AI blog

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.

The pure SaaS model shows its limits: saturated market, slower growth, more demanding customers. At the same time, AI is paving the way for a new generation of businesses that merge technology and human expertise. This is called Service as a Platform (SaaP). This model no longer sells a tool, but a result. Find out why the most visionary players are adopting this approach, and how it could redefine your business.

Anticipating sales, avoiding stockouts, estimating the obsolescence of a product or forecasting demand: prediction is no longer science fiction, but a strategic lever for any data-driven company. In this article, learn how AI—from Machine Learning to Deep Learning to Language Models—is revolutionizing prediction, and how to choose the right approach for your business needs.

Faced with the frenzy around artificial intelligence, many are wondering if it is not a bubble. However, behind the fashion effect, AI is making a lasting impression in businesses. But before integrating AI into processes, one prerequisite is essential: having reliable, rich and well-structured data. This article explores why data is the foundation of any successful AI strategy and how businesses can prepare for it today.

All organizations seek to increase efficiency, reduce waste, and focus on adding value. But beyond classical methods, Artificial Intelligence is now opening up new perspectives. In this article, discover how AI can become a catalyst for Lean Management, with concrete examples and strategic courses of action.

Artificial intelligence is no longer a distant promise: it is already redesigning our jobs and our skills. Between new opportunities and a feeling of overwhelm, a LinkedIn study reveals how French professionals are experiencing this revolution. In this article, discover the most transformed sectors, the skills to be developed and the concrete strategies to succeed in the age of AI.

AI is disrupting employment, some positions are disappearing... but it is above all a transition. New jobs are emerging, productivity is increasing and data is becoming a strategic lever thanks to AI. Rather than a threat, it's an opportunity to reinvent work.