<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" version="2.0">
  <channel>
    <title>LNS Research Industrial Transformation and Operational Excellence Blog</title>
    <link>https://blog.lnsresearch.com</link>
    <description>LNS Research blog is an informal environment for analysts to share thoughts on a range of manufacturing technology and business topics.</description>
    <language>en-us</language>
    <pubDate>Tue, 23 Jun 2026 15:00:00 GMT</pubDate>
    <dc:date>2026-06-23T15:00:00Z</dc:date>
    <dc:language>en-us</dc:language>
    <item>
      <title>Scaling AI Trust &amp; Plant Credibility: Chief Sustainability Officer CTA</title>
      <link>https://blog.lnsresearch.com/building-ai-trust-plant-credibility-chief-sustainability-officers-cta</link>
      <description>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://blog.lnsresearch.com/building-ai-trust-plant-credibility-chief-sustainability-officers-cta" title="" class="hs-featured-image-link"&gt; &lt;img src="https://blog.lnsresearch.com/hubfs/Which%20of%20The%20Following%20Best%20Describes%20How%20You%20Feel%20About%20AI_Featured%20Image-1.png" alt="Which of the following bests describes how you feel about AI" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p style="text-align: left;"&gt;  &lt;span&gt;AI is often presented as the next breakthrough for manufacturing. Boards demand it, CEOs promote it, and vendors sell it. On plant floors, however, workers cite tools that don’t reflect operational reality and mistrust recommendations. “Efficiency” projects feel disconnected from daily work, safety, and lasting impact. This has widened the&amp;nbsp;credibility gap and serves as a wake-up call for executives investing in AI (Figure 1). &lt;/span&gt;&lt;/p&gt; 
&lt;h5 style="line-height: 15.693334px; text-align: center;"&gt;&lt;span style="color: #112432;"&gt;&lt;strong&gt;Figure 1:&lt;/strong&gt; &lt;span style="font-weight: bold;"&gt;Surveys show a significant gap in how AI is being perceived,&amp;nbsp;w&lt;/span&gt;&lt;span style="font-weight: bold;"&gt;ith anxious workers on the shop floor and excited execs on the top floor.&lt;/span&gt;&lt;/span&gt;&lt;/h5&gt;</description>
      <content:encoded>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://blog.lnsresearch.com/building-ai-trust-plant-credibility-chief-sustainability-officers-cta" title="" class="hs-featured-image-link"&gt; &lt;img src="https://blog.lnsresearch.com/hubfs/Which%20of%20The%20Following%20Best%20Describes%20How%20You%20Feel%20About%20AI_Featured%20Image-1.png" alt="Which of the following bests describes how you feel about AI" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p style="text-align: left;"&gt;  &lt;span&gt;AI is often presented as the next breakthrough for manufacturing. Boards demand it, CEOs promote it, and vendors sell it. On plant floors, however, workers cite tools that don’t reflect operational reality and mistrust recommendations. “Efficiency” projects feel disconnected from daily work, safety, and lasting impact. This has widened the&amp;nbsp;credibility gap and serves as a wake-up call for executives investing in AI (Figure 1). &lt;/span&gt;&lt;/p&gt; 
&lt;h5 style="line-height: 15.693334px; text-align: center;"&gt;&lt;span style="color: #112432;"&gt;&lt;strong&gt;Figure 1:&lt;/strong&gt; &lt;span style="font-weight: bold;"&gt;Surveys show a significant gap in how AI is being perceived,&amp;nbsp;w&lt;/span&gt;&lt;span style="font-weight: bold;"&gt;ith anxious workers on the shop floor and excited execs on the top floor.&lt;/span&gt;&lt;/span&gt;&lt;/h5&gt;  
&lt;img src="https://track.hubspot.com/__ptq.gif?a=136847&amp;amp;k=14&amp;amp;r=https%3A%2F%2Fblog.lnsresearch.com%2Fbuilding-ai-trust-plant-credibility-chief-sustainability-officers-cta&amp;amp;bu=https%253A%252F%252Fblog.lnsresearch.com&amp;amp;bvt=rss" alt="" width="1" height="1" style="min-height:1px!important;width:1px!important;border-width:0!important;margin-top:0!important;margin-bottom:0!important;margin-right:0!important;margin-left:0!important;padding-top:0!important;padding-bottom:0!important;padding-right:0!important;padding-left:0!important; "&gt;</content:encoded>
      <category>Sustainability</category>
      <category>Operational Excellence</category>
      <category>Industry 4.0 / Smart Manufacturing</category>
      <category>Industrial Analytics</category>
      <category>Industrial Transformation</category>
      <category>Industrial AI</category>
      <category>Digital Transformation</category>
      <category>Decision Intelligence</category>
      <category>Industrial Safety</category>
      <category>AI Adoption</category>
      <category>AI Governance</category>
      <category>Human-Centered AI</category>
      <category>Chief Sustainability Officer (CSO)</category>
      <category>AI Trust</category>
      <pubDate>Tue, 23 Jun 2026 15:00:00 GMT</pubDate>
      <author>Allison.Kuhn@lns-global.com (Allison Kuhn)</author>
      <guid>https://blog.lnsresearch.com/building-ai-trust-plant-credibility-chief-sustainability-officers-cta</guid>
      <dc:date>2026-06-23T15:00:00Z</dc:date>
    </item>
    <item>
      <title>The 2026 Industrial Productivity Index and World’s Most Productive Companies™</title>
      <link>https://blog.lnsresearch.com/2026-worlds-most-productive-companies</link>
      <description>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://blog.lnsresearch.com/2026-worlds-most-productive-companies" title="" class="hs-featured-image-link"&gt; &lt;img src="https://blog.lnsresearch.com/hubfs/wmpc-2026-blog/fig_1_63f9d3e658.png" alt="The 2026 Industrial Productivity Index and World’s Most Productive Companies™" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p&gt;2025 was a historic year.&lt;/p&gt;</description>
      <content:encoded>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://blog.lnsresearch.com/2026-worlds-most-productive-companies" title="" class="hs-featured-image-link"&gt; &lt;img src="https://blog.lnsresearch.com/hubfs/wmpc-2026-blog/fig_1_63f9d3e658.png" alt="The 2026 Industrial Productivity Index and World’s Most Productive Companies™" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p&gt;2025 was a historic year.&lt;/p&gt;  
&lt;img src="https://track.hubspot.com/__ptq.gif?a=136847&amp;amp;k=14&amp;amp;r=https%3A%2F%2Fblog.lnsresearch.com%2F2026-worlds-most-productive-companies&amp;amp;bu=https%253A%252F%252Fblog.lnsresearch.com&amp;amp;bvt=rss" alt="" width="1" height="1" style="min-height:1px!important;width:1px!important;border-width:0!important;margin-top:0!important;margin-bottom:0!important;margin-right:0!important;margin-left:0!important;padding-top:0!important;padding-bottom:0!important;padding-right:0!important;padding-left:0!important; "&gt;</content:encoded>
      <pubDate>Wed, 10 Jun 2026 15:55:51 GMT</pubDate>
      <author>matthew.littlefield@lnsresearch.com (Matthew Littlefield)</author>
      <guid>https://blog.lnsresearch.com/2026-worlds-most-productive-companies</guid>
      <dc:date>2026-06-10T15:55:51Z</dc:date>
    </item>
    <item>
      <title>The Industrial DataOps Tipping Point: Are We There Yet?</title>
      <link>https://blog.lnsresearch.com/the-industrial-dataops-tipping-point-are-we-there-yet</link>
      <description>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://blog.lnsresearch.com/the-industrial-dataops-tipping-point-are-we-there-yet" title="" class="hs-featured-image-link"&gt; &lt;img src="https://blog.lnsresearch.com/hubfs/Top%20Challenges%20With%20Industrial%20AI_Featured%20Image.png" alt="Top Challenges With Industrial AI" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p style="text-align: justify;"&gt;&lt;span&gt;With the emergence of Industrial DataOps, the OT side of the fence is finally getting the attention and solution it deserves, which IT has been getting for quite some time now. Over the past few years, multiple waves of technology and vendor activity have targeted this challenge. But I believe we have now reached a tipping point. Significant capital flows, new product launches, open-source momentum, and accelerating adoption tell us that Industrial DataOps is no longer an emerging concept.&lt;/span&gt;&lt;/p&gt;</description>
      <content:encoded>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://blog.lnsresearch.com/the-industrial-dataops-tipping-point-are-we-there-yet" title="" class="hs-featured-image-link"&gt; &lt;img src="https://blog.lnsresearch.com/hubfs/Top%20Challenges%20With%20Industrial%20AI_Featured%20Image.png" alt="Top Challenges With Industrial AI" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p style="text-align: justify;"&gt;&lt;span&gt;With the emergence of Industrial DataOps, the OT side of the fence is finally getting the attention and solution it deserves, which IT has been getting for quite some time now. Over the past few years, multiple waves of technology and vendor activity have targeted this challenge. But I believe we have now reached a tipping point. Significant capital flows, new product launches, open-source momentum, and accelerating adoption tell us that Industrial DataOps is no longer an emerging concept.&lt;/span&gt;&lt;/p&gt;  
&lt;img src="https://track.hubspot.com/__ptq.gif?a=136847&amp;amp;k=14&amp;amp;r=https%3A%2F%2Fblog.lnsresearch.com%2Fthe-industrial-dataops-tipping-point-are-we-there-yet&amp;amp;bu=https%253A%252F%252Fblog.lnsresearch.com&amp;amp;bvt=rss" alt="" width="1" height="1" style="min-height:1px!important;width:1px!important;border-width:0!important;margin-top:0!important;margin-bottom:0!important;margin-right:0!important;margin-left:0!important;padding-top:0!important;padding-bottom:0!important;padding-right:0!important;padding-left:0!important; "&gt;</content:encoded>
      <category>Information Technology</category>
      <category>Industrial Internet of Things (IIoT)</category>
      <category>Industry 4.0 / Smart Manufacturing</category>
      <category>Industrial Analytics</category>
      <category>Connectivity</category>
      <category>Data Analytics</category>
      <category>Industrial AI</category>
      <category>Digital Transformation</category>
      <category>Industrial DataOps</category>
      <category>Data Governance</category>
      <category>Unified Namespace</category>
      <category>AI in Manufacturing</category>
      <category>Smart Manufacturing</category>
      <category>DataOps</category>
      <category>Data Quality</category>
      <category>Data Platforms</category>
      <category>Data Architecture</category>
      <category>AI Adoption</category>
      <category>Data Strategy</category>
      <pubDate>Tue, 12 May 2026 14:15:00 GMT</pubDate>
      <author>vivek.murugesan@lns-global.com (Vivek Murugesan)</author>
      <guid>https://blog.lnsresearch.com/the-industrial-dataops-tipping-point-are-we-there-yet</guid>
      <dc:date>2026-05-12T14:15:00Z</dc:date>
    </item>
    <item>
      <title>Industrial AI for Safety, Quality, &amp; a Sustained Competitive Advantage</title>
      <link>https://blog.lnsresearch.com/industrial-ai-for-safety-quality-a-sustained-competitive-advantage</link>
      <description>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://blog.lnsresearch.com/industrial-ai-for-safety-quality-a-sustained-competitive-advantage" title="" class="hs-featured-image-link"&gt; &lt;img src="https://blog.lnsresearch.com/hubfs/What%20is%20the%20current%20status%20of%20agentic%20ai_Featured%20Image.png" alt="What is the current status" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p&gt;&lt;span&gt;Executives&lt;/span&gt;&lt;span&gt; are in a race as Industrial AI is rewriting the rules of manufacturing leadership. As margins tighten and operations grow more complex, the winners won’t just be those who adopt digital tools first, but those who industrialize AI fastest and most effectively. Industrial Transformation Leaders, the top 29% of manufacturers leveraging digital technologies to drive step-change performance, have dramatically improved safety, quality, and profitability (&lt;strong&gt;Figure 1&lt;/strong&gt;).&lt;/span&gt;&lt;/p&gt; 
&lt;h5 style="line-height: 15.693334px; text-align: center;"&gt;&lt;span style="color: #112432;"&gt;&lt;strong&gt;Figure 1:&lt;/strong&gt; Leaders have dramatically improved performance&lt;br&gt;compared to Followers in every metric that matters&lt;/span&gt;&lt;/h5&gt;</description>
      <content:encoded>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://blog.lnsresearch.com/industrial-ai-for-safety-quality-a-sustained-competitive-advantage" title="" class="hs-featured-image-link"&gt; &lt;img src="https://blog.lnsresearch.com/hubfs/What%20is%20the%20current%20status%20of%20agentic%20ai_Featured%20Image.png" alt="What is the current status" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p&gt;&lt;span&gt;Executives&lt;/span&gt;&lt;span&gt; are in a race as Industrial AI is rewriting the rules of manufacturing leadership. As margins tighten and operations grow more complex, the winners won’t just be those who adopt digital tools first, but those who industrialize AI fastest and most effectively. Industrial Transformation Leaders, the top 29% of manufacturers leveraging digital technologies to drive step-change performance, have dramatically improved safety, quality, and profitability (&lt;strong&gt;Figure 1&lt;/strong&gt;).&lt;/span&gt;&lt;/p&gt; 
&lt;h5 style="line-height: 15.693334px; text-align: center;"&gt;&lt;span style="color: #112432;"&gt;&lt;strong&gt;Figure 1:&lt;/strong&gt; Leaders have dramatically improved performance&lt;br&gt;compared to Followers in every metric that matters&lt;/span&gt;&lt;/h5&gt;  
&lt;img src="https://track.hubspot.com/__ptq.gif?a=136847&amp;amp;k=14&amp;amp;r=https%3A%2F%2Fblog.lnsresearch.com%2Findustrial-ai-for-safety-quality-a-sustained-competitive-advantage&amp;amp;bu=https%253A%252F%252Fblog.lnsresearch.com&amp;amp;bvt=rss" alt="" width="1" height="1" style="min-height:1px!important;width:1px!important;border-width:0!important;margin-top:0!important;margin-bottom:0!important;margin-right:0!important;margin-left:0!important;padding-top:0!important;padding-bottom:0!important;padding-right:0!important;padding-left:0!important; "&gt;</content:encoded>
      <category>Operational Excellence</category>
      <category>Industry 4.0 / Smart Manufacturing</category>
      <category>Industrial AI</category>
      <category>Digital Transformation</category>
      <category>Smart Manufacturing</category>
      <category>Agentic AI</category>
      <category>Advanced Analytics</category>
      <category>Workforce Enablement</category>
      <pubDate>Tue, 28 Apr 2026 14:02:00 GMT</pubDate>
      <author>Allison.Kuhn@lns-global.com (Allison Kuhn)</author>
      <guid>https://blog.lnsresearch.com/industrial-ai-for-safety-quality-a-sustained-competitive-advantage</guid>
      <dc:date>2026-04-28T14:02:00Z</dc:date>
    </item>
    <item>
      <title>Four Categories of Manufacturing Execution Solutions</title>
      <link>https://blog.lnsresearch.com/four-categories-of-manufacturing-execution-solutions</link>
      <description>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://blog.lnsresearch.com/four-categories-of-manufacturing-execution-solutions" title="" class="hs-featured-image-link"&gt; &lt;img src="https://blog.lnsresearch.com/hubfs/undefined-Apr-01-2026-08-00-31-3138-PM.png" alt="Four Categories of Manufacturing Execution Solutions" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p&gt;MES is not disappearing. The market is maturing, and it is splitting into four distinct categories that reflect how different manufacturers address the same fundamental execution challenges. Before evaluating specific vendors, manufacturers need to understand which category fits their situation.&lt;/p&gt;</description>
      <content:encoded>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://blog.lnsresearch.com/four-categories-of-manufacturing-execution-solutions" title="" class="hs-featured-image-link"&gt; &lt;img src="https://blog.lnsresearch.com/hubfs/undefined-Apr-01-2026-08-00-31-3138-PM.png" alt="Four Categories of Manufacturing Execution Solutions" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p&gt;MES is not disappearing. The market is maturing, and it is splitting into four distinct categories that reflect how different manufacturers address the same fundamental execution challenges. Before evaluating specific vendors, manufacturers need to understand which category fits their situation.&lt;/p&gt;  
&lt;img src="https://track.hubspot.com/__ptq.gif?a=136847&amp;amp;k=14&amp;amp;r=https%3A%2F%2Fblog.lnsresearch.com%2Ffour-categories-of-manufacturing-execution-solutions&amp;amp;bu=https%253A%252F%252Fblog.lnsresearch.com&amp;amp;bvt=rss" alt="" width="1" height="1" style="min-height:1px!important;width:1px!important;border-width:0!important;margin-top:0!important;margin-bottom:0!important;margin-right:0!important;margin-left:0!important;padding-top:0!important;padding-bottom:0!important;padding-right:0!important;padding-left:0!important; "&gt;</content:encoded>
      <category>Industrial Automation</category>
      <category>Manufacturing Execution System (MES)</category>
      <category>Manufacturing Technology</category>
      <category>Production Management</category>
      <category>Discrete Manufacturing</category>
      <category>Process Optimization</category>
      <category>ERP Integration</category>
      <category>Batch Manufacturing</category>
      <category>Industry Trends</category>
      <pubDate>Tue, 14 Apr 2026 13:45:00 GMT</pubDate>
      <author>niels.andersen@lns-global.com (Niels Andersen)</author>
      <guid>https://blog.lnsresearch.com/four-categories-of-manufacturing-execution-solutions</guid>
      <dc:date>2026-04-14T13:45:00Z</dc:date>
    </item>
    <item>
      <title>Where Palantir Won &amp; C3 Didn’t: A Tale of Two Industrial AI Platforms</title>
      <link>https://blog.lnsresearch.com/where-palantir-won-c3-didnt-a-tale-of-two-industrial-ai-platforms</link>
      <description>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://blog.lnsresearch.com/where-palantir-won-c3-didnt-a-tale-of-two-industrial-ai-platforms" title="" class="hs-featured-image-link"&gt; &lt;img src="https://blog.lnsresearch.com/hubfs/Industrial%20AI%20Platform_Featured%20Image.png" alt="Industrial AI Platform" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p style="text-align: justify;"&gt;&lt;span style="line-height: 115%;"&gt;Software categories are nothing without some healthy competition, and it’s only more interesting when it’s a true 1-v-1 comparison of two companies with similar characteristics. Whether it was Seeq vs. TrendMiner in self-serviced Advanced Industrial Analytics, or more recently Highbyte vs. Litmus in Industrial DataOps, a closer look at each of these matchups provides some good insights on what it takes to win in a category.&lt;/span&gt;&lt;/p&gt;</description>
      <content:encoded>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://blog.lnsresearch.com/where-palantir-won-c3-didnt-a-tale-of-two-industrial-ai-platforms" title="" class="hs-featured-image-link"&gt; &lt;img src="https://blog.lnsresearch.com/hubfs/Industrial%20AI%20Platform_Featured%20Image.png" alt="Industrial AI Platform" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p style="text-align: justify;"&gt;&lt;span style="line-height: 115%;"&gt;Software categories are nothing without some healthy competition, and it’s only more interesting when it’s a true 1-v-1 comparison of two companies with similar characteristics. Whether it was Seeq vs. TrendMiner in self-serviced Advanced Industrial Analytics, or more recently Highbyte vs. Litmus in Industrial DataOps, a closer look at each of these matchups provides some good insights on what it takes to win in a category.&lt;/span&gt;&lt;/p&gt;  
&lt;img src="https://track.hubspot.com/__ptq.gif?a=136847&amp;amp;k=14&amp;amp;r=https%3A%2F%2Fblog.lnsresearch.com%2Fwhere-palantir-won-c3-didnt-a-tale-of-two-industrial-ai-platforms&amp;amp;bu=https%253A%252F%252Fblog.lnsresearch.com&amp;amp;bvt=rss" alt="" width="1" height="1" style="min-height:1px!important;width:1px!important;border-width:0!important;margin-top:0!important;margin-bottom:0!important;margin-right:0!important;margin-left:0!important;padding-top:0!important;padding-bottom:0!important;padding-right:0!important;padding-left:0!important; "&gt;</content:encoded>
      <category>Operational Excellence</category>
      <category>Industry 4.0 / Smart Manufacturing</category>
      <category>Advanced Industrial Analytics</category>
      <category>Industrial AI</category>
      <category>Digital Transformation</category>
      <category>Industrial DataOps</category>
      <category>Manufacturing Technology</category>
      <category>AI in Manufacturing</category>
      <category>Enterprise AI Platforms</category>
      <category>Supply Chain &amp; Operations</category>
      <pubDate>Tue, 07 Apr 2026 14:47:00 GMT</pubDate>
      <author>vivek.murugesan@lns-global.com (Vivek Murugesan)</author>
      <guid>https://blog.lnsresearch.com/where-palantir-won-c3-didnt-a-tale-of-two-industrial-ai-platforms</guid>
      <dc:date>2026-04-07T14:47:00Z</dc:date>
    </item>
  </channel>
</rss>
