Marketing & Insights Executive Digital Tourism Think Tank
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stars The framing
Stop Prompting, Start Delegating: The Rise of Autonomous AI
foundation
Foundation
Project knowledge
Claude Projects, Custom GPTs, Gems. A structured knowledge source holding your brand voice and destination context. An agent without this produces generic output.
hub
Connector
MCP & integrations
Model Context Protocol links AI to your actual stack. Email, calendar, CMS, project management, databases. The bridge between knowing and doing.
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Agent
AI as infrastructure
The shift from talking to AI to delegating to AI. Work that runs on a schedule, agents that act across tools, outputs produced without real-time prompting.
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apps Four platforms, agentic capability
Matching the Right AI Agent to the Right Task
workspaces
Gemini
Native to Google Workspace. The strongest choice for teams whose primary stack is Gmail, Docs, Sheets and Drive.
grid_viewGemini in Workspace
scienceGoogle AI Studio
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Copilot
Built into Microsoft 365. The right choice if the organisation runs on Outlook, Teams, SharePoint and Word.
buildMicrosoft Copilot Studio
scheduleCopilot Tasks
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Claude
Strongest MCP connector library and the most flexible setup for DMOs already running Claude Projects.
desktop_windowsCowork
exploreClaude in Chrome
chat
ChatGPT
Mature integration library and an agentic mode that runs across the web. Strong for teams already in the OpenAI ecosystem.
smart_toyAgent Mode
terminalCodex
3 / 8
play_circle Two live demonstrations
Streamlining the Workflow: From Raw Project Data to Deployed Solutions
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Pattern 01 · Cowork on the desktop
Turning a folder of files into a structured brief
A working folder filled with partner contracts, research PDFs and notes. Useful detail, no shape. Cowork reads the folder on the desktop, sorts the files by type, drafts a one-page summary of each and writes the output back as a clean working set the team can act on.
Cowork reads every file in the working folder on the desktop
Sorts content by type: partner contract, research, internal note
Drafts a short summary card for each file using the brand voice in project knowledge
Writes the summaries back to a new folder ready for the team
The unlockA full afternoon of file triage and summary writing compressed into a ten-minute brief and a five-minute review.
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Pattern 02 · Codex end to end
From a visitor dataset to a deployed interactive tool
A destination with rich visitor data, brand guidelines and no clear way to make the data usable across the team. Codex reads the dataset, applies the brand voice from project knowledge, builds a structured interactive view of the data and deploys it as an accessible tool with a shareable URL.
Codex extracts the relevant insights from the visitor dataset
Applies the brand voice and editorial rules from project knowledge
Builds an interactive visualisation as a structured artefact
Deploys the result as an accessible tool the team can share through a URL
The unlockA workflow that previously needed three teams and four weeks compressed to one working session and two hours.
The architecture both patterns share
foundation
Project knowledge
Brand, voice, context
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hub
MCP connector
Tools, data, deployment
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precision_manufacturing
Agentic workflow
Reads, decides, writes
arrow_forward
workspaces
Output
Tool updated, artefact deployed
4 / 8
bolt Six workflows, pick what matches the work
Workflow productivity
Six scenarios the team could delegate next week. Each one has a copy-paste prompt tagged for the most suitable tool. Pick one that matches the role and try it on a device now.
Scenario 1 · Daily admin
Email automation 101
Triage incoming visitor enquiries, partner correspondence and press requests. The agent reads your inbox, sorts by intent, drafts replies in your destination's voice. You review and send.
Claude + Gmail MCPCopilotGemini
Read the last 20 unread messages in my inbox. Sort by intent: visitor enquiry, partner communication, press request, internal, or noise. For each visitor enquiry, draft a reply using the brand voice in my project knowledge, friendly, specific, locally-informed. For partner messages, summarise the ask and flag urgency. List press requests separately for me to handle personally.
Scenario 2 · Daily admin
Scheduling and meeting coordination
Calendar coordination, meeting prep, follow-ups. An agent handles the administrative layer that consumes senior time, finding slots, sending invites, drafting agendas, preparing pre-reads.
CoworkCopilot TasksChatGPT Tasks
I have a 30-minute strategy review with Sarah next week. Find three slots that work for both calendars between Tuesday and Thursday. Draft the invite using my standard meeting template. Prepare a one-page pre-read summarising our last three exchanges on this topic. Schedule the invite once I confirm a slot.
Scenario 3 · Strategic delegation
Project management automation
Project management cleaned up at speed. Claude reads the project, audits it against the team's working preferences, restructures and writes the result back. Senior project management compressed.
Claude + Asana MCPNotion / Linear MCP
Audit my Asana project [PROJECT NAME]. The team finds long task lists overwhelming, we want each programme element clearly identified in its own column with detail held in sub-tasks rather than separate tasks. Reduce task count by consolidating overlapping items. Keep all original detail in descriptions and sub-tasks. Apply our naming convention: [TIME] · [TITLE] · [OWNER]. Write the restructured project back.
Scenario 4 · Strategic delegation
Database management with Airtable
An agent that updates your databases from natural language input. The DTTT reports directory works this way, Claude reads new reports, extracts structured fields and writes them back to Airtable. No manual data entry.
Claude + Airtable MCP
I'm attaching a new report PDF. Read it and extract: title, publisher, date, primary topic, three to five key data points, relevant DMO use cases, and a one-paragraph summary in our editorial voice. Write a new row to the reports directory base on Airtable using these fields. Tag it with the publisher and topic. Flag if any field cannot be confidently extracted.
Scenario 5 · Intelligence
Weekly competitive intelligence brief
A recurring brief on what comparable destinations are publishing, posting and being cited for. The agent runs on a schedule, pulls from the sources you trust, and lands a one-page brief in your inbox every Monday.
ChatGPT TasksClaude + web search
Every Monday at 7am, search for what these five destinations [LIST] have published or been cited for in the previous week: blog posts, news mentions, campaign launches and AI search citations. Produce a one-page brief with the three most notable items, a paragraph each, and a strategic implication for our destination. Email it to me before 9am.
Scenario 6 · Brand-aligned output
Brand-aligned content production
Knowledge system as design system. Brand guidelines, voice rules and visual identity loaded as a project or gem, every piece of content produced sits naturally inside your brand without manual checking.
Claude ProjectGemini Gem
Produce a 600-word seasonal feature article for our spring campaign. Topic: [TOPIC]. Audience: [PRIMARY VISITOR PROFILE]. Channel: destination website blog. Use the voice rules, structural conventions and editorial standards loaded in this project. Include three specific local references that only someone with our destination knowledge would include. Avoid all generic travel-writing phrases listed in our voice document.
Every prompt depends on the knowledge system
Every workflow is only as good as the foundation it reads from.
Notice each prompt references "our voice", "our editorial standards", "our naming convention", "our brand guidelines". Before any of these workflows go into production, the project knowledge needs to be tight, written for AI to read, not just for humans to skim.
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smart_toy Agentic mode
Where agentic workflows do the work
Agentic mode runs a task across the web, the tools and the data, then returns with a structured result. Useful when the work involves research, analysis or moving between many sources in one session.
explore
The tool
Claude in Chrome
Claude operates the browser. Reads pages, fills forms, clicks through workflows and summarises what it finds. Strong for research-heavy tasks that span many tabs.
smart_toy
The tool
ChatGPT Agent Mode
ChatGPT runs a virtual environment for the session. Browses, writes code, runs analyses and builds files. Useful for tasks that need both research and structured output.
Pattern 01 · Research
Deep research across competitors
Browses, reads and synthesises across many sources in one session. Useful when the question is "what do we know about X" and the answer needs to come from several dozen places.
Claude in ChromeChatGPT Agent Mode
Research how five comparable destinations [LIST] are positioning themselves on sustainable tourism in 2026. For each, find their public commitments, the language they use, two examples of campaigns and any data they report. Cross-reference with what their visitors are saying on TripAdvisor, Reddit and Instagram. Produce a structured brief I can take to a strategy session: what is similar, what is differentiated, where the gaps are for us.
Pattern 02 · Analytics
Pulling insight from analytics, social and reviews
Logs into the analytics surfaces, pulls the relevant reports, cross-references them with social performance and review platforms, and produces a strategic read rather than a data dump.
Claude in ChromeChatGPT Agent Mode
Log into our Google Analytics property [DOMAIN]. Compare the last 90 days to the previous 90 across organic traffic, top-performing pages, top entry pages from AI sources and conversion paths. Pull the same period from our social analytics in [PLATFORMS]. Pull the most recent 200 reviews from [TRIPADVISOR/GOOGLE]. Cross-reference: where is engagement growing, where is it dropping, what content themes correlate with both. Produce three strategic recommendations and a content priority list for the next quarter.
Pattern 03 · Strategic content plan
From insight to a working content plan
Turns the analytics read above into a structured editorial plan. Themes, channels, formats, dates, owners. The output is a working artefact the team can act on, not a deck.
Claude in ChromeClaude + Airtable MCP
Take the strategic insights from the analytics pull above. Build a content plan for the next 12 weeks. For each week: name the theme, the channel mix, the format types, the expected output volume, and the team member who would lead. Apply our editorial voice and content rules from project knowledge. Write the plan to a new Airtable base I can share with the team. Flag any week where the brief is thin and a strategic conversation is needed before commissioning.
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hub The connector layer
MCP is the connector. Here is what is production-ready
Model Context Protocol is the open standard that lets AI read from and write to the team's actual tools. Anthropic launched it, OpenAI, Microsoft and Google now support it. The connector library is growing weekly. The four below are mature enough to use in production today.
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Asana
Read and write tasks, projects, sub-tasks, owners and dates. One of the most mature integrations. Restructure projects, audit task lists, automate status reporting.
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Airtable
Read and write to any base. The DTTT reports directory works this way, Claude extracts structured fields from PDFs and writes new rows back to the base automatically.
mail
Gmail & Google Workspace
Read, draft and send email. Read and write calendar events. Strong integration with Docs, Sheets and Drive. The connector layer for Google-first teams.
chat
Slack
Read channels, post messages, summarise conversations. Agents can monitor specific channels and surface what matters without flooding inboxes.
Setting up the first connector
Start with one. Use it for two weeks. Add a second.
Most agentic disappointment comes from trying to connect everything at once. The pattern that works: pick the workflow that costs the team the most time today, set up that one connector, use it for two weeks until it is genuinely embedded, then add the next one. Project knowledge sharpens with each connection.
Start with what costs the team most
Inbox management, project admin or status reporting, whichever consumes the most senior time.
Test in a low-stakes context
A side project, a draft folder, a staging Airtable. Not the production newsletter list on day one.
Build a review habit
Agentic work needs human review. Schedule the review time when the workflow is set to run.
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flag Strategic reality
Agentic workflows are powerful. They are also not a shortcut
Agentic workflows extend what a small DMO team can do. They do not replace the work of building the foundations the workflows read from. Five points keep the approach strategic.
01
foundation
Project knowledge first
A workflow without a tight knowledge system produces generic output faster. Foundations are not optional preliminaries.
02
touch_app
Delegate, do not automate blindly
Hand over the work that follows clear, repeatable rules. Keep judgement work with humans.
03
visibility
Build a review habit
Agentic workflows compress time, they do not eliminate review. Plan the review when the workflow is set up.
04
linear_scale
One workflow at a time
Set up one workflow, use it for two weeks until embedded, then add the next.
05
trending_up
Time saved is team capacity
Free time should go to the work that needs human judgement, not budget cuts.
Where this sits in the AI programme
Workflow is the operational layer
It sits on top of the knowledge system, the project, custom GPT or gem that holds the destination's voice and facts, which sits on top of an AI-first team workflow, which sits on top of the strategic decision to be AI-first.
