Data – The foundation of everything

To get meaningful results from AI, high-quality data is essential. That means having control over how data is collected, stored, and used. Everyone must also agree on key terms – for example, what a “tenant”, “building”, or “energy consumption” actually means. Without shared definitions, AI cannot function effectively.

This is where standards like RealEstateCore come in. It’s an open data standard specifically for real estate, making it easier to collect and use data from different systems in a unified way. Think of it as giving AI a common language for understanding buildings.

AI for everyone – not just experts

With the right tools, you don’t need to be a technician or data analyst to benefit from AI. Tools like Microsoft Copilot (AI integrated directly into Excel, Outlook, Teams, etc.) enable more people within the organization to work smarter with data.

When connected to ProptechOS – a platform that collects and structures building data – Copilot can answer questions like:

  • “Which building currently has the highest energy costs?”
  • “How has the indoor temperature changed over the past three weeks in our offices?”
  • “When is the next scheduled service for our most heavily used properties?”

ProptechOS uses RealEstateCore to structure data. This helps Copilot and other AI tools understand the context of the data and deliver insights, visualizations, and concrete recommendations – right within the tools people already use.

Metadata – information about the information

AI doesn’t just need data – it needs context. That’s where metadata comes in: information about the data. For example, what a sensor actually measures, where it is located in the building, and what factors influence it.

The better the metadata, the more accurate AI’s conclusions. Without context, AI risks misinterpreting or drawing incorrect conclusions.

Implementing AI requires new ways of working

  • Change management
    Introducing AI is not just flipping a switch. It requires changes in workflows and decision-making processes across the organization. Without that, the improvements will be small, and the full potential of AI won’t be realized.
  • Skills development
    More knowledge is needed around data, AI, and ethics in the real estate industry. Not everyone needs to become an expert, but everyone should understand how AI affects their work and how they can benefit from it.
  • Demonstrating clear value
    When you can clearly show how AI creates value – such as saving energy, reducing errors, or improving daily operations – engagement within the organization increases. AI becomes something people want to use, not something foreign or complicated.

Security and ethics – two crucial perspectives

  • Cybersecurity
    AI can help detect suspicious behavior in systems – such as hacking attempts or emerging technical issues. This makes digital buildings more secure.
  • Risks and responsibility
    As we use AI more, understanding the risks becomes critical – such as potential bias in algorithms or misinterpretation of data. It’s also vital to manage personal data responsibly and follow legal and ethical guidelines.

AI creates business value

  • Optimization and energy efficiency
    AI can control building operations in real time – adjusting heating, ventilation, and lighting based on needs and forecasts. This reduces energy consumption and lowers costs.
  • Predictive maintenance
    AI can predict when equipment is likely to fail. This allows for proactive maintenance and avoids costly emergency repairs. It leads to more reliable operations and improved financial outcomes.
  • Clear link to KPIs
    For AI to be a true investment – not just a cost – there must be clear goals: What should improve? What will be measured? Who is responsible? AI initiatives must be managed like any other business investment.

Building AI-ready infrastructure

  • Flexible and open solutions
    For AI to work long-term, systems must be upgradeable and interoperable. Investing in open technologies and standards – like RealEstateCore – prevents vendor lock-in and ensures adaptability.
  • Own and understand your data
    Property owners must have full control over their data – the ability to extract, move, or delete it. This is key to using AI independently and sustainably.
  • Transparency in AI decisions
    You need to be able to trace how AI reaches its conclusions. This builds trust and makes it easier to identify errors or improve models.

Integration – making everything work together

  • More advanced systems
    AI is driving the development of technically advanced systems – not just collecting data, but also analyzing and acting automatically on-site. This is known as edge computing – where data processing happens locally.
  • A unified platform
    For systems to communicate and share data, a unified digital platform is needed. ProptechOS is an example of such a platform, acting as a central hub for all data.
  • Streamlined connections
    Connecting AI to systems like building management systems (BMS) or energy meters requires modern, standardized interfaces. This simplifies integrations and accelerates AI deployment and impact.

Summary

AI is the next major step in the digital transformation of real estate. It’s not just about incremental improvements – it’s about unlocking entirely new possibilities. To succeed, we need:

  • High-quality data and shared standards (e.g. RealEstateCore)
  • Systems that collect and structure data (e.g. ProptechOS)
  • AI tools that are accessible to more people (e.g. Microsoft Copilot)
  • A focus on security, ethics, and business value
  • Organizations ready to adapt and learn

If we bring all this together, AI can drive both better business outcomes and a smarter, more sustainable society.