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How can artificial intelligence contribute to value creation in real estate? This question was at the heart of a conference organised by LuxReal, in partnership with Deloitte, on 10 December, under the title “AI in Real Estate: from value creation to robotization of operations”.

While AI has become a dominant topic across industries, its concrete applications in real estate are not always easy to grasp. Between technological promises, regulatory constraints and operational realities, the sector sometimes struggles to distinguish hype from real impact. Yet, as the discussions clearly demonstrated, AI is no longer theoretical: it is already being deployed across multiple segments of the real estate value chain, provided it is approached pragmatically.

AI as a tangible driver of value creation

Before delving into technology, the speakers agreed on one fundamental point: artificial intelligence is first and foremost a practical lever for value creation, both operationally and financially.

For Ian Forsyth, the most immediate benefits can already be observed at building level. “In any large commercial office building, there's an easy 15% of total electrical energy to be saved through the implementation of these tools,” explained the Sustainable Building Solutions expert.

Predictive maintenance, intelligent control of heating, ventilation and air conditioning systems, or AI-powered chat solutions capable of diagnosing technical issues before an intervention all contribute to reducing costs, improving asset performance and strengthening ESG metrics. As Ian Forsyth pointed out, “what people call today a virtual site engineer already exists. It tells you what wrong, what equipment is to bring and how to fix it faster.”

Beyond short-term savings, these technologies pave the way for a deeper transformation, with buildings evolving into active participants in energy systems, capable of feeding energy back into the grid and unlocking new sources of net operating income.

From data foundations to scalable operations

From an asset and portfolio management perspective, value creation is closely linked to scalability. And scalability, in turn, depends on data.

Amos Schelling, Head of Group Control Office at Patrizia, highlighted AI’s potential to transform operational complexity into efficiency. “The most exciting use case for me is creating true scalability of the operations around real estate asset management,” he said.

At Patrizia, AI is already being deployed along two complementary dimensions: automation of operational processes through RPA, and advanced analytics, including automated lease analysis and tenant behaviour prediction. “There is a massive amount of documents in real estate, and AI was built to deal with that massive data amount,” Amos Schelling added.

At the same time, AI acts as a catalyst, forcing the industry to confront its long-standing data challenges. As he noted, “AI will either help fix our data issues, or force us to finally fix them.”

This observation was echoed during the keynote delivered by Piotr Zatorski, Senior Manager at Deloitte Luxembourg, who stressed that AI initiatives can only succeed if built on solid data foundations. “AI is only as good as the data underneath it,” he explained, adding that “you no longer manage buildings, you manage a live ecosystem of real estate data.”

Unified data models, automated data collection and robust governance are therefore not optional. They are prerequisites for any ambition to scale AI across real estate portfolios.

Predictive maintenance: a flagship AI use case

Among the most concrete use cases for AI discussed at the conference by Piotr Zatorski and Ian Forsyth, predictive maintenance clearly stood out as a prime example of immediate value creation.

By analysing data from building systems and sensors, AI makes it possible to detect anomalies early, anticipate failures and optimise maintenance planning. Drawing on his background in heavy industry, Ian Forsyth highlighted the gap that still exists in real estate. “Coming from heavy industry, predictive maintenance was the norm back in 2008. In commercial real estate today, even in Luxembourg, it is still very much run-to-failure.”

AI is now closing this gap, reducing downtime, lowering maintenance costs and extending asset lifecycles, while simultaneously improving ESG performance.

From proof of concept to real-world deployment

According to Liubomyr Bregman, Data Scientist and Machine Learning Engineer at Deloitte, AI has clearly moved beyond experimentation. “AI is no longer a toy. We are moving from proof of concept to real production systems,” he said.

The emergence of agentic AI, capable of orchestrating multiple tools and processes, opens new possibilities, provided organisations remain pragmatic. “If a problem can be solved without agentic AI, that should be the way to go,” he added.

Risks, governance and human adoption

Moderated by Thibault Chollet of Deloitte Luxembourg, the panel also addressed the risks associated with AI adoption, including data bias, cybersecurity threats, regulatory compliance and over-reliance on automated models.

For Amos Schelling, governance and education are essential. “The goal is not to say no to AI, but to say yes… in the right way,” he stressed. In this context, AI literacy emerges as a critical success factor, alongside the involvement of operational teams in identifying high-impact, realistic use cases.

A strategic imperative for competitiveness

In closing, Jérôme Wittamer, Founder and Managing Partner at Expon Capital, placed the discussion in a broader strategic context. “We don’t invest in AI. We invest in founders using the tools of their era,” he explained.

With North American markets already ahead, AI is no longer optional for European real estate players. As Jérôme Wittamer concluded, “people now have to make the jump. Otherwise the gap will just be enormous.”

The challenge is therefore not whether AI will reshape real estate, but how organisations can adopt it in a gradual, controlled and value-driven manner, turning technology into a long-term competitive advantage.

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