AI Blog

The emergence of generative AI, especially since the launch of ChatGPT, is profoundly transforming the developer market. According to data from the Bureau of Labor Statistics, traditional programmer positions fell by -27.5%, while senior developer and architect positions fell by only -0.3%. This trend shows that automation mostly impacts basic programming tasks, while skills in system design, complex problem solving, and collaboration remain essential. The developers who will succeed will be those who know how to use AI as an efficiency tool, while continuing to learn and refine their analytical and organizational skills. For companies, this means training their teams in generative AI tools and recruiting profiles capable of bringing real added value.

Tokens are the units of text that an artificial intelligence model like GPT-4 uses to understand and generate responses. They can be whole words, parts of words, or even punctuations. Each interaction with the AI consumes tokens, both for input and for output. Understanding this concept is essential to optimize the use of AI models, especially in APIs where each token used has a cost. The article explains the concept using an excerpt from The Little Prince and provides tips for using tokens properly.

According to Gartner, 85% of data projects fail, often due to a lack of a clear objective, poorly exploited data, or a lack of buy-in from business teams. To succeed, it is essential to define a specific business need, to structure and clean the data beforehand, and to involve the teams from the start. The tool must be integrated into existing processes and evolve with the company to ensure a lasting impact.

Mistral AI launches Mistral OCR, a new optical character recognition solution that goes beyond simple text extraction by integrating the management of tables, equations and complex layouts. With an accuracy of 98.96% and a processing speed of up to 2,000 pages per minute, this tool is a reference for automating document processing. It has applications in scientific research, heritage preservation, customer service, and regulatory document management. Available on The Platform, it will soon be accessible via the main cloud providers.

Google has extended its AI Overviews feature to several European countries, including Switzerland, Germany, and Italy. These summaries generated by artificial intelligence offer direct answers to users, raising concerns about the impact on organic traffic and SEO strategies. To adapt, advertisers and content creators will have to adjust their approaches and offer unique added value.

Finding relevant and reliable information on the Internet is a constant challenge. Between the multitude of sources, the sorting of results and the risk of misinformation, the search for information takes time and requires increased expertise. That's where Deep Search, ChatGPT's new feature, comes in. Unlike traditional search engines that list links, Deep Search goes further: it collects, analyzes, and synthesizes information from the web to provide a structured and sourced document. A promising advance for professionals, but also a solution that poses certain limitations. We tested Deep Search to understand how it works and assess its effectiveness.

AI in Business Intelligence should not flood the user with an avalanche of useless indicators, but on the contrary prioritize and contextualize data to make it usable. An effective approach is based on fewer numbers, better explained, better context, and intelligent prioritization of insights. StratBoard™ embodies this philosophy by putting forward relevant and actionable data, rather than an excess of information that is difficult to interpret.

AI is a powerful tool that can either accelerate our skills or make us dependent if we rely too much on it. Rather than seeing it as a substitute, it should be used as a lever by maintaining control and strengthening our own skills. The balance between automation and human expertise is key to getting the most out of it without losing control.

The debate between open-source AI and proprietary models is often presented as a binary choice, when in reality, everything depends on the need. Proprietary models offer performance and simplicity, while open-source models offer flexibility and control. The challenge is not to choose a side, but to adopt the most appropriate solution according to business constraints, costs and the need for data control.

Generative AI is transforming the use of data in business, but it raises security questions. Rather than avoiding it, it is possible to use it with confidence thanks to proven methods: on-premise models for total control, secure APIs guaranteeing the confidentiality of requests, advanced encryption to protect exchanges, and hybrid approaches combining performance and compliance. The challenge lies in the intelligent integration of AI, ensuring data security at each stage.

SaaS solutions have become ubiquitous, but with the rise of AI, their role is shifting. Large Language Models and autonomous agents allow businesses to automate tasks, interact directly with data, and create custom tools without relying on predefined software interfaces. This raises the question: is SaaS becoming a mere infrastructure layer for AI-driven applications? To stay relevant, SaaS providers must specialize in specific industry verticals and integrate AI with consulting to ensure structured, high-quality data. The future of SaaS may be less visible, more modular, and fully AI-powered.

A leading business school organized three webinars to educate its 320 employees on the challenges and opportunities of artificial intelligence. The goal was to harmonize knowledge, demystify AI, and introduce a roadmap for its integration into professional practices. Through an interactive format and case studies tailored to different profiles (faculty, administrative staff, and support teams), this training helped unite teams around a common AI vision.

Strat37 organized a seminar for 60 executives and managers from a leading insurance brokerage group to raise awareness of the challenges and opportunities of artificial intelligence. The goal was to demystify AI, illustrate its real-world applications, and help executives understand how to integrate this technology into their strategies. Through an interactive format and sector-specific use cases, these leaders identified immediate opportunities and outlined a roadmap for AI adoption within their organization.

A major transport company aimed to improve the monitoring of its commercial and operational performance. The goal was to automate margin and revenue calculations while simplifying access to critical data for faster and more efficient decision-making. With an AI-powered solution, we implemented an interactive dashboard enabling real-time logistics tracking, optimizing costs, and maximizing profitability.

Our client wanted to accelerate access to commercial data, enabling teams to make decisions faster without relying on complex reports or specialized analysts. By integrating a generative AI tool, we automated data extraction and interpretation in real-time, allowing instant analysis and strategic recommendations. This approach reduced decision-making time and improved business responsiveness.