AI is making expensive mistakes with business data. Here’s a fix
Too many AI tools analysing company financials are working with messy PDFs and unstructured text, leading to hallucinations and errors that could cost millions. Meanwhile, there’s a goldmine of clean, structured financial data sitting in XBRL filings that AI systems have (too often) been ignoring.
The solution? Model Context Protocol (MCP) servers. This new approach plugs AI tools directly into authoritative XBRL data sources, cutting out the noise and delivering accurate, traceable answers. Want to compare Tesla’s and Ford’s liquidity ratios? Ask in plain language and get an instant, accurate response with full traceability back to the source filings.
This is a process that promises precision over guesswork. No more dodgy scraping, no more opaque AI outputs, no more wondering if your AI is hallucinating about crucial numbers. Just solid, explainable analysis grounded in real, regulator-governed numbers with full traceability.
Explore how MCP bridges the gap between XBRL and AI in our new blog by Best Practices Board Chair Janis Steinmann and member José Antonio Huizar Moreno.
