Research, measurement and community

An organisation can begin benchmarking by grading all AI-assisted work consistently using the Productivity Score over a defined period, typically a quarter. From that dataset, three measures are immediately available: average Productivity Score across all graded work, proportion of deliverables reaching P3 or above, and distribution of Delivery Extension Scores by work type. Together these form an organisational productivity baseline against which future periods can be compared and targets set.

Once a baseline exists, organisations can set forward-looking targets. A well-formed target specifies a time period, a scope (all AI-assisted work, or a defined category such as content production or research), and a measurable threshold, for example, achieving an average Productivity Score of 3.2 or above across all advisory deliverables by the end of the financial year. The framework provides the instrument; organisations set the ambition level appropriate to their context.

If multiple destination organisations report aggregate scores into a shared pool on an anonymised and opt-in basis, DTTT can publish sector-average productivity figures and give individual organisations a sense of where they sit relative to peers. This turns the framework from a private disclosure tool into a shared industry productivity benchmark, relevant not only for internal performance management but for demonstrating the value of AI investment to funders, boards and government bodies.

Tourism faces a long-standing productivity challenge relative to many other sectors. Output per worker has historically been constrained by the labour-intensive nature of hospitality and visitor services, seasonal demand patterns and fragmented supply chains. The digital transformation of destination management has begun to address some of these constraints, but the evidence base for measuring that impact at scale remains thin.

AI adoption is one of the most significant levers now available for improving productivity across knowledge-based work in tourism, from research and strategy to marketing, communications and programme delivery. Demonstrating that impact requires consistent measurement across a range of indicators: time saved per task, volume of output per FTE, cost per unit of delivery, and the relationship between AI capability extension and the breadth of work that teams can realistically take on. These are all dimensions the DTTT Productivity Model captures directly.

Aggregate data from organisations using the framework consistently over time creates the foundation for sector-level productivity analysis. This is relevant not only to individual organisations managing performance and investment decisions, but to national tourism bodies, enterprise agencies and government departments making the case for digitisation investment, demonstrating returns to funders and informing policy on AI adoption in the visitor economy. The academic partnership programme described below is designed to support the independent research needed to make that case with rigour.

Organisations that formally adopt the framework and apply it to their AI work can register as official adopters. Registration gives public acknowledgement of your commitment, listing on the DTTT framework adopters directory, and a direct channel into the governance process for feedback on the models.

Each model has a working group open to practitioners applying the framework in their organisations. Working groups meet periodically to discuss emerging edge cases, proposed changes and practical implementation questions. Meetings are conducted by written circulation or videoconference.

Organisations applying the framework to real work are invited to submit case studies for publication with attribution. Case studies help other adopters understand how the models apply across different contexts and contribute to the body of practice that will inform future versions. If you have applied the framework to a project or output and would like to share your experience, get in touch and we will discuss how best to do that.