Some AI solution providers tout their ability to develop a minimum viable product (MVP) at a very low cost. Typically, an MVP includes using your test data set to implement a simplistic base model. Unfortunately, a minimally viable solution doesn’t include the cost and effort to implement a resilient data infrastructure; ramp the model to reflect real world data volumes, the cost of data (internal and external); ongoing testing, validation, and retraining; careful curation of data/model/result sets; compliance requirements for explainability and ethical behavior; and don’t forget user adoption.