<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>DigiVisory</title>
    <link>http://digivisory.com</link>
    <description>Practical AI intelligence for operators who build, buy, and deploy.</description>
    <language>en-us</language>
    <atom:link href="http://digivisory.com/feed.xml" rel="self" type="application/rss+xml" />
    <lastBuildDate>Tue, 01 Sep 2026 22:07:11 GMT</lastBuildDate>
    <item>
      <title>The AI Operating Model: Ownership, Risk, Delivery, and Control</title>
      <link>http://digivisory.com/blog/ai-operating-model</link>
      <guid isPermaLink="true">http://digivisory.com/blog/ai-operating-model</guid>
      <description>An AI operating model assigns ownership, defines risk tiers, sets delivery standards, and enforces control to manage AI systems consistently and safely across the enterprise.</description>
      <category>AI Operating Frameworks</category>
      <dc:creator>Jeff Ellis</dc:creator>
      <pubDate>Mon, 10 Aug 2026 13:00:00 GMT</pubDate>
    </item>
    <item>
      <title>How to Measure AI ROI: A Workflow-Level Framework</title>
      <link>http://digivisory.com/blog/how-to-measure-ai-roi</link>
      <guid isPermaLink="true">http://digivisory.com/blog/how-to-measure-ai-roi</guid>
      <description>Measure AI ROI by focusing on workflows, not tools or seats. Establish a baseline, include verification costs, and separate value into four types to get real numbers.</description>
      <category>AI Value &amp; Adoption</category>
      <dc:creator>Jeff Ellis</dc:creator>
      <pubDate>Mon, 10 Aug 2026 13:00:00 GMT</pubDate>
    </item>
    <item>
      <title>How to Design an AI Workflow: A Human-to-System Blueprint</title>
      <link>http://digivisory.com/blog/how-to-design-an-ai-workflow</link>
      <guid isPermaLink="true">http://digivisory.com/blog/how-to-design-an-ai-workflow</guid>
      <description>An AI workflow is a repeatable sequence turning defined inputs into outputs with humans in the loop where errors are costly. This guide shows how to design and measure such workflows.</description>
      <category>AI Workflows</category>
      <dc:creator>Jeff Ellis</dc:creator>
      <pubDate>Mon, 10 Aug 2026 13:00:00 GMT</pubDate>
    </item>
    <item>
      <title>How Companies Actually Use AI: A Primary-Source Field Guide</title>
      <link>http://digivisory.com/blog/how-companies-actually-use-ai</link>
      <guid isPermaLink="true">http://digivisory.com/blog/how-companies-actually-use-ai</guid>
      <description>Companies disclose AI use through engineering blogs, job postings, patents, and filings. This guide ranks these sources by trustworthiness to reveal what AI systems actually run in production.</description>
      <category>AI in the Wild</category>
      <dc:creator>Jeff Ellis</dc:creator>
      <pubDate>Mon, 10 Aug 2026 13:00:00 GMT</pubDate>
    </item>
    <item>
      <title>The Enterprise AI Implementation Playbook: Build the System, Not the Pilot</title>
      <link>http://digivisory.com/blog/enterprise-ai-implementation-playbook</link>
      <guid isPermaLink="true">http://digivisory.com/blog/enterprise-ai-implementation-playbook</guid>
      <description>Enterprise AI succeeds by building operational memory and workflows before models. Focus on capturing decisions, exceptions, and corrections to avoid stalled pilots and failed rollouts.</description>
      <category>AI Playbooks</category>
      <dc:creator>Jeff Ellis</dc:creator>
      <pubDate>Mon, 10 Aug 2026 13:00:00 GMT</pubDate>
    </item>
    <item>
      <title>How to Evaluate AI Vendors: A Buyer's Scorecard for Enterprise AI</title>
      <link>http://digivisory.com/blog/how-to-evaluate-ai-vendors</link>
      <guid isPermaLink="true">http://digivisory.com/blog/how-to-evaluate-ai-vendors</guid>
      <description>Evaluate AI vendors using a 12-point scorecard covering problem fit, memory ownership, exit path, evaluation evidence, and support. Focus on operational control and exit options before buying.</description>
      <category>AI Playbooks</category>
      <dc:creator>Jeff Ellis</dc:creator>
      <pubDate>Mon, 10 Aug 2026 13:00:00 GMT</pubDate>
    </item>
    <item>
      <title>Prompting vs RAG vs Fine-Tuning: The Operator's Decision Guide</title>
      <link>http://digivisory.com/blog/prompting-vs-rag-vs-fine-tuning</link>
      <guid isPermaLink="true">http://digivisory.com/blog/prompting-vs-rag-vs-fine-tuning</guid>
      <description>Choose prompting when facts are known and behavior steering is needed. Use RAG for dynamic, document-based answers. Fine-tune for stable, repeatable tasks requiring consistent output.</description>
      <category>AI Education</category>
      <dc:creator>Jeff Ellis</dc:creator>
      <pubDate>Mon, 10 Aug 2026 13:00:00 GMT</pubDate>
    </item>
    <item>
      <title>Rent the Model, Own the Memory: The Enterprise AI Memory Architecture</title>
      <link>http://digivisory.com/blog/rent-the-model-own-the-memory</link>
      <guid isPermaLink="true">http://digivisory.com/blog/rent-the-model-own-the-memory</guid>
      <description>Enterprise AI memory architecture captures your organization's decision knowledge separately from rented models. Owning memory ensures continuity and accuracy when swapping AI providers.</description>
      <category>AI Operating Frameworks</category>
      <dc:creator>Jeff Ellis</dc:creator>
      <pubDate>Mon, 10 Aug 2026 13:00:00 GMT</pubDate>
    </item>
  </channel>
</rss>