Add new article(s) from aiadopters.club (#31)

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

public/feed.xml

@@ -5,9 +5,18 @@
     <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>Wed, 24 Dec 2025 00:59:43 GMT</lastBuildDate>
+    <lastBuildDate>Thu, 25 Dec 2025 00:59:56 GMT</lastBuildDate>
     <atom:link href="https://kbanc.com/feed.xml" rel="self" type="application/rss+xml"/>
 
+    <item>
+      <title>Hallmark Spent 115 Years Selling Effort, Then AI Showed Up</title>
+      <link>https://kbanc.com/claims-library/hallmark-spent-115-years-selling-effort-then-ai-showed-up</link>
+      <guid>https://kbanc.com/claims-library/hallmark-spent-115-years-selling-effort-then-ai-showed-up</guid>
+      <pubDate>Wed, 24 Dec 2025 00:00:00 GMT</pubDate>
+      <description>5 atomic claims about hallmark demonstrates a unique ai strategy focused on operational improvement rather than customer-facing generative tools. by making ai invisible and focusing on relationship tracking, they&apos;ve maintained the human touch in greeting card production while leveraging machine learning behind the scenes..</description>
+      <author>kamil@kbanc.com (Kamil Banc)</author>
+    </item>
+
     <item>
       <title>The AI Skill That Actually Gets You Hired in 2026</title>
       <link>https://kbanc.com/claims-library/ai-skill-hired-2026</link>

public/sitemap.xml

@@ -2,19 +2,19 @@
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     <loc>https://kbanc.com/</loc>
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     <loc>https://kbanc.com/claims-library</loc>
-    <lastmod>2025-12-24</lastmod>
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     <loc>https://kbanc.com/claims-library/all</loc>
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@@ -342,4 +342,10 @@
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+  <url>
+    <loc>https://kbanc.com/claims-library/hallmark-spent-115-years-selling-effort-then-ai-showed-up</loc>
+    <lastmod>2025-12-24</lastmod>
+    <changefreq>monthly</changefreq>
+    <priority>0.8</priority>
+  </url>
 </urlset>

src/data/claims.ts

@@ -2032,6 +2032,44 @@ export const ALL_CLAIMS_DATA: ClaimData[] = [
     ],
     infographics: [],
     supportingContext: "This analysis draws from a Stanford lecture featuring Andrew Ng and Lawrence Moroney, who has interviewed hundreds of candidates across Google, Microsoft, and startups. The insights reflect real hiring patterns and organizational structure changes at leading AI companies. For practitioners, this means prioritizing three pillars: deep understanding of both technical and market dynamics, clear business focus that connects work to outcomes, and a bias toward delivery over credentials. The practical application involves building portfolios that demonstrate business judgment, managing technical debt proactively, and developing the ability to filter signal from noise in an increasingly hype-driven field.",
+  },
+  {
+    slug: "hallmark-spent-115-years-selling-effort-then-ai-showed-up",
+    title: "Hallmark Spent 115 Years Selling Effort, Then AI Showed Up",
+    date: "2025-12-24",
+    featuredClaim: "Hallmark sells 6 billion cards yearly using invisible AI for operations while keeping human sentiment intact.",
+    description: "Hallmark demonstrates a unique AI strategy focused on operational improvement rather than customer-facing generative tools. By making AI invisible and focusing on relationship tracking, they\'ve maintained the human touch in greeting card production while leveraging machine learning behind the scenes.",
+    keyPoints: [
+      "Hallmark uses \'Preservationist Innovation\' to protect the human core of their product",
+      "Their \'Recipient Graph\' recommendation system tracks relationship history instead of purchase history",
+      "AI is strategically applied to backend operations, not customer-facing interactions",
+      "The company prioritizes removing friction through invisible AI implementations"
+    ],
+    topics: [TOPICS.STRATEGY, TOPICS.BUSINESS, TOPICS.IMPLEMENTATION],
+    claims: [
+      "Hallmark moves six billion greeting cards annually despite free messaging alternatives like WhatsApp and iMessage being available.",
+      "Hallmark\'s Recipient Graph tracks relationship history for gift recipients rather than tracking the buyer\'s own purchase history.",
+      "Hallmark\'s infrastructure stack using invisible AI reduced their total cost of ownership by sixty percent overall.",
+      "Hallmark discontinued Video Greetings product by twenty twenty-five because scanning QR codes created too much user friction.",
+      "Sign and Send uses computer vision to extract handwritten messages and prints them on physical cards automatically."
+    ],
+    claimTitles: [
+      "Traditional Cards Still Thrive",
+      "Recipient-Focused Recommendation System",
+      "Sixty Percent Cost Reduction",
+      "Video Greetings Product Failure",
+      "Invisible AI in Sign-Send"
+    ],
+    originalUrl: "https://aiadopters.club/p/hallmark-spent-115-years-selling",
+    quote: "AI should remove friction, not add it.",
+    keyStatistics: [
+      { stat: "6 billion cards annually", context: "Hallmark\'s current yearly card sales volume despite free digital messaging alternatives" },
+      { stat: "60% cost reduction", context: "Total cost of ownership decrease achieved through invisible AI infrastructure implementation" },
+      { stat: "\$4 billion company", context: "Hallmark\'s current valuation after 115 years in the greeting card industry" },
+      { stat: "115 years", context: "Length of time Hallmark has operated in the greeting card market" }
+    ],
+    infographics: [],
+    supportingContext: "Hallmark\'s \'Preservationist Innovation\' framework represents a methodologically distinct approach to AI adoption that prioritizes backend optimization over customer-facing generative features. The company\'s data team, led by executives like Chai Pallapothula, developed custom relationship-tracking algorithms that create shadow profiles for gift recipients rather than buyers themselves. This approach is particularly relevant for SMB operators in gifting, personalization, or relationship-driven commerce sectors where standard collaborative filtering fails. Practitioners can apply this methodology by identifying which aspects of their product embody core customer values that should remain human-driven, then deploying AI exclusively to reduce operational friction in delivery and fulfillment.",
   }
 ];