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    <title>Field Notes | Agents of Change</title>
    <link>https://www.janetteroush.com/notes</link>
    <description>Research notes, insights, and evolving definitions from the AI Lab for tourism innovation. By Janette Roush, Chief AI Officer.</description>
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    <lastBuildDate>Sun, 07 Jun 2026 12:30:23 GMT</lastBuildDate>
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    <dc:creator>Janette Roush</dc:creator>
    <dc:rights>Copyright 2026 Janette Roush</dc:rights>
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      <title>The Leapfrog Thesis: Why Some Teams Should Skip Chatbots Entirely</title>
      <link>https://www.janetteroush.com/notes#leapfrog-thesis</link>
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      <pubDate>Sat, 07 Mar 2026 00:00:00 GMT</pubDate>
      <dc:creator>Janette Roush</dc:creator>
      <category>AI Strategy, Agentic AI, Digital Leapfrogging, Personal OS</category>
      <description>In the early 2000s, entire regions of sub-Saharan Africa skipped landline telephone infrastructure and went directly to mobile phones. Banking in Kenya skipped branch networks for M-Pesa. The pattern is always the same: when a transitional technology arrives alongside a more advanced alternative, th...</description>
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        <![CDATA[In the early 2000s, entire regions of sub-Saharan Africa skipped landline telephone infrastructure and went directly to mobile phones. Banking in Kenya skipped branch networks for M-Pesa. The pattern is always the same: when a transitional technology arrives alongside a more advanced alternative, the organizations that skip the middle step gain a structural advantage. I believe we are watching this pattern play out right now in AI adoption. The transitional technology is the chatbot workflow. The more advanced alternative is agentic AI—a setup where AI operates directly in your files and systems, completes actual tasks, and submits its work for your review. The leapfrog path: set up a Personal OS, pick one manual workflow, write a markdown guide that teaches the AI how it works, then let the AI do the work while you review the output.]]>
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      <title>Building Your DMO&apos;s AI Strategy: The Two-Priority Framework</title>
      <link>https://www.janetteroush.com/notes#dmo-ai-strategy-framework</link>
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      <pubDate>Fri, 06 Mar 2026 00:00:00 GMT</pubDate>
      <dc:creator>Janette Roush</dc:creator>
      <category>AI Strategy, DMO, Framework, Source of Truth, GEO</category>
      <description>Every DMO needs an AI strategy. The question is what goes in it. After working with destination organizations across the country, a clear framework has emerged: your AI strategy has two priorities, and both are required. Priority One: Source of Truth — your destination&apos;s information needs to be accu...</description>
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        <![CDATA[Every DMO needs an AI strategy. The question is what goes in it. After working with destination organizations across the country, a clear framework has emerged: your AI strategy has two priorities, and both are required. Priority One: Source of Truth — your destination's information needs to be accurate wherever someone encounters it. Priority Two: AI-Capable — every function in your organization uses AI fluently. These two priorities reinforce each other. Start with the AI Audit: ask ChatGPT, Perplexity, and Google AI Overviews about your destination, check what's wrong, and run your key pages through Google's Rich Results Test. Build the Source of Truth with schema markup, GEO-optimized content, structured data feeds, and authoritative citations. Become AI-Capable by embedding AI fluency into marketing, research, partner services, and operations — then stand up governance with an AI use policy, departmental AI champions, and a steering committee.]]>
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      <title>AI Agents Taxonomy: Four Types That Matter for Tourism</title>
      <link>https://www.janetteroush.com/notes#ai-agents-taxonomy</link>
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      <pubDate>Wed, 15 Jan 2025 00:00:00 GMT</pubDate>
      <dc:creator>Janette Roush</dc:creator>
      <category>AI Agents, Framework, Tourism Strategy</category>
      <description>After analyzing hundreds of AI tools and their applications in destination marketing, a clear taxonomy has emerged. There are four distinct types of AI agents, each serving different operational needs:

Operator Agents automate browser-based tasks—the digital equivalent of &quot;using the mouse for you.&quot;...</description>
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        <![CDATA[After analyzing hundreds of AI tools and their applications in destination marketing, a clear taxonomy has emerged. There are four distinct types of AI agents, each serving different operational needs:

Operator Agents automate browser-based tasks—the digital equivalent of "using the mouse for you." For DMOs, this means automated lead generation, competitive web scraping, and data extraction. Tools like Browse.ai and Manus.im fall into this category.

Researcher Agents perform deep analysis and synthesis. Unlike simple search tools, these agents can analyze dozens of competitors simultaneously, synthesize market research, and generate comprehensive reports. This is where strategic intelligence happens.

Builder Agents create digital products from natural language prompts. Lovable.ai generates functional websites; Claude Artifacts builds interactive applications. For tourism marketers, this means rapid prototyping without engineering resources.

Automator Agents orchestrate workflows across multiple platforms. N8N, Agent.ai, and Google Gemini Gems connect disparate systems and automate multi-step processes. This is the infrastructure layer for AI-powered operations.

Understanding this taxonomy helps DMOs make strategic technology decisions. Each type solves different problems. Most organizations will eventually need all four.]]>
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      <title>Model Context Protocol: Solving the Trust Problem</title>
      <link>https://www.janetteroush.com/notes#mcp-breakthrough</link>
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      <pubDate>Fri, 10 Jan 2025 00:00:00 GMT</pubDate>
      <dc:creator>Janette Roush</dc:creator>
      <category>MCP, Technical, Data Accuracy</category>
      <description>AI hallucination isn&apos;t a bug—it&apos;s a feature of how large language models work. They generate plausible text based on patterns, not facts. This is fine for creative writing, catastrophic for travel planning.

The Model Context Protocol (MCP) represents a paradigm shift. Instead of trying to train AI ...</description>
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        <![CDATA[AI hallucination isn't a bug—it's a feature of how large language models work. They generate plausible text based on patterns, not facts. This is fine for creative writing, catastrophic for travel planning.

The Model Context Protocol (MCP) represents a paradigm shift. Instead of trying to train AI to "know" facts, MCP creates a standardized way for AI to query authoritative data sources in real-time. Think of it as an API layer specifically designed for AI agents.

For tourism, the implications are profound:
- Accessible travel route verification (no more hallucinated ramp locations)
- Real-time venue capacity checks (critical for meeting planners)
- Authoritative attraction operating hours (not "best guess" information)

The technical architecture is elegant: DMOs maintain their "source of truth" databases, MCP provides the query protocol, and AI agents can reliably access verified information. This shifts DMO strategy from "creating content for humans to read" to "maintaining data for AI to query."

This is not future speculation. Anthropic's Claude Desktop already implements MCP. The question is no longer "will this happen?" but "which DMOs will adopt it first?"]]>
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      <title>Why CRIT Framework Matters: Context Over Commands</title>
      <link>https://www.janetteroush.com/notes#crit-framework-origin</link>
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      <pubDate>Sun, 05 Jan 2025 00:00:00 GMT</pubDate>
      <dc:creator>Janette Roush</dc:creator>
      <category>CRIT Framework, Prompting, Methodology</category>
      <description>Most AI prompt guidance focuses on the task: &quot;Write me an email.&quot; &quot;Create a social media post.&quot; This approach consistently produces mediocre results for strategic work.

The CRIT Framework (Context, Role, Interview, Task) emerged from observing what separates exceptional AI outputs from generic ones...</description>
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        <![CDATA[Most AI prompt guidance focuses on the task: "Write me an email." "Create a social media post." This approach consistently produces mediocre results for strategic work.

The CRIT Framework (Context, Role, Interview, Task) emerged from observing what separates exceptional AI outputs from generic ones: rich context.

Context: Tourism professionals operate in a specialized domain. AI doesn't inherently understand DMO budget cycles, state tourism office hierarchies, or CVB stakeholder dynamics. Providing this context—often through voice input for natural explanation—transforms output quality.

Role: Assigning AI a specific role ("You are a convention sales director with 15 years of experience") activates relevant training data patterns and adjusts tone appropriately.

Interview: Before jumping to the task, let AI ask clarifying questions. This surfaces assumptions and ensures alignment. The best strategic outputs come after 2-3 rounds of AI-led questioning.

Task: Only after establishing context, role, and conducting an interview should you specify the task. By this point, AI has sufficient context to produce strategic-level work.

This framework was developed specifically for tourism professionals because our industry's context is too nuanced for generic prompting advice. The difference in output quality is not incremental—it's transformational.]]>
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