support_agent Zone · The Advisory Clinic

AI Readiness, Workflow & Knowledge Systems

calendar_today Tuesday 2 June

schedule 40 minutes

groups Two facilitators

James Arnold

Digital Trends Analyst
Digital Tourism Think Tank

Fábio Caldeira

Digital Trends Analyst
Digital Tourism Think Tank

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insights What you told us

Patterns across your submissions

A snapshot of the priorities surfaced in the pre-event Typeform, drawn from eight respondents. The Clinic will focus on the highest ranked themes.

PRIORITY 01

trending_up

63%

Upskilling staff

The capability and confidence to use AI well, ranked highest across submissions.

PRIORITY 02

school

50%

AI workflow integration

Moving AI from occasional use into the way work actually happens.

PRIORITY 03

hub

38%

Managing data quality

Keeping the information AI draws on accurate, current and structured.

PRIORITY 04

policy

25%

Building internal knowledge bases

Organising what the team knows so AI can act on it.

PRIORITY 05

speed

13%

Balancing the speed of AI adoption

Moving forward at a pace that brings the wider organisation along.

PRIORITY 06

handshake

13%

Clear expectations for partners and agencies

Setting the standard for how partners use AI on work that carries your brand.

Personal AI maturity (3.1 / 5)

62%

Organisational AI maturity (2.1 / 5)

43%

Maturity gap between you and your organisation

20%

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checklist AI Readiness, Workflow & Knowledge Systems

Easy steps to help you implement this

STEP 01

Audit where your team actually is before adding anything new. Map which tools are being used, by whom and whether they connect to anything shared.

STEP 02

Build your knowledge infrastructure first. A project knowledge trained on your destination's voice and context is what separates useful AI output from generic noise.

STEP 03

Embed AI into existing workflows rather than running it alongside them. Tools that sit outside how work actually happens get used occasionally.

STEP 04

Set a quality standard and document it. Decide what good looks like for your organisation and measure every output against it before it leaves the team.

Tools worth knowing

Claude Projects

Build a persistent knowledge base trained on your destination's documents, brand voice and processes. Every conversation draws on that context automatically.

ChatGPT Custom GPTs

Create a configured assistant for a specific workflow that colleagues can use without any prompt engineering knowledge.

Microsoft Copilot

If you are in the Microsoft 365 environment, use it to surface and summarise institutional knowledge already sitting across your file systems.

NotebookLM

Upload reports, strategies and data sources and query them conversationally. Useful for synthesising knowledge across multiple documents.

Zapier and Make

Connect AI tools to the systems your team already uses. The practical route to embedding AI into existing workflows rather than running it alongside them.

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01

Challenge One

Moving from working group to organisation-wide adoption

A small group moves on AI while the wider organisation stays still, widening the maturity gap the longer adoption stays contained.

Diagnose

Why does this matter for your destination?

Diagnose

What have you tried so far?

Constraint

What is the biggest constraint holding you back?

Next move

What is the first step you can take this week?

→Easy first step. Pick one workflow that has already been tested and document it as a single page someone outside the group can follow without any briefing.

→Easy first step. Bring colleagues in early, before the strategy is finished. Adoption builds through participation.

×What to avoid. Keeping AI activity inside a small group until everything is ready. By the time it is ready, the gap between early adopters and everyone else is harder to close.

×What to avoid. Framing adoption as training. People engage when AI solves a problem they already have.

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02

Challenge Two

Building an AI programme with structure and longevity

Engagement runs high in the first session and tails off after that, so the programme needs a clear sequence that outlasts the person who built it.

Diagnose

Why does this matter for your destination?

Diagnose

What have you tried so far?

Constraint

What is the biggest constraint holding you back?

Next move

What is the first step you can take this week?

→Easy first step. Define what the first phase needs to deliver before designing any phase after it. Sequence is what separates a programme from a set of isolated sessions.

→Easy first step. Run something small with a live group before building further. One session will teach you more than any amount of planning.

×What to avoid. Designing for every scenario before testing any of them.

×What to avoid. Building the whole programme around your most advanced participants. A programme that only works for organisations already moving quickly will leave most of the room behind.

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03

Challenge Three

Enabling partners and industry to access destination knowledge

Partners return to the DMO for answers they could find themselves, while well-structured destination knowledge lets industry self-serve.

Diagnose

Why does this matter for your destination?

Diagnose

What have you tried so far?

Constraint

What is the biggest constraint holding you back?

Next move

What is the first step you can take this week?

→Easy first step. Organise your knowledge before making it accessible. The quality of what partners can find depends on how well it has been structured at the source.

→Easy first step. Test with one partner before scaling. Use in practice surfaces what the tool needs to do, which is rarely what was assumed at the design stage.

×What to avoid. Building a partner-facing tool on knowledge that has not been audited for accuracy. Unreliable outputs erode trust in the DMO as much as in the tool itself.

×What to avoid. Treating launch as completion. A knowledge resource that is not maintained drifts out of date and quietly stops being used.

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04

Challenge Four

Building national data standards for AI readiness

AI surfaces destinations whose data is clean and machine-readable, while inconsistent formatting across the ecosystem creates visibility gaps that content alone cannot fix.

Diagnose

Why does this matter for your destination?

Diagnose

What have you tried so far?

Constraint

What is the biggest constraint holding you back?

Next move

What is the first step you can take this week?

→Easy first step. Look for standards that already exist before designing new ones. Other sectors and some leading DMOs have done this work and there is no value in reinventing it.

→Easy first step. Frame the leadership case around visibility loss. Executives respond to evidence of competitive disadvantage more readily than to infrastructure arguments.

×What to avoid. Setting national standards without bringing smaller operators in early. Standards designed without the people who implement them tend to fail at the implementation stage.

×What to avoid. Treating data structure as a back-end problem separate from content strategy. The two are connected and the case for investment is stronger when that connection is visible.

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waving_hand Carry the conversation forward

Take this back to your team

Download your notes and the recommendations from the session. If you want to keep working through these questions with us, start a thread or explore the Advisory Membership.

open_in_new Explore Advisory Membership

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