AI blog

The multiplicity of data formats (Excel, PDF, ERP) generates errors, information losses and limited analyses. To remedy this, automation is essential: OCR extraction, transformation scripts, a single repository, and standardized formats. The benefits are immediate: time savings (70%), reliable decisions and technological agility, turning this challenge into a competitive advantage. Trying again Claude can make mistakes. Be sure to check his answers.

The quality of the product repository is often the determining factor in the success of e-commerce projects, well beyond technology. However, aligning IT, business, and sales teams around these product attributes remains a major challenge. This article explores the challenges of this alignment and proposes concrete strategies to transform your framework into a real performance driver.

In an increasingly digitized economic environment, data management has become not only a technical and operational issue, but also a legal one. Regulations like the GDPR impose strict requirements on all businesses, while poor data quality is an often underestimated risk. This article explores compliance issues related to data and proposes concrete strategies to transform this regulatory constraint into business opportunities.

Data compartmentalization in business is one of the major obstacles to innovation and operational performance. These “data silos” limit collaboration, slow down decisions, and prevent the valorization of information capital. Learn how to effectively break down data silos to unleash your organization's potential and create true sustainable competitive advantage.

The migration or merging of databases is a critical moment in the digital transformation of businesses. Without careful attention to data quality, these projects can quickly turn into operational and financial nightmares. This practical guide introduces you to the challenges, mistakes to avoid and best practices to make your migrations a lasting success.

In the modern retail world, the quality of product sheets directly determines business performance. However, their management remains mostly manual, mobilizing up to 30% of the time of product teams. Artificial intelligence is now transforming this constraint into a competitive advantage, while freeing your teams from repetitive tasks to focus on strategy and innovation.

In an economic environment where responsiveness makes the difference, SMEs and ETIs that effectively structure their data gain a decisive competitive advantage. However, the majority of French companies continue to manage their information assets in a dispersed and obsolete manner. This article explains how to transform this data chaos into a real driver of growth, with concrete steps and measurable benefits for your organization.

Data is now considered the new black gold for businesses, but for many organizations, it remains trapped in crippling digital disorder. This data chaos generates invisible but substantial costs: time wasted searching for information, risky decisions based on erroneous data, and compromised agility. Discover how to identify this problem and, most importantly, how to transform this disorder into a true growth lever through a structured approach to your information assets.RéessayerClaude peut faire des erreurs. Assurez-vous de vérifier ses réponses.

AI diagnostic is a methodical 4-step approach allowing companies to identify and prioritize opportunities for applying artificial intelligence in their specific context. Through a structured process of 10 days of guidance (framing, assessment, use case identification, recommendations), organizations obtain a clear vision of the most relevant AI projects to develop and a concrete action plan for their implementation. This pragmatic approach transforms AI from an abstract concept into projects with measurable added value, while raising awareness among teams about the challenges of digital transformation.

It is now possible to use your data effectively without recruiting a data analyst. Thanks to AI and accessible tools, managers, CFOs or business managers can obtain automatic analyses, ask questions in natural language, and manage their business without technical skills.

This guide helps businesses choose the right AI dashboard platform by comparing the best solutions based on key criteria: quality of insights, simplicity, security, security, integration, and advanced features.

Many businesses make strategic decisions based on dashboards that are visually flawless but fed by incorrect or incomplete data. This article explores the risks associated with poor data quality, the invisible errors in visualization tools, and best practices for making reliable analyses and making truly informed decisions.

LoRa is a lightweight and effective fine-tuning method for adapting a language model to specific business needs. Unlike full fine-tuning, which is more expensive, LoRa allows rapid specialization with few resources. It is an ideal solution for businesses that want to customize an AI model without heavy infrastructure.

Data quality is an underestimated strategic driver. Complete, accurate, consistent and up-to-date data is essential to effectively manage your business, avoid reporting errors, and make your decisions reliable. This article presents a concrete method in three steps — audit, cleaning, governance — and shows how adapted tools (including AI) make it possible to structure your databases sustainably. The result: time savings, better internal coordination, and truly useful analyses. Clean data is the starting point for all sustainable performance.

ChatGPT now offers native integration with SharePoint (in beta for Plus, Pro, and Team users). This feature makes it possible to conduct in-depth searches of internal documents, to generate summaries, briefs or analyses based on data stored in SharePoint — while respecting access rights. A significant time-saver for teams working with information spread across multiple sites or departments.