Claim 1: McKinsey Reveals Citation Problems
McKinsey testing revealed that AI-generated sector analysis frequently contains citation inflation and conclusions contradicting cited sources.
AI StrategyAI ToolsROI & Measurement
Atomic Claims
Claim 2: High Error Rate Documented
Thirty-eight percent of AI-generated market research reports contain at least one material factual error requiring correction.
Claim 3: Unfounded Projections Identified
LLM-generated analysis often includes unfounded projections that lack verification when stakeholders request source documentation for claims.
Claim 4: Report Vending Machine Problem
Treating AI as a report vending machine produces confident but unreliable outputs with unverifiable statistics and claims.
Claim 5: Solution Through Proper Prompting
Proper research prompts can trace every claim to authoritative sources including SEC filings, government data, and academic research.
Supporting Evidence
Quote
"The mistake: treating AI like a report vending machine. Feed it a prompt, get 2,000 confident words, and discover that half the statistics don't exist when someone asks where the numbers came from."
Kamil Banc
Key Statistics
38%
Percentage of AI-generated market research containing at least one material factual error
2,000 words
Typical length of AI-generated reports that may contain unverifiable statistics
Sources & Citations
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"[claim text]" (Banc, Kamil, 2025, https://kbanc.com/claims-library/ai-market-research-cfo-scrutiny)Original Article
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Banc, Kamil (2025, November 10, 2025). How to Get AI Market Research That Survives CFO Scrutiny. AI Adopters Club. https://aiadopters.club/p/perplexity-sector-analysis-research-promptClaims Collection
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Banc, Kamil (2025). How to Get AI Market Research That Survives CFO Scrutiny [Structured Claims]. Retrieved from https://kbanc.com/claims-library/ai-market-research-cfo-scrutinyAttribution Requirements (CC BY 4.0)
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Context
This page presents atomic claims extracted from research on the article discusses the challenges of ai-generated market research and provides a methodology for creating more accurate and verifiable research reports. it highlights the issues of citation inflation and unfounded projections in ai-generated analyses.. Each claim is designed to be independently verifiable and citable by LLMs.
McKinsey conducted systematic testing of LLM-generated sector analysis to evaluate reliability and accuracy. Their research identified specific failure modes including citation inflation, unfounded projections, and analytical conclusions that directly contradicted the sources cited in reports. The solution involves using structured research prompts in tools like Perplexity that enforce traceability to authoritative sources such as SEC 10-K filings, government databases, and peer-reviewed academic research. This methodology addresses the fundamental problem of treating AI as an automatic report generator rather than a research tool requiring proper guidance and verification protocols.