Taking development costs into account
After making sure that data is available, and that the data is (or can be) annotated with the required intents, entities, and classification categories, the next consideration for deciding whether NLP is a good fit for an application is the cost of developing the application itself. Some technically feasible applications can nevertheless be impractical because they would be too costly, risky, or time-consuming to develop.
Development costs include determining the most effective machine learning approaches to a specific problem. This can take significant time and involve some trial and error as models need to be trained and retrained in the process of exploring different algorithms. Identifying the most promising algorithms is also likely to require NLP data scientists, who may be in short supply. Developers have to ask the question of whether the cost of development is consistent with the benefits that will be realized by the final application...
