Effective AI is based on targeted data, carefully chosen. By combining business and tech expertise, companies transform AI into a strategic and secure lever.
Today, artificial intelligence (AI) has become essential to transform data into strategic actions and reliable results. Just as a garden requires precise watering to produce good fruit, an efficient AI depends directly on quality data, carefully chosen. What many do less is that selecting and providing this data effectively represents a real challenge.
The complexity of corporate data
Company challenges are very different from those of major players in the consumer AI. The latter have immense volumes of easily accessible data, notably from the web, and also generating artificially data thanks to learning supervised by humans to continuously improve their systems.
On the other hand, companies must manage often dispersed, partitioned and complex data to use. These data come from multiple sources such as old operational systems, archives accumulated over several years, or various platforms of financial management, human resources, supply chain and customer relations. In addition, strict regulatory constraints framing the use of personal data requires rigorous governance to guarantee confidentiality and conformity.
The importance of choosing your data well
The quality of the results obtained by AI depends directly on the data used. Rather than submerging AI with an excessive amount of data, it is essential to adopt a targeted and strategic approach. This “data minimalism” makes it possible to simplify their processing, to accelerate AI responses and to improve its global efficiency. It is also a necessity to comply with regulations and ensure the relevance of the developed models.
Associate business expertise with technological skills
An efficient AI requires more than advanced technology: it requires close collaboration between business teams, who know the real strategic challenges of the company, and technological teams, which master the structuring and analysis of data. Together, they identify the processes to be improved, choose precisely the useful data and determine the best architecture for their AI models, such as a model based on the generation increased by recovery (RAG) or the improvement of an existing model.
Concrete example: Improve the detection of frauds
Take the case of a financial company wishing to improve its detection of fraud. Its algorithms generate useful alerts but often too complex to interpret directly. By associating a business expert with a data engineer, the company can refine a natural language treatment model (LLM) to transform these technical alerts into clear and easily exploitable explanations. The model is drawn to a specific set of selected data, thus ensuring regulatory compliance and operational relevance. The business expert gradually validates the results to guarantee their precision.
Thanks to this collaborative approach, the company benefits from a tailor -made AI, perfectly suited to its specific needs, while keeping the total control of its data and respecting the rules in force. Unlike the consumer AI models, this targeted and secure solution significantly improves the effectiveness of analysts.
Towards a mastered and pragmatic AI
Mastering AI therefore does not only mean adopting the ever -efficient models, but above all understand how to use your own data effectively. By structuring information in a targeted way and combining business expertise and technological know-how, companies can transform AI into a real strategic lever. A pragmatic and thoughtful approach makes it possible not only to improve the performance of models, but also to guarantee reliable, exploitable and aligned results with the real needs of the teams.
