Once a model has been iterated and successfully implemented, stakeholders can consider its future intent including options to repurpose, expand, or scale the model. Once the model is functional, stakeholders can use existing platforms to share and encourage similar projects and build on what has been created. Peer review and a validation of the model beyond one organisation is useful to contribute back to the sector and build sustainable practice around AI/ML. Templatization, or the adaptation of a solution for another organisation’s use , can also accelerate impact and avoid unnecessary and costly duplication. However, while models offer new insights, clearly communicating the limits of those insights can be difficult to those who are excited by the new capabilities. There can be a tendency to iterate by extending the model to areas outside of the bounds of what the model was intended to do. Extending and iterating needs must be done carefully since, for example, models built on data from a global dataset might not be explanatory in specific countries. Acknowledging these limitations during the scaling stage of a model, can avoid "pilotitis" and help attract additional investment.

Please find below a legend of what can be found within the framework:

📚Resources - e.g. reports, articles, and case studies

🛠Tools - e.g. guidelines, frameworks and scorecards

🔗Links - e.g. online platforms, videos, hubs and databases

❌Gap analysis - tools or resources are currently missing

👥 List of stakeholders which should be included in the specific decision point