Real Estate Use Cases

Contracts and pricing: Property management firms are responsible for maintaining and understanding large troves of documentation. AI can quickly sift through huge amounts of data, easing the process of reviewing and drafting key documents. These tools will help verify that contractual clauses are written correctly and do not contain any oversights. They can also monitor regulatory activity and notify businesses in real time about any changes, new rules, and potential compliance issues. With appropriate oversight, they will even be able to make the necessary adjustments in some cases. 

During negotiations, firms could call upon their AI agents to screen tenant applications and leverage historical and current market data. Property managers could come to the table confident that their pricing decisions are defensible and backed by data, tailored to both meet their needs and satisfy applicants’ expectations.

Tenant management and customer service: Intelligent systems can offer around-the-clock support for maintenance or information requests. Previously, if a tenant experienced a non-emergency issue with an appliance during the night, they might need to wait until the following day to notify their management company and schedule repairs. Autonomous agents can respond immediately, no matter the time, and place a service request on the schedule for the following morning. Prompt responses will help reassure tenants they are being heard, reducing instances of friction and building loyalty. Should an emergency arise, the system can immediately notify the management company and update the maintenance schedule accordingly.

Back-office support: AI will transform the back office, processing and validating payments automatically and sending reminders to tenants or other customers who miss a deadline. With access to this financial information, intelligent tools can also help collect and organize data for financial reporting obligations and, if given the appropriate parameters, may even supplement actions such as filing taxes, cutting down on compliance costs while increasing efficiency.

Portfolio Management: Agentic AI can act as a continuous decision-making partner in investment management for both real estate and construction firms. It can autonomously monitor market dynamics, forecast project viability, and reallocate capital across portfolios in real time. It can also evaluate factors like material cost fluctuations, urban development plans, and rental yield trends to optimize asset performance without constant human oversight.

Construction Use Cases

Coordination and planning: AI agents can engage in forecasting, simulation, and planning for construction projects. They can also oversee communications with and between parties like inspectors, contractors, and subcontractors. Acting as project managers, these systems will monitor and log progress when a job is running smoothly, and step in to help course correct when necessary, independently adjusting schedules or budget forecasts based on changing circumstances. If, for example, malfunctioning machinery causes a work stoppage, an AI agent can flag the breakdown and incorporate the time needed for repairs into an updated project roadmap. With an autonomous agent managing workflows, organizations may be better insulated against human scheduling errors and resultant cost overruns.

Payment Management: Agentic AI can help construction companies manage payment applications, ensuring contractors and subcontractors are paid on time. It can also log completed work for recordkeeping and reporting purposes, keeping information standardized and accessible and reducing the chances of documentation getting lost or misclassified.

Permits and compliance: Construction projects require proper permitting and regular inspections to verify that job-site conditions are safe and compliant. Mistakes or misstatements in permitting documentation can be expensive and may increase overall compliance costs or open organizations up to enforcement actions. Intelligent agents can reduce these risks by gathering information for use in permit applications, interpreting and filling out the necessary forms, keeping track of permits filed, and updating the company in real time if permitting needs change. This function can be particularly impactful with respect to local jurisdictions, where regulations can often vary widely and can be difficult to track manually.

Agentic AI can also monitor labor union agreements and related workforce regulations, helping firms proactively align with union requirements, avoid disputes, and maintain smooth operations across all jurisdictions.

Humans in the Loop

Real estate and construction companies can pursue these applications today. As agentic AI advances, companies can integrate these systems even more deeply into operations. Think of smart buildings and autonomous construction equipment, all managed and guided by intelligent tools.

Even as AI functionality continues to evolve, one factor remains constant: Humans are essential to support both initial integration and provide ongoing oversight of new tools and technologies. Leaders must remain aware of the challenges AI can bring and treat adoption not as a one-off instance but as part of a long-term strategy.

Agentic AI Risks and Challenges

In the past, real estate and construction companies have not been as tech-forward as other industries. To support agentic AI, they will have to make up ground, particularly in areas like governance, cybersecurity, and AI literacy.

AI Bias

The risks posed by unseen biases grow substantially with AI agents. Data used to train AI is subject to the biases of the humans who provide it, sometimes causing a program to “inherit” the discriminatory biases of its creators. Inherited biases could lead to unfair or inaccurate outputs that damage the businesses that rely on them. This risk is especially prevalent for real estate firms, which may employ intelligent tools for tasks like pricing, contract negotiations, and application screening. For instance, if those systems have inherited a bias that causes them to treat applicants differently based on a protected characteristic, the firm could violate fair housing regulations, leading to significant financial, legal, and reputational risks.

Preventing AI bias demands continuous and active testing, covering both the underlying dataset and the AI’s outputs. Companies should request bias test results from any potential AI vendor, and check whether a vendor has obtained third-party certifications such as SOC 2 as an additional layer of confidence. A lack of bias testing is a red flag. The risks of harm to a business and its customers are too great to ignore. Organizations inexperienced in making these evaluations may consider enlisting a knowledgeable third party with the resources and experience to help check for unseen biases.

Governance

Strong AI governance, covering both technical concerns and operational risks, is critical for successful AI implementation. Because autonomous agents will operate cross-functionally, building a governance framework must be a cross-functional process, incorporating feedback from each area of the business and covering critical domains like risk management, data ethics, data privacy, data lifecycle management, and organizational structure.

For real estate and construction companies, the first step is assigning decision rights. Where will an AI agent be empowered to make decisions, what data will it leverage to do so, and how will human oversight be conducted? Answering these questions means defining specific use cases, such as the options above, and will allow firms to clearly delineate the AI agent’s role and assessing any risks tied to each use case. Organizations will also need a process to document decisions and deliver feedback. For domains that introduce compliance risks, such as construction site safety or tenant application screening, governance teams should implement several layers of checks to ensure that all decisions are responsible and ethical.

Governance is not just as a means for organizations to protect themselves, but also a way to unlock the full potential of their AI agents. A clear scope and well-defined decision parameters will enable safer usage, but they also create higher quality and more reliable outputs. 

Cybersecurity

Interconnected systems can increase security vulnerabilities, necessitating new protections against novel forms of data theft. Real estate and construction companies will need clear visibility into AI input data, how that data is processed, who has access to it, and how it is shared to support data loss prevention (DLP) and stop sensitive data from leaking. 

For organizations that employ outward-facing AI, such as agents that handle tenant inquiries, these needs are even sharper. A user interacting with an agent could ask a question that causes the system to reveal sensitive business information. Known as “prompt injection,” this tactic is increasingly used by bad actors to steal information without breaking into a company’s systems.

In some cases, strengthening cybersecurity also involves physical security. On a construction site, for instance, supervisors will likely use mobile devices or machinery that communicate with AI agents. Firms must be sure they have adequate endpoint security and a robust mobile device management strategy to track usage. If an unauthorized individual gains access, whether on purpose or by mistake, they could obtain sensitive information. These devices should only be accessed by trusted, predesignated users.