AI Capability Model

01Written Content Generation

Using AI to produce, refine and adapt written content across formats and audiences, from destination guides to strategy documents, while maintaining editorial control and quality.

02Visual and Creative Production

Directing AI tools to generate, edit and enhance visual content for campaigns and communications, applying creative judgement to ensure outputs are on-brand and fit for purpose.

03Data Storytelling

Using AI to transform data and analysis into clear, compelling narratives that non-specialist audiences can understand and act on, not just summarising numbers but building the story around them.

04Audience and Personalisation Thinking

Using AI to produce meaningfully differentiated content for distinct visitor or stakeholder segments, addressing different motivations and value propositions rather than simply adjusting tone.

01Research Synthesis

Using AI to process and synthesise large volumes of information quickly, while maintaining the critical judgement to assess what the evidence actually says and what remains uncertain.

02Data Analysis and Interpretation

Using AI to surface patterns, anomalies and opportunities within datasets, then applying strategic knowledge of the destination or sector to determine which findings are genuinely significant.

03Critical Evaluation of AI Output

Systematically questioning and verifying AI-generated analysis before using it: treating AI conclusions as hypotheses to test rather than findings to report, and independently checking what matters most.

04Strategic Pattern Recognition

Using AI to distinguish meaningful signals from noise in complex or large datasets, and translating those patterns into strategic implications rather than surface observations.

01Prompt Engineering and Instruction Design

Designing detailed, reliable prompts and system instructions that produce consistently high-quality outputs, and building reusable assets that the whole team can apply, not just the individual who created them.

02Workflow and Process Design

Redesigning team workflows so that AI takes over appropriate steps end-to-end rather than just assisting with individual tasks, making efficiency gains reliable, repeatable and accessible to others.

03Tool Selection and Configuration

Evaluating and selecting AI tools against specific organisational needs (including capability, data security, cost and integration with existing systems) and making structured recommendations with clear rationale.

04Automation and Integration Thinking

Connecting AI-powered steps into coherent processes that reduce manual intervention, handling failures and edge cases thoughtfully, and documenting systems so others can maintain and improve them over time.

01Responsible and Ethical Practice

Identifying and managing the ethical, legal and data governance dimensions of AI use before problems arise: including data protection obligations, consent, potential bias and appropriate disclosure.

02Team Enablement and Change Navigation

Creating the conditions for colleagues to adopt AI confidently and sustainably: addressing anxiety and inconsistency directly, building psychological safety and modelling the kind of thoughtful AI engagement you want to see.

03Continuous Learning and Adaptation

Treating AI capability development as an ongoing professional discipline rather than a one-time event: evaluating new capabilities systematically, updating practices where there is genuine value, and helping colleagues understand what has changed.

04AI Governance and Oversight

Building the accountability frameworks, policies, quality standards and audit processes that make an organisation's AI use responsible, consistent and auditable over time, not just setting rules but creating the infrastructure that makes them work.