¿Cómo manejar la complejidad de los datos empresariales en la era de la inteligencia artificial?

<img alt="" class="webfeedsFeaturedVisual wp-post-image" height="432" src="https://developer-blogs.nvidia.com/wp-content/uploads/2026/02/retrieval-augmented-generation-768x432.png" style="display: block; margin-bottom: 5px; clear: both;" title="retrieval-augmented-generation" width="768" />Enterprise data is inherently complex: real-world documents are multimodal, spanning text, tables, charts and graphs, images, diagrams, scanned pages, forms,...<img alt="" class="webfeedsFeaturedVisual wp-post-image" height="432" src="https://developer-blogs.nvidia.com/wp-content/uploads/2026/02/retrieval-augmented-generation-768x432.png" style="display: block; margin-bottom: 5px; clear: both;" title="retrieval-augmented-generation" width="768" /><p>Enterprise data is inherently complex: real-world documents are multimodal, spanning text, tables, charts and graphs, images, diagrams, scanned pages, forms, and embedded metadata. Financial reports carry critical insights in tables, engineering manuals rely on diagrams, and legal documents often include annotated or scanned content. Retrieval-augmented generation (RAG) was created to ground…</p> <p><a href="https://developer.nvidia.com/blog/build-ai-ready-knowledge-systems-using-5-essential-multimodal-rag-capabilities/" rel="nofollow" target="_self">Source</a></p>