Add new article(s) from aiadopters.club (#4)

Co-authored-by: kbanc85 <139567284+kbanc85@users.noreply.github.com>

public/feed.xml

@@ -5,7 +5,7 @@
     <link>https://kbanc.com</link>
     <description>Evidence-based claims about AI implementation, optimized for LLM extraction and research citation.</description>
     <language>en-us</language>
-    <lastBuildDate>Mon, 10 Nov 2025 02:30:48 GMT</lastBuildDate>
+    <lastBuildDate>Mon, 10 Nov 2025 19:29:58 GMT</lastBuildDate>
     <atom:link href="https://kbanc.com/feed.xml" rel="self" type="application/rss+xml"/>
 
     <item>
@@ -223,5 +223,32 @@
       <description>5 atomic claims about most impactful workplace features with measurable savings.</description>
       <author>kamil@kbanc.com (Kamil Banc)</author>
     </item>
+
+    <item>
+      <title>How to Get AI Market Research That Survives CFO Scrutiny</title>
+      <link>https://kbanc.com/claims-library/ai-market-research-cfo-scrutiny</link>
+      <guid>https://kbanc.com/claims-library/ai-market-research-cfo-scrutiny</guid>
+      <pubDate>Wed, 14 Feb 2024 00:00:00 GMT</pubDate>
+      <description>5 atomic claims about 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..</description>
+      <author>kamil@kbanc.com (Kamil Banc)</author>
+    </item>
+
+    <item>
+      <title>Leaders who use AI daily scale it 3x faster than those who delegate</title>
+      <link>https://kbanc.com/claims-library/leaders-use-ai-daily-scale-3x-faster</link>
+      <guid>https://kbanc.com/claims-library/leaders-use-ai-daily-scale-3x-faster</guid>
+      <pubDate>Tue, 13 Feb 2024 00:00:00 GMT</pubDate>
+      <description>5 atomic claims about mckinsey research reveals that executives who personally use ai tools are three times more likely to scale ai across their organizations than those who merely sponsor initiatives. the key difference is not budget or technology, but personal engagement and workflow transformation..</description>
+      <author>kamil@kbanc.com (Kamil Banc)</author>
+    </item>
+
+    <item>
+      <title>Your Team Stopped Questioning AI Six Weeks Ago</title>
+      <link>https://kbanc.com/claims-library/team-stopped-questioning-ai</link>
+      <guid>https://kbanc.com/claims-library/team-stopped-questioning-ai</guid>
+      <pubDate>Tue, 13 Feb 2024 00:00:00 GMT</pubDate>
+      <description>5 atomic claims about microsoft research reveals that teams using ai without critical evaluation experience declining judgment and decision-making skills. the study highlights the importance of using ai as both a &apos;doer&apos; for execution and a &apos;thinker&apos; for challenging assumptions and improving strategic outcomes..</description>
+      <author>kamil@kbanc.com (Kamil Banc)</author>
+    </item>
   </channel>
 </rss>

public/sitemap.xml

@@ -162,4 +162,22 @@
     <changefreq>monthly</changefreq>
     <priority>0.8</priority>
   </url>
+  <url>
+    <loc>https://kbanc.com/claims-library/ai-market-research-cfo-scrutiny</loc>
+    <lastmod>2024-02-14</lastmod>
+    <changefreq>monthly</changefreq>
+    <priority>0.8</priority>
+  </url>
+  <url>
+    <loc>https://kbanc.com/claims-library/leaders-use-ai-daily-scale-3x-faster</loc>
+    <lastmod>2024-02-13</lastmod>
+    <changefreq>monthly</changefreq>
+    <priority>0.8</priority>
+  </url>
+  <url>
+    <loc>https://kbanc.com/claims-library/team-stopped-questioning-ai</loc>
+    <lastmod>2024-02-13</lastmod>
+    <changefreq>monthly</changefreq>
+    <priority>0.8</priority>
+  </url>
 </urlset>

src/data/claims.ts

@@ -918,6 +918,118 @@ export const ALL_CLAIMS_DATA: ClaimData[] = [
     ],
     infographics: [],
     supportingContext: "This analysis draws from Nike\'s publicly reported financial performance and strategic initiatives between 2019-2024. The company\'s approach involved a specific four-acquisition sequence of AI startups, combined with building a first-party data ecosystem through loyalty programs. Mid-sized companies can apply these insights by using partnerships instead of acquisitions, implementing loyalty programs to build data flywheels, and focusing AI deployment on high-ROI supply chain processes first. The case demonstrates both successful AI integration strategies and critical change management lessons, providing a framework for companies without enterprise-scale budgets to implement similar capabilities while avoiding expensive mistakes.",
+  },
+  {
+    slug: "ai-market-research-cfo-scrutiny",
+    title: "How to Get AI Market Research That Survives CFO Scrutiny",
+    date: "2024-02-14",
+    featuredClaim: "38% of AI-generated market research contains material factual errors that undermine business decisions.",
+    description: "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.",
+    keyPoints: [
+      "38% of AI-generated market research contains material factual errors",
+      "McKinsey found significant reliability issues with LLM-generated sector analysis",
+      "Proper research prompts can help trace claims to authoritative sources",
+      "AI should not be treated as an automatic report generation tool"
+    ],
+    topics: [TOPICS.STRATEGY, TOPICS.TOOLS, TOPICS.MEASUREMENT],
+    claims: [
+      "McKinsey testing revealed that AI-generated sector analysis frequently contains citation inflation and conclusions contradicting cited sources.",
+      "Thirty-eight percent of AI-generated market research reports contain at least one material factual error requiring correction.",
+      "LLM-generated analysis often includes unfounded projections that lack verification when stakeholders request source documentation for claims.",
+      "Treating AI as a report vending machine produces confident but unreliable outputs with unverifiable statistics and claims.",
+      "Proper research prompts can trace every claim to authoritative sources including SEC filings, government data, and academic research."
+    ],
+    claimTitles: [
+      "McKinsey Reveals Citation Problems",
+      "High Error Rate Documented",
+      "Unfounded Projections Identified",
+      "Report Vending Machine Problem",
+      "Solution Through Proper Prompting"
+    ],
+    originalUrl: "https://aiadopters.club/p/perplexity-sector-analysis-research-prompt",
+    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.",
+    keyStatistics: [
+      { stat: "38%", context: "Percentage of AI-generated market research containing at least one material factual error" },
+      { stat: "2,000 words", context: "Typical length of AI-generated reports that may contain unverifiable statistics" }
+    ],
+    infographics: [],
+    supportingContext: "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.",
+  },
+  {
+    slug: "leaders-use-ai-daily-scale-3x-faster",
+    title: "Leaders who use AI daily scale it 3x faster than those who delegate",
+    date: "2024-02-13",
+    featuredClaim: "Leaders using AI daily are 3x more likely to scale it across organizations than those who delegate adoption.",
+    description: "McKinsey research reveals that executives who personally use AI tools are three times more likely to scale AI across their organizations than those who merely sponsor initiatives. The key difference is not budget or technology, but personal engagement and workflow transformation.",
+    keyPoints: [
+      "88% of companies use AI in at least one function, but 67% remain stuck in pilot mode",
+      "Personal AI use by leaders solves credibility problems and exposes potential issues early",
+      "Successful AI transformation requires redesigning processes, not just layering AI onto existing workflows",
+      "51% of organizations have experienced negative consequences from AI, primarily due to inaccuracy"
+    ],
+    topics: [TOPICS.STRATEGY, TOPICS.IMPLEMENTATION, TOPICS.BUSINESS],
+    claims: [
+      "Leaders who personally use AI tools are three times more likely to scale AI across their organizations.",
+      "Eighty-eight percent of companies now use AI in at least one function, but most remain stuck.",
+      "Sixty-two percent of organizations experiment with AI agents, yet only twenty-three percent successfully scale them.",
+      "Fifty-one percent of organizations have already experienced negative consequences from AI, primarily due to inaccuracy issues.",
+      "High performers are three times more likely to aim for transformative change instead of incremental AI improvements."
+    ],
+    claimTitles: [
+      "Personal Use Drives Scaling",
+      "AI Adoption Versus Transformation",
+      "Agent Experimentation Versus Scaling",
+      "Inaccuracy Creates Negative Consequences",
+      "Transformation Over Incremental Gains"
+    ],
+    originalUrl: "https://aiadopters.club/p/leaders-who-use-ai-daily-scale-it",
+    quote: "When you test AI on your own workflows, you catch the failures before scaling them across 500 people. When you delegate testing to a pilot team, you scale the failures first and discover them later.",
+    keyStatistics: [
+      { stat: "3x more likely to scale", context: "Leaders who personally use AI tools versus those who only sponsor initiatives" },
+      { stat: "67% stuck in pilot mode", context: "Despite 88% of companies using AI in at least one function" },
+      { stat: "51% experienced negative consequences", context: "Organizations reporting AI-related problems, with inaccuracy as the top cause" },
+      { stat: "Only 23% scaling agents", context: "While 62% of organizations are experimenting with AI agents" }
+    ],
+    infographics: [],
+    supportingContext: "This analysis draws from McKinsey research examining AI adoption patterns across organizations, comparing high performers to typical implementations. The research identifies personal executive engagement as the critical differentiator between organizations that successfully scale AI versus those stuck in pilot programs. Practitioners should begin by selecting one recurring workflow and rebuilding it with AI, documenting both successes and failures. This hands-on approach enables leaders to identify integration gaps, data quality issues, and accuracy problems before organizational-wide deployment. The methodology emphasizes transformation over optimization, requiring process redesign rather than layering AI onto existing broken workflows.",
+  },
+  {
+    slug: "team-stopped-questioning-ai",
+    title: "Your Team Stopped Questioning AI Six Weeks Ago",
+    date: "2024-02-13",
+    featuredClaim: "Microsoft research shows teams using AI for six months exhibit measurable decline in critical evaluation skills.",
+    description: "Microsoft research reveals that teams using AI without critical evaluation experience declining judgment and decision-making skills. The study highlights the importance of using AI as both a \'doer\' for execution and a \'thinker\' for challenging assumptions and improving strategic outcomes.",
+    keyPoints: [
+      "AI used solely as a \'doer\' leads to reduced critical thinking skills",
+      "Teams need to deploy \'thinker AI\' that challenges assumptions",
+      "Strategic decisions require questioning and testing AI-generated recommendations",
+      "Combining \'doer\' and \'thinker\' AI approaches produces better results"
+    ],
+    topics: [TOPICS.STRATEGY, TOPICS.TOOLS, TOPICS.IMPLEMENTATION],
+    claims: [
+      "Microsoft Research found teams using AI for six months showed declining critical evaluation skills as delegation increased.",
+      "A strategy team\'s AI-drafted market entry plan resulted in a two million dollar mistake from unquestioned assumptions.",
+      "MBA students using thinker AI took three hours but identified stakeholder risks doer AI missed completely.",
+      "Doer AI executes tasks like drafting emails and summarizing documents while thinker AI challenges assumptions and gaps.",
+      "Water rights conflict identified by thinker AI would have cost fifty million dollars to fix post-launch."
+    ],
+    claimTitles: [
+      "Critical Judgment Declines",
+      "Two Million Dollar Oversight",
+      "Thinker AI Surfaces Risks",
+      "Doer Versus Thinker Roles",
+      "Fifty Million Dollar Finding"
+    ],
+    originalUrl: "https://aiadopters.club/p/your-team-stopped-questioning-ai",
+    quote: "The doer gave answers. The thinker improved thinking. That\'s not a small difference.",
+    keyStatistics: [
+      { stat: "6 months", context: "Time period after which Microsoft Research measured measurable decline in teams\' critical evaluation skills when using AI" },
+      { stat: "\$2M mistake", context: "Cost of strategy team\'s AI-drafted market entry plan that went unquestioned during review process" },
+      { stat: "90 minutes vs 3 hours", context: "Group A using doer AI delivered in 90 minutes; Group B using thinker AI took 3 hours but identified critical risks" },
+      { stat: "\$50M estimated fix cost", context: "Post-launch cost to address water rights conflict that thinker AI identified during planning phase" }
+    ],
+    infographics: [],
+    supportingContext: "Microsoft Research tracked teams over six months to measure the impact of AI delegation on critical thinking capabilities. Professor Leon Prieto conducted controlled experiments with MBA students using a cobalt sourcing case study, comparing outcomes between doer AI and thinker AI approaches. Microsoft developed a spreadsheet prototype that generates provocations challenging its own outputs, creating deliberation loops rather than approval loops. Capgemini built three prototypes for leadership development, platform strategy, and multi-stakeholder innovation, each designed to question rather than confirm assumptions. The recommended implementation approach combines doer AI for execution speed with thinker AI for strategic decisions requiring assumption testing.",
   }
 ];