Retrieval Augmented Generation (RAG) is an intelligent assistant that helps you find exactly what you’re looking for in a pile of medical records. Like an X-ray shows you hidden details inside the body, RAG helps you quickly extract precise information from complex data. RAG provides instant, accurate answers—often visualized in charts or summaries that require analysts to produce manually. RAG combines two AI capabilities—retrieval systems and generative models.

Let’s say you’re leading a company that receives thousands of documents daily. These documents come in various formats like Excel, PDFs, CSVs, and more. And they differ in terms of layout. Before you can analyze the data, your team spends hours sorting, cleaning, and preparing these documents. Most of their time is spent preparing the documents for integration into business systems. Then, a colleague shares how intelligent document processing helped him save time and boost productivity.

Pharmaceutical industry leaders are adopting new artificial intelligence (AI) technologies and increasing process efficiency. The Infosys report on AI adoption shows that pharmaceuticals are among the most mature industries in Al adoption. In the same report, 40 percent of the respondents claimed their organizations had deployed Al and that it was working as expected. AI-powered features help them manage massive volumes of pharmaceutical data with great accuracy and speed.

AI has entered many aspects of data integration, including data mapping. AI data mapping involves smart identification and mapping of data from one place to another. Sometimes, creating data pipelines manually can be important. The process might require complex transformations between the source and target schemas while setting up custom mappings.

Picture this: You are part of a BI team at a global garment manufacturer with dozens of factories, warehouses, and stores worldwide. Your team is tasked with extracting insights from company data. You begin the ETL (Extract, Transform, Load) process but find yourself struggling with the manual effort of understanding table structures and revisiting and modifying pipelines due to ongoing changes in data sources or business requirements.

The year is 1424. You’re shipping goods across the world, and the ship in question gives you a bill of lading. It’s a piece of paper containing details about what your goods are, where you’re shipping them from, and where they’re headed. Fast forward to 2024. You’re shipping your goods across the world, and the shipping company gives you a bill of lading. It’s still (most likely) a piece of paper.

Data pipeline automation plays a central role in integrating and delivering data across systems. The architecture is excellent at handling repetitive, structured tasks, such as extracting, transforming, and loading data in a steady, predictable environment, because the pipelines are built around fixed rules and predefined processes. So, they will continue to work if you maintain the status quo, i.e., as long as your data follows a consistent structure.