support_agent Organiser · The Advisory Clinic

AI Readiness, Workflow & Knowledge Systems

calendar_today Tuesday 2 June · Morning

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 they told us

Patterns across the 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

school

63%

Upskilling staff

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

PRIORITY 02

schema

50%

AI workflow integration

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

PRIORITY 03

database

38%

Managing data quality

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

PRIORITY 04

menu_book

25%

Internal knowledge bases

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

PRIORITY 05

speed

13%

Speed of adoption

Balancing pace of AI adoption with the team's capacity to absorb it.

PRIORITY 06

handshake

13%

Partner expectations

Setting clear expectations for partners and agencies on AI use.

Personal AI maturity (3.1 / 5)

62%

Organisational AI maturity (2.1 / 5)

43%

Maturity gap between individual and organisation

20%

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checklist AI Readiness, Workflow & Knowledge Systems

Easy steps to help them implement this

STEP 01

Audit where the 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 the knowledge infrastructure first. A project knowledge trained on the 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 the organisation and measure every output against it before it leaves the team.

Tools worth knowing

Claude Projects

Build a shared workspace with project knowledge, custom instructions and reusable artefacts that hold the destination's voice and context.

ChatGPT Custom GPTs

Package a workflow as a reusable assistant with its own instructions and reference files so anyone on the team can run it consistently.

Microsoft Copilot

For teams already in the Microsoft ecosystem, AI sits directly inside Word, Excel, Outlook and Teams where the work is already happening.

NotebookLM

Upload internal documents and turn them into a queryable knowledge base. Useful for onboarding, research synthesis and briefing prep.

Zapier and Make

Connect AI tools into the workflows the team already runs without needing a developer. Triggers, actions and AI steps in one flow.

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01

Challenge One

Moving from working group to organisation-wide adoption

A working group with no pathway into the wider organisation creates a two-speed culture, with the gap between personal and organisational AI maturity widening the longer adoption stays contained.

7:30

7 min 30 sec per challenge

Diagnose

Why does this matter for your destination?

A working group with no pathway into the wider organisation creates a two-speed culture where a small group advances while everyone else stays still.
The gap between personal and organisational AI maturity is already visible in your data and will widen if adoption stays contained.
Destinations that embed AI across teams move faster and more consistently.

Diagnose

What have you tried so far?

Have you defined what the working group is responsible for delivering, or is it still exploratory?
Has any of the group's work been tested with people outside it?
Where has internal communication about AI worked and where has it been ignored?

Constraint

What is the biggest constraint holding you back?

Is the blocker leadership confidence, time, the absence of a shared tool stack, or making AI feel relevant to people whose jobs look different from the working group's?
Is there a tendency to wait for a finished strategy before sharing anything?

Next move

What is the one move that unlocks everything else?

Identify one person outside the working group whose job would benefit from one AI workflow and start there.
Share one piece of working group output with a wider team, even informally.

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02

Challenge Two

Building an AI programme with structure and longevity

A programme without a clear sequence draws strong engagement in the first session and diminishing returns after, so it needs a stable model the DMO can share and that outlasts the person who built it.

7:30

7 min 30 sec per challenge

Diagnose

Why does this matter for your destination?

A programme without a clear sequence tends to produce strong engagement in the first session and diminishing returns after that.
Industry and partner enablement depends on the DMO having a stable and credible model to share.
A well-structured programme creates replicable value, the difference between a project and something that outlasts the person who built it.

Diagnose

What have you tried so far?

How far have you got with defining the structure of your programme and where does it feel least resolved?
Have you shared a draft or early version with any partners or industry contacts and what came back?
Where does the programme feel strongest and where does the ambition outrun delivery capacity?

Constraint

What is the biggest constraint holding you back?

Is it scope, resource, getting the right organisations to commit as participants, or designing something that works for destinations at every level of AI maturity?
Is there a risk the programme tries to do too much before any part of it has been tested?

Next move

What is the one move that unlocks everything else?

Define what success looks like at the end of the first phase only.
Identify one destination or partner to run the first session with as a live test before building further.

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03

Challenge Three

Enabling partners and industry to access destination knowledge

Partners who cannot reach destination knowledge on their own keep returning to the DMO for answers that could be self-served, while AI built on well-structured knowledge lets industry find answers and self-assess.

7:30

7 min 30 sec per challenge

Diagnose

Why does this matter for your destination?

Partners who cannot access destination knowledge on their own keep returning to the DMO for things that could be self-served, which is costly at any scale.
AI tools built on well-structured destination knowledge let industry find answers, check information and self-assess in ways that were not practical before.
Destinations that help their ecosystem work more effectively position themselves as infrastructure.

Diagnose

What have you tried so far?

Is your destination knowledge organised in a way an AI tool could draw on reliably?
Have you piloted any partner-facing tools or knowledge resources, even at a basic level?
What do your partners ask for most often and does that knowledge exist somewhere structured and findable?

Constraint

What is the biggest constraint holding you back?

Is the blocker the state of the knowledge itself, the technical build, the resource to maintain it, or uncertainty about whether partners would use it?
Is there a governance question about what knowledge should be open to partners and what should not?

Next move

What is the one move that unlocks everything else?

List the ten questions your partners ask most often and check whether the answers exist in a structured, accessible form anywhere in your organisation.
Test whether those answers could be fed into an existing tool without a custom build.

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04

Challenge Four

Building national data standards for AI readiness

Most DMOs hold significant destination data but the way it is structured, stored and made accessible varies enormously across organisations and scales. Establishing shared standards that work for a national body and a small local operator at the same time is one of the most consequential infrastructure decisions the sector is facing.

7:30

7 min 30 sec per challenge

Diagnose

Why does this matter for your destination?

AI systems surface destinations whose data is structured, clean and machine-readable. Inconsistent or poorly formatted data across the ecosystem creates visibility gaps that no amount of content production will fix.
National data standards create a shared foundation that smaller operators can build on rather than each solving the same technical problem independently.
The investment required to get this right is significant and the case for it needs to be made to leadership in terms they find compelling.

Diagnose

What have you tried so far?

Is there any existing data standard or taxonomy your organisation uses and how consistently is it applied across partners?
Have you attempted to audit how your destination data is currently structured and where the gaps in machine-readability are?
Has the conversation with senior leadership about data infrastructure investment happened and if so how was it received?

Constraint

What is the biggest constraint holding you back?

Is the challenge technical, financial, political or a combination of all three?
Is there a risk that different bodies attempt to set their own standards independently, creating fragmentation rather than consistency?

Next move

What is the one move that unlocks everything else?

Identify one existing data standard in adjacent sectors or from a leading DMO and assess whether it could serve as a starting point rather than building from scratch.
Document one concrete example of where poor data structure has directly affected your destination's AI visibility and use it as the opening case for the leadership conversation.

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favorite The Advisory Clinic

Thank you.

lightbulb

Key pattern

The one theme that surfaced across all four challenges.

arrow_forward

First move

The smallest action you can take back to your organisation this week.

block

Common trap

The mistake most organisations make with this topic.

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