AI blog

The real estate sector is undergoing a silent but radical transformation. Behind transactions, square meters and strategic trade-offs, a new reality is emerging: data has become the invisible driver of performance, advice and productivity. However, in the field, the majority of actors still operate in a fragmented landscape: scattered Excel files, heterogeneous frameworks, analyses carried out “in other words by experts” in the absence of consolidated data. Faced with these challenges, some leaders decided to accelerate: structure their data assets, deploy concrete AI tools and raise their service level. Their vision, their difficulties and their ambitions reveal the major challenges that the entire real estate sector must now face.

Each building produces a mountain of invisible data. Among them, supplier invoices represent an exceptionally rich source of information, but still largely underused. Their manual processing, which is often fragmented, prevents real estate departments from obtaining a clear vision of their costs and their optimization levers. Artificial intelligence is changing this reality. By automating the reading, analysis and classification of invoices, it transforms an administrative process into a complete management tool. This paradigm shift is not limited to saving time: it gives financial data back its primary role, informing decisions, improving profitability and strengthening the overall performance of the real estate stock.

Since 2025, the BACS decree has required the implementation of energy automation systems in tertiary buildings. By combining these tools with AI, companies are transforming a regulatory constraint into a driver of performance and profitability.

L’intelligence artificielle n’est plus un gadget technologique : c’est un accélérateur stratégique. Mais encore faut-il savoir où commencer, comment structurer ses données, et avec qui avancer. C’est là que les agences IA et data font toute la différence : elles traduisent la complexité technologique en impact métier.

The most successful business departments are no longer content to look at their internal indicators. They know that data alone is not enough: it must be placed in a larger environment in order to become decision-making. Enriching your data with external information — open data, weather, weather, transport, market — makes it possible to understand the “why” behind the results, to anticipate variations and to act more quickly. Data enrichment thus becomes a strategic lever, well beyond simple analysis.

Artificial intelligence fascinates, promises and is gradually becoming an essential performance driver. But behind the technological revolution, a simple truth stands out: no AI can be smarter than the data that feeds it. Before investing in models, co-pilots or augmented dashboards, companies must face a challenge that is often underestimated: the quality of their data. Because projects rarely fail because of algorithms — they fail because of a clean, coherent and governed data base. This article brings together the key figures that remind us why data control is the real starting point of any AI strategy.

Where the majority of companies limit themselves to their internal data, the most agile combine their information with external sources — open data, weather, transport, market. This simple reflex turns numbers into understanding, accelerates decision-making and redefines the role of business departments.

For years, businesses sought to accumulate dashboards, reports, and metrics, believing that more data meant more performance. However, in a world where information flows at the speed of light, true competitive advantage is no longer measured by the quantity of data produced, but by the speed with which an organization can transform this data into decisions. The time between data and decision has become the most strategic KPI in modern business.

A company's performance no longer depends only on the power of its tools, but on how they interact. In an environment saturated with technology, real value now comes from orchestration, not accumulation. It is this idea that makes Service-as-a-Platform a model for the future. Rather than a succession of applications, it offers a living platform, where data, analysis and artificial intelligence are based on business needs. This model is redefining how businesses design their systems, teams, and even decisions.

Many businesses want to “analyze their data.” But analyze what, if the data is incomplete or inconsistent? In the majority of data projects, errors do not come from analysis tools, but from the quality of the data beforehand. That's why, at Strat37, our golden rule always remains the same: Build Reliability → Enrich → Analyze. Each stage consolidates the next, and skipping one of them is building on sand.

Many businesses focus their efforts on artificial intelligence models, but true maturity doesn't happen there. It is built on data: its quality, its coherence, its structure. The most advanced organizations don't chase the latest model, they invest in the reliability of their data. This is where the real transformation takes place. Learn why AI maturity depends on the data first, not the model.

For the last two years, we have read everywhere that artificial intelligence will revolutionize productivity. The numbers are raining down, the studies are coming one after the other, and the tools are multiplying. But in the field, in a company that lives and breathes data and AI like Strat37, the question is more complex: yes, AI saves us time, but not always where we think it is, and not without effort. This article offers honest feedback: what AI really does for us, what it doesn't do, and above all, what it's changing in the way we work.

Do your databases contain duplicates or inconsistencies? Learn how artificial intelligence is transforming data preparation and reliability into an automated, fast, and cost-effective process. An essential item for businesses that want to regain control over their data.

To succeed in an artificial intelligence project, it is not enough to deploy a successful model. It is still necessary to have a solid data infrastructure and a fluid collaboration between two complementary profiles: the Data Engineer and the Machine Learning Engineer. This article explores their roles, interactions and the technical skills that are essential to make AI a sustainable strategic lever.

This is THE question that most leaders and innovation managers are asking themselves today. Between rare technical profiles, AI consultants, and employees in need of training, it's hard to know where to start. In this article, we help you identify the right profiles and structure an AI team adapted to your reality, whether you are an SME, a large group or a startup. Read the full article to discover the 5 key profiles that make it possible to effectively integrate AI into a business.