2. Prototypes are not very useful
After defining a product concept, design thinking suggests creating a quick and cheap prototype for testing ideas with users. The prototype visually shows the user an idea, an initial concept. The way the client interacts with the prototype allows designers to test hypotheses and make the necessary changes. In addition, the team often understands the technical limitations of bringing the idea to life during the prototyping stage.
In the case of artificial intelligence, prototypes are not so useful due to several reasons. When we work with machine learning models, it makes no sense to use cheap prototypes like “The Wizard of Oz” (when a user’s interaction with the system is tested while another person performs the system’s functions). Although it allows us to easily check the general concept, this prototype tells us too little about the technical capabilities of the real solution.
Technical prototypes, on the other hand, are often very disappointing at first. Indeed, the first outcomes of artificial intelligence technology almost always fail to produce the desired results. For example, Google Translate has been widely known for its mistakes in the past, but everyone recognizes how good it is today thanks to a long learning period.
To get good results in a short time you can focus on one case or scenario. So, for all digital assistants, we could have created an optimal prototype – a bot that would deliver ideal results for a predetermined scenario. However, such a prototype would say nothing about the technical feasibility of the project since one case could radically differ from another.