Appendix B: Interview questions

Image 12: Animation of Argumented reality and Smart City technology. Source: Sasin Paraksa, iStock

tions must be clearly defined before developing and training algorithms begins. Since an individual AI system can only solve a very narrowly defined problem, it will be important to determine which AI-assisted processes and products should be prioritised. Cost-benefit may vary according to local infrastructures and capacities. • Focus on economic sustainability: AI is still a very novel approach that is used to help find solutions for specific challenges, particularly in highly complex urban contexts. As such, there is very little in the way of past experiences or business models that provide knowledge or insights. However, ensuring a project’s economic sustainability is key to its long-term success. Initiated projects (such as start-up initiatives) face this challenge throughout their organically evolving development and steadily review and improve their business models to ensure they succeed on the market. Funded projects, however, need to ensure their economic sustainability after the funding has ended. As such, an in-depth assessment and solid business model have to be created for the solution in the early stages – even during the tendering process – to ensure it is economically sustainable in the long-run and after the project has ended. In this report we outlined six highly successful and promising solutions. However, our desk research during early stages of the report revealed a high number of less successful projects, which emphasizes just how important these challenges are.