The real challenge for AI in real estate is not AI

Everyone expects AI to transform the real estate sector. However, the technology itself is not the biggest challenge. According to Bram Adema, CEO of CFP Green Buildings, the real challenge lies in the quality and availability of real estate data. Without a complete and trusted digital representation of buildings, even the most advanced AI models will struggle to deliver meaningful insights. The future of AI in real estate therefore starts with one fundamental question: can we create a trusted digital truth for every building?

Artificial Intelligence (AI) is evolving at an extraordinary pace. Tasks that previously required consultants, analysts, specialists or complex software can now increasingly be supported, or even carried out entirely, by AI.

As a result, this shift has major implications for knowledge-intensive industries. The real estate sector is also at the beginning of a fundamental transformation. Not because AI will suddenly take over decision-making, but because it is changing the way organisations work, use information and substantiate decisions. “The question is no longer whether AI will impact real estate,” says Bram Adema, CEO of CFP Green Buildings. “The real question is where AI can genuinely add value and under what conditions.”

Why AI works differently in real estate

According to Adema, there are three reasons why AI adoption in real estate is progressing more slowly than in many other industries.

1. A lack of digital and validated data

While sectors such as finance and e-commerce have worked with large volumes of structured data for years, the real estate industry still relies heavily on separate documents, fragmented data sources and varying definitions. This makes it difficult to develop reliable AI applications.

2. High accuracy requirements

Real estate decisions often involve significant financial consequences. AI is not being used to answer casual questions, but to support investments, financing decisions, risk assessments and decarbonisation strategies. This means organisations require reliability, transparency and a clear audit trail.

3. Fragmented and restricted data

Much of the relevant real estate data is not publicly available. Information on buildings, energy performance, financing structures and property portfolios is often stored within closed systems managed by owners, lenders, asset managers and government bodies. As a result, AI models do not always have access to the information needed to deliver reliable analyses.

From generative AI to validated data sources

According to Adema, this is precisely where both the challenge and the opportunity lie. “Many AI models can already generate impressive analyses today, but the outputs are often inconsistent, not validated and difficult to audit. Real estate decisions require reliability, transparency and an audit trail, particularly when investors, banks, governments and asset managers make decisions based on those insights.”

For that reason, Adema believes the real challenge lies not in the AI models themselves, but in the quality of the data on which they rely. Without validated data, AI may generate an answer, but there is no guarantee that the answer is correct. This is why the focus is gradually shifting from generative AI towards validated data sources that enable AI to generate meaningful insights based on reliable, verifiable and traceable information.

Over the coming years, the discussion will no longer centre solely on what AI can do. Increasingly, attention will be directed towards how organisations manage the quality of their data, processes and decision-making. Ultimately, that is where the foundation for successful AI adoption in real estate will be built. This development is not only visible across the market but also within CFP itself. In a separate article, CTO Jarno Schimmelpennink explains how AI is being used to accelerate software development, optimise business processes and further enhance the Green Buildings Tool.

The next step: a digital infrastructure for real estate

While many AI applications today focus on individual tasks, Adema believes the real transformation will take place at a much higher level. The breakthrough for real estate will come when entire ecosystems become digitally connected and accessible. To make this possible, the sector requires a digital infrastructure in which buildings, energy performance, risks, retrofit measures and investment scenarios are connected at scale.

The next step is the digitalisation of the existing building stock. Worldwide, there are billions of homes and hundreds of millions of buildings, yet the vast majority still lack a complete digital twin. Until this gap is closed, the full potential of AI will remain limited.

Adema sees NXTBLDNG as an important building block in this transition. The platform creates a digital twin for every building, based on existing data sources. By making this information available to all stakeholders, the digital twin gradually evolves towards the ultimate goal: 100% completeness, quality and currency. The order in which data is added follows the natural lifecycle of a building: access, update, improve and enrich.

A complete digital twin creates a shared digital truth for every building. As a result, banks, investors, property owners, energy companies, governments, contractors and valuers all work with the same validated and up-to-date information. This not only provides a reliable foundation for AI and better-informed decision-making, but also makes the existing building stock digitally accessible. According to Adema, the impact of this development is enormous. The world’s largest asset class and largest market become digitally connected, creating new opportunities for e-commerce and digital customer journeys across sectors such as energy, banking, government, construction and valuation. This creates opportunities to reduce transaction costs, improve efficiency and ultimately deliver a higher-quality built environment that better supports human wellbeing.

The value does not lie solely in automating work. More importantly, it lies in enabling entirely new insights. Which buildings face the highest transition risks? Where will investments deliver the greatest impact? Which portfolios are prepared for future regulations? And how can financial institutions better support clients in improving the sustainability performance of their real estate assets?

AI as an accelerator of the energy transition and building upgrades

AI will become an increasingly natural part of the real estate sector over the coming years. Initially, it will serve as a tool to support day-to-day activities. Next, it will accelerate analysis and decision-making. Ultimately, AI will become part of a broader digital infrastructure that enables faster, better and more transparent real estate decisions.

For organisations looking to benefit from AI, the challenge is therefore not only selecting the right model. More importantly, it is about establishing reliable data, robust processes and the right expertise. Those that take the lead in these areas will be able to use AI not only to improve real estate decision-making, but also to accelerate the energy transition and the upgrading of existing buildings.