Understanding your role in bringing AI to your business is crucial. So let's look at how some real estate companies are already using the technology in an array of applications.

Common ways of interacting with AI and GenAI systems include:

• Using existing foundation models or tools such as GPT4 or Microsoft Copilot for internal tasks, e.g., summarizing a market research report.
• Purchasing off-the-shelf AI-powered products/services from PropTech service providers, e.g., buying SaaS product for HVAC system control.
  
• Partnering with an external AI experts to customize solutions to your specific business needs, e.g., creating a customized tool to optimize sustainability performance in a portfolio.
  
• Training, and finetuning, models with proprietary data to provide service in a client-facing interface, e.g., building a client-facing chatbot that makes investment recommendations using historical transaction data.
  

In these use cases, real estate investors, developers, and corporate occupiers are generally categorized as "AI users/deployers," a term defined as "natural or legal persons that deploy an AI system in a professional capacity" by the European Union’s AI Act. This distinguishes them from AI developers and individual end-users.

AI developers focus on creating systems and ensuring the systems function correctly and responsibly, while AI users/deployers must navigate the practical, ethical, and regulatory implications of implementing and relying on these systems in their professional activities.

^{Source: JLL Research, March 2024}

Real estate technology adopters are already familiar with challenges from data security, privacy and IP. These challenges have been identified by over 1,000 senior decision-makers in real estate globally as the biggest challenge in their technology initiatives. AI introduces additional complexity to these issues, but it does not alter their nature.

Each use case has its nuances, but for AI users/deployers there is a set of critical questions to consider. They also apply to your broader tech initiatives.

• How is the model trained? Where is it stored?
• How is your data used? Is there any IP/trade secret involved? Can you opt out?
• How secure is the tool? Can your data be encrypted? When using third-party services, is the provider compliant with regulations such as GDPR and the EU AI Act?

One scenario unique to GenAI and Large Language Models (LLMs) is when proprietary information, such as transaction history, is accidentally uploaded by employees into the public domain as part of the prompts, which could then be used in training later iterations of the model. Such a breach might also occur when fine-tuning foundational models with proprietary data.

To mitigate this type of data leak, companies could consider establishing a "sandbox" environment for deploying and fine-tuning foundation models, doubling down on data governance, setting up responsible data use policy and investing in extensive employee training.

Copyright challenges intensify when engaging with GenAI for content creation, particularly with images intended for public use. Not only is there a risk of your IP being infringed upon, but there's also the possibility of inadvertently infringing on others' IP. If the model has not been developed responsibly with licensed content, users of the model may also be held liable. While many model developers are claiming fair use, the legal landscape regarding use remains uncertain. Choosing the AI provider and establishing guidelines on where and how AI cannot be applied is critical in this case.

''Potential risks in leveraging AI for real estate aren't barricades, but rather steppingstones. With agility, quick adaptation, and partnership with trusted experts, we convert these risks into opportunities.''