Meet Your New Client: Writing Reports for AI -- Benchmarking Information Loss in Market Research Deliverables

2025-08-17

AI Summary

This study addresses the AI-readability bottleneck of traditional market research deliverables (PDF/PPTX) in knowledge management systems when used with retrieval-augmented generation (RAG). It systematically evaluates information fidelity loss—particularly for non-textual elements such as charts—during conversion to Markdown, focusing on factual question-answering performance. Using an end-to-end benchmarking framework integrating RAG pipelines, LLM-based QA evaluation, and cross-format conversion analysis, we find that text extraction is robust, but structural and semantic information from charts is severely degraded, impairing accurate insight retrieval. We thus propose the “AI-native deliverable” paradigm, advocating AI-parsability–driven redesign of report structure and metadata standards. This work provides the first quantitative characterization of the modality gap introduced by format conversion, establishing both theoretical grounding and practical guidelines for AI-ready research deliverables. (149 words)

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Knowledge Representation and Reasoning: Knowledge Acquisition
• Humans and AI: AI for Accessibility

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

Abstract

As organizations adopt retrieval-augmented generation (RAG) for their knowledge management systems (KMS), traditional market research deliverables face new functional demands. While PDF reports and slides have long served human readers, they are now also "read" by AI systems to answer user questions. To future-proof reports being delivered today, this study evaluates information loss during their ingestion into RAG systems. It compares how well PDF and PowerPoint (PPTX) documents converted to Markdown can be used by an LLM to answer factual questions in an end-to-end benchmark. Findings show that while text is reliably extracted, significant information is lost from complex objects like charts and diagrams. This suggests a need for specialized, AI-native deliverables to ensure research insights are not lost in translation.

Problem

• Evaluating information loss in market research deliverables for AI systems
• Comparing PDF and PPTX document conversion effectiveness for LLM usage
• Addressing information loss from complex objects like charts and diagrams

Innovation

• RAG systems benchmark information loss
• PDF and PPTX converted to Markdown
• AI-native deliverables for complex objects