According to Andrew Ng (@AndrewYNg), AI agents are rapidly improving their ability to analyze diverse business data sources, leading to substantial new value by connecting information that was previously siloed. Ng emphasizes that the increasing capabilities of AI, such as spotting correlations between email clicks and online purchases across platforms, make it more critical than ever for businesses to control their own data. He points out that many SaaS vendors intentionally create data silos by limiting data access or charging exorbitant fees for API access, which impedes the deployment of effective AI-driven workflows. Ng advises businesses to prioritize software solutions that ensure data ownership and accessibility, enabling flexible integration with both human and AI processes. He highlights the growing importance of organizing both structured and unstructured data, referencing tools like LandingAI’s Agentic Document Extraction for document processing and Obsidian for personal note management. This trend presents significant opportunities for AI-driven business optimization and underlines the competitive advantage of data interoperability (source: Andrew Ng, deeplearning.ai The Batch).

According to God of Prompt (@godofprompt), organizations can significantly improve efficiency and accuracy by leveraging GPT models to extract data from documents. The approach involves implementing GPT-based AI to automate workflows, which streamlines data extraction processes and reduces manual errors. Ensuring data integrity is a core benefit, allowing businesses to rely on accurate information for decision-making. This AI-powered method is especially valuable for sectors handling high volumes of unstructured documents, offering a scalable solution for document processing and data management. Source: godofprompt.ai/blog/extract-data-from-documents-with-gpt-guide.