Field Notes

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.

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?"

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.