Leveling up Building Operations Through Generative AI

Generative artificial intelligence (Gen AI) is a form of artificial intelligence that independently creates new content and solutions based on existing information and spontaneous input — including text, photorealistic images, videos, or audio content. Unlike classical AI, which identifies patterns and solves predefined tasks, Gen AI develops solutions that did not exist before.

Since the breakthrough of ChatGPT, one thing has become clear: this technology is fundamentally changing the way we work — and the real estate industry is no exception.

What Is Generative AI ?

Generative AI goes beyond the applications of classical AI. While conventional systems analyze, categorize, and summarize data, Gen AI generates entirely new content in natural language from existing information and fresh prompts. This includes, for example, text, photorealistic images, videos, or audio content.

This is made possible through Large Language Models (LLMs) that contextualize words and generate coherent text. Multimodal Large Language Models (MLLMs) expand these capabilities by processing and creating multiple data types simultaneously. This makes generative AI a true productivity booster for virtually every economic sector.

According to a study by management consultancy McKinsey, Gen AI technologies could unlock global annual productivity gains worth between EUR 2.4 trillion and EUR 4.1 trillion.

Generative AI vs. Classical AI: What is the Difference?

Classical AI analyzes existing data, identifies known patterns, and solves predefined tasks. In building operations, it can:

• analyze large volumes of operational data in real time,
• perform predictive analyses,
• provide tailored recommendations for action.

Applications range from optimizing materials and building designs to predictive control of heating, ventilation, and air‑conditioning (HVAC) systems and developing new resource concepts. A good example is intelligent demand side management, which uses specific algorithms to manage electricity consumption strategically, reducing costs and stabilizing the power grid.

These conventional applications are valuable but limited in adaptability. So, what if there were a digital building assistant that not only analyzes information but also develops new solutions to ever‑changing operational challenges in real time, directly responding to operator or asset manager queries?

How Generative AI Works in Building Operations

With generative AI, the digital co‑pilot for building operations is no longer a distant vision — it’s already a reality. Here is how it works:

Extensive Data Sources as the Foundation

The foundation is a cloud platform that continuously collects, structures, and processes operational data from the building. The AI assistant is integrated directly into this platform and accesses this curated data base in real time. The model is pre‑trained with massive datasets from public sources, including:

• technical documentation
• operating manuals
• building plans
• sensor data
• databases
• scientific publications
• field reports

This broad spectrum of data gives Gen AI comprehensive domain expertise that is far beyond what conventional AI models offer.

Interactive Communication via Chat or Voice Input

Based on this data, the digital building assistant can respond to so‑called “prompts”. These are natural‑language instructions or questions. Through an intuitive interface such as a chat window, technical operators can ask questions or issue specific commands.

Agent‑based systems autonomously determine which additional data to retrieve for example, the current system status, in order to provide precise, real‑time answers to specific operational questions, not just general ones.

Examples:

• How do I set the correct target value?
• What open tasks remain in the project?
• What are the biggest optimization potentials in the building?
• How can these potentials be realized?

Tailored Recommendations for Optimized Operation

Immediately after receiving a query, the system accesses building‑specific data, activates the relevant information from its training corpus, and returns customized recommendations.

The results are precisely adapted to each building’s individual conditions. If the digital building assistant receives feedback on its responses, it can contextually refine future suggestions.

But facility management isn’t the only field that benefits. Asset managers also gain a fast, user‑friendly way to stay informed about how their portfolios are performing.

Open API Integration vs. Specialized AI Assistant

Modern cloud platforms are no longer closed systems. They act as open data hubs. They include an integrated AI assistant yet can also be queried directly via open interfaces (APIs) from enterprise AI tools such as Microsoft Copilot or Langdock.

This means operators can choose whether to use a specialized building assistant or execute generative AI queries directly within their preferred environment, both drawing on live, structured building data.

Still, for many operators, the integrated AI assistant remains the preferred option. Building operations are complex, with many stakeholders, fragmented service providers, and heterogeneous data sources. Generic AI tools are catching up, but still cannot match the depth and accuracy provided by curated, building-specific data in one place.

Generative AI in Buildings Must Become State‑of‑the‑Art

Whether through a specialized assistant or open API integration, generative AI makes building operations faster, more efficient, and future‑proof. Given increasing technical complexity and an ongoing skills shortage, Gen AI is no longer a “nice‑to‑have” — it’s a strategic enabler for smarter decisions, optimized operations, and a competitive portfolio.

The key question is no longer whether — but how ready your building portfolio is for AI.