The tertiary and public real estate sector is entering a new era. While artificial intelligence (AI) was once associated with asset valuation or the automation of announcements, it is now becoming a central lever in the technical management of real estate assets.
As asset managers must meet growing regulatory obligations, while controlling their investments and valuing their portfolio, technical data is becoming a strategic asset. In this context, AI does not merely promise productivity gains: it structures information, ensures reliable decisions, and allows for large-scale management.
Why AI is becoming essential for real estate departments
Managing a real estate portfolio, whether it consists of a few buildings or several hundred, involves handling a considerable volume of technical data: equipment (HVAC, lighting, building management systems), surface areas, materials, aging, consumption, regulatory documents, and diagnostics.
This data is often scattered across Excel files, PDF folders, or paper plans, making consolidation tedious. Furthermore, the quality of this data is uneven: outdated, incomplete, or unusable without manual re-entry. The result is that strategic decisions are often based on assumptions rather than on consolidated facts.
It is precisely in this complexity that AI makes sense. It does not replace human expertise but allows it to be amplified across an entire portfolio. It structures, ensures reliability, and connects data to enable informed decisions.
Five concrete use cases of AI in technical real estate management
1. Automatic classification of equipment and materials
Thanks to computer vision, AI tools can now automatically identify technical equipment such as boilers, heat pumps, lights, or ventilation ducts from simple photos or videos taken on-site.
They are also capable of recognizing visible materials on facades or inside buildings: glass, concrete, wood, metal cladding, etc. This allows for a semi-automated inventory of technical assets, organized hierarchically (site > building > floor > zone > equipment).
This use allows for savings of up to 50% time on survey campaigns, while ensuring greater reliability than manual methods.
2. Generation of digital plans from field data
Some AI solutions allow for generating vectorized and dimensioned plans of a building from PDF plans or photos taken on-site. These "digital plans" can then be used in maintenance tools, energy audits, or asset tracking.
Beyond visualization, these models serve as a common foundation for strategic decisions. They facilitate the simulation of technical scenarios, integration with CMMS or ERP systems, and become pillars of the real estate master plan.
This is a concrete way to restore readability to ancient or poorly documented assets.
3. Automatic reading of existing technical documents
Natural Language Processing (NLP) AI allows for automatically extracting information contained in technical documents: DOE, audit reports, PDF surveys, maintenance contracts, etc.
It identifies key data (brands, references, dates, powers, noted anomalies…), structures it, cross-references it, and makes it directly usable in a centralized database.
This processing allows for avoiding re-entry, accelerating document analysis, and especially for ensuring continuity of information between technical, regulatory, and budgetary departments.
4. Automated detection of pathologies and aging
Coupled with supervised learning models, AI can detect visible signs of degradation in photos or videos: infiltration, cracks, corrosion, mold, deformation, etc.
The analysis can be enriched with automatically estimated severity scores and even maintenance or repair recommendations. This allows technical teams to efficiently prioritize their interventions, especially in large portfolios where exhaustive inspection is difficult to conduct regularly.
5. Work recommendations and intelligent budget planning
By crossing technical data (condition, aging, consumption, compliance), average unit costs, regulatory issues, and energy objectives, AI can suggest actions and propose coherent and simulated multi-year work plans.
It calculates potential energy savings, evaluates return on investment, and proposes phasing according to several criteria: technical urgency, economic impact, regulatory compliance, ESG objectives.
This process, once reserved for large groups or specialized engineering firms, is becoming accessible to all real estate departments wishing to manage their assets intelligently.
AI as an ally against regulations
The tightening of regulatory frameworks makes the structuring of technical data essential:
The Tertiary Decree requires detailed tracking and justification of energy savings building by building;
The regulatory energy audit requires testing precise, traceable, and simulatable scenarios;
CSRD obligations or Green Taxonomy mandate the structuring of technical ESG data;
AI allows for quick, efficient, and standardized responses to these requirements. It transforms compliance into a continuous process rather than a one-time constraint.
Artificial intelligence at the service of the real estate master plan
A relevant real estate master plan rests on a detailed and consolidated knowledge of the technical condition of the portfolio, as well as the ability to project investments over 5, 10, or 15 years.
With AI, it becomes possible to:
automatically detect points of weakness,
model several renovation trajectories,
estimate costs and associated energy impacts,
and prioritize actions according to objective criteria (risk, cost, return on investment).
This is a major transformation: we no longer just react, we anticipate, we simulate, we manage.
How to initiate an AI approach at the scale of your assets?
To launch an effective approach, it is essential to:
Structure a first foundation of reliable data: plans, equipment, photos, consumption data.
Ensure compatibility and exportability of this data to your existing tools (CMMS, BIM, industry tools...).
Support your field teams with intuitive and mobile tools, augmented by AI (assisted photography, automatic classification…).
Train your teams on AI's industry use cases so that they know how to leverage it as a professional ally.
Conclusion: AI is not a luxury, it is the new standard
AI does not replace technical management actors. It gives them enhanced capabilities to do better, faster, and on a larger scale.
It allows for analyzing 100 buildings with the same level of precision as a human eye, producing continuous audits, planning works with more coherence, and managing the performance of one's assets with complete peace of mind.
In a context of regulatory, budgetary, and climatic tension, failing to structure technical data is falling behind. The question is no longer "should we proceed?" but rather "how to equip ourselves intelligently, starting today?".
